Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

Zhiyi Chen; Xuerong Liu; Qingwu Yang; Yan-Jiang Wang; Kuan Miao; Zheng Gong; Yang Yu; Artemiy Leonov; Chunlei Liu; Zhengzhi Feng; Hu Chuan-Peng

doi:10.1001/jamanetworkopen.2023.1671

. 2023 Mar 6;6(3):e231671. doi: 10.1001/jamanetworkopen.2023.1671

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

A Systematic Review

Zhiyi Chen ^1,^2,^✉, Xuerong Liu ^1,², Qingwu Yang ³, Yan-Jiang Wang ³, Kuan Miao ^1,², Zheng Gong ^1,², Yang Yu ¹, Artemiy Leonov ⁴, Chunlei Liu ⁵, Zhengzhi Feng ^1,^2,^✉, Hu Chuan-Peng ⁶

¹School of Psychology, Third Military Medical University, Chongqing, China

²Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China

³Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China

⁴Department of Psychology, Clark University, Worcester, Massachusetts

⁵School of Psychology, Qufu Normal University, Qufu, China

⁶School of Psychology, Nanjing Normal University, Nanjing, China

Accepted for Publication: January 15, 2023.

Published: March 6, 2023. doi:10.1001/jamanetworkopen.2023.1671

^✉

Corresponding Author: Zhiyi Chen, PhD, (chenzhiyi@tmmu.edu.cn); and Zhengzhi Feng, PhD, MD (fzz@tmmu.edu.cn), Army Medical University, No. 30 Gao Tan-Yan Main St, Shapingba, Chongqing 400038, China.

Author Contributions: Dr Chen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Chen and Ms X. Liu contributed equally to this work.

Concept and design: Chen, X. Liu, Wang, Zhengzhi.

Acquisition, analysis, or interpretation of data: Chen, X. Liu, Yang, Miao, Gong, Yu, Leonov, C. Liu, Chuan-Peng.

Drafting of the manuscript: Chen, X. Liu, Yang, Miao, Yu.

Critical revision of the manuscript for important intellectual content: Chen, Wang, Gong, Leonov, C. Liu, Zhengzhi, Chuan-Peng.

Statistical analysis: Chen, X. Liu, Miao, Gong, Yu.

Obtained funding: Zhengzhi.

Administrative, technical, or material support: Chen, X. Liu, Yang, Yu, C. Liu, Zhengzhi.

Supervision: Chen, Wang, Zhengzhi.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was funded by grant CWS20J007 from the Chinese People's Liberation Army (PLA) Key Research Foundation, grant 20220102 from the Army Medical University Academic Rising Star Foundation, and grant 2022160258 from PLA Talent Program Foundation.

Role of the Funder/Sponsor: The funders had a role in reviewing and approving this manuscript for publication. The funders had no role in the design and conduct of the study or the collection, management, analysis, and interpretation of the data.

Data Sharing Statement: See Supplement 2.

^✉

Corresponding author.

PMCID: PMC9989906 PMID: 36877519

This systematic review assesses the clinical applicability and reporting quality of available artificial intelligence models in psychiatry.

Key Points

Question

Are there potential risks to translating neuroimaging-based artificial intelligence (AI) models into direct clinical applications, such as psychiatric diagnosis?

Findings

In this systematic review of 517 studies presenting 555 neuroimaging-based AI models for psychiatric diagnostics, most models had a high overall risk of bias and had poor clinical applicability. All articles provided incomplete reports for validation.

Meaning

Findings of this study suggest that, in their current form, neuroimaging-based AI models for psychiatric diagnosis need to address risk of bias before use in clinical practice.

Abstract

Importance

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

Objective

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

Evidence Review

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

Findings

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

Conclusions and Relevance

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Introduction

Given the lack of biomarkers to guide psychiatric diagnoses, machine learning has been increasingly applied to develop brain-based hallmarks. Thus, neuroimaging-based artificial intelligence (AI) models have proliferated rapidly during the past 3 decades. Rather than enabling a clinician-based diagnosis of discrete symptoms, neuroimaging-based AI models enable an objective, symptom-centered, individualized and neurobiologically explicable estimate of psychiatric conditions.^1,2 In this vein, these models show considerable potential to be translated for use in early diagnosis, treatment development, and clinical decision-making.^3,4,5,6,7 However, lack of an evidence-based systematic appraisal of AI models impedes stakeholder enthusiasm for applying them in clinical practice.^8,9

Systematic appraisals of clinical AI models may provide solid evidence to validate their reliability in medical practice. For instance, despite global efforts to develop AI diagnostic models for COVID-19, these models built either through traditional multivariable analysis or imaging-based AI technique were found to have poor clinical implications due to a high risk of bias (ROB).^10,11 Furthermore, systematic appraisals revealed the high ROB in clinical models across all medical specialties, especially in psychiatric diagnoses.^12,13 During the past 3 decades, advances in neuroimaging-based techniques opened radically new venues for psychiatric diagnosis by providing potentially more reliable and sensitive brain biomarkers in AI prediction models.^14,15 Despite their strengths, the absence of systematic appraisals for biomarker-based AI models leaves a gap between the experimental proof-of-concept models and clinical applicability.

In addition, poor reporting quality impedes the establishment of better practices in AI-based psychiatric diagnosis. Despite the developments of multifarious benchmarks for reporting clinical prediction models (eg, STARD [Standards for Reporting of Diagnostic Accuracy], DECIDE-AI [Early-Stage Clinical Evaluation of Decision Support Systems Driven by Artificial Intelligence], and TRIPOD [Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis]),^16,17,18,19 the reporting quality in current publications still needs to be improved, with approximately 80% of articles providing incomplete information and less than 50% of contents following the TRIPOD guideline, limiting clinical potential and contributing to research waste.^20,21,22 Even after 3 decades of neuroimaging-based AI model developments, it remains unclear whether the reports of such developments are unbiased and complete enough to warrant translation into clinical practice for psychiatric diagnosis.

In this study, we sought to systematically assess the ROB and reporting quality of neuroimaging-based AI models for psychiatric diagnosis that were developed in the past 3 decades. To this end, we used the Prediction Model Risk of Bias Assessment Tool (PROBAST) and modified Checklist for Evaluation of Image-Based Artificial Intelligence Reports (CLEAR) benchmarks.^23,24

Methods

The study protocol was registered at PROSPERO (CRD42022340624) and followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline (Figure 1^{25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541}).^542,543 The Chinese Army Medical University Institutional Review Board officially censored the study and deemed it exempt from approval and the requirement for informed consent given that no original human or animal participants were involved.

Figure 1. — DSM indicates *Diagnostic and Statistical Manual of Mental Disorders*.

^aOf the 555 artificial intelligence (AI) models, 469 were tested at the internal sample and 86 at the independent external sample, including intellectual disability,^25,26,27,28 autism spectrum disorder,^{29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147} attention-deficit/hyperactivity disorder,^{44,51,72,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381} bipolar disorder,^{290,314,321,327,328,339,343,346,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406} major depressive disorder,^{309,346,359,363,381,405,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473} social anxiety disorder,^{474,475,476,477} unspecified anxiety disorder,^346,478,479 general anxiety disorder,⁴⁸⁰ obsessive-compulsive disorders,^{346,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496} posttraumatic stress disorder,^{346,409,497,498,499,500,501,502,503} somatic symptom disorder,⁵⁰⁴ anorexia nervosa,^505,506,507 binge-eating disorder,⁵⁰⁸ insomnia disorder,^509,510 conduct disorder,^511,512,513 antisocial personality disorder,^514,515,516 other or unknown substance use disorder,^{517,518,519,520} alcohol use disorder,^{346,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536} opioid use disorder,⁵³⁷ borderline personality disorder,^538,539,540 disruptive behavior disorders,⁵⁴¹ and social anxiety disorder.³⁴⁶

^bEighty-six AI models^{30,32,34,36,42,44,46,53,59,60,61,63,69,70,72,81,82,88,91,117,149,151,156,162,163,164,169,170,172,180,182,187,191,197,212,213,220,221,247,254,258,260,261,263,269,272,278,280,283,284,285,287,290,301,305,307,319,335,352,353,355,356,359,385,407,417,422,443,452,454,483,484,510,519,526,532,534} were tested at the independently external sample.

Retrieval Strategy

Following retrieval benchmarks,^543,544 we conducted a literature search in PubMed in June 2022 using Boolean codes and RSS (Really Simple Syndication) feeds (eMethods 1 in Supplement 1). We initially included studies that (1) were published between January 1, 1990, and March 16, 2022; (2) were peer-reviewed, full-length original articles published in journals or conference proceedings; and (3) developed or validated neuroimaging-based AI models for diagnosing psychiatric diseases that were defined in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition).

We screened studies from the initial literature pool by following these inclusion criteria: (1) at least 1 algorithm was used to build the AI model for psychiatric diagnosis; (2) training features were limited to neuroimaging-based biomarkers (eg, [functional] magnetic resonance imaging, electroencephalogram, and functional near-infrared spectroscopy); and (3) minimum fundamental information for the AI model was reported. Reference lists were further searched for suitable original studies. Exclusion criteria were articles that (1) were not peer-reviewed original research (eg, preprint, review, meta-analysis, or comments), (2) involved non–neuroimaging-based features (eg, behavioral factors or blood and genes) or nonhuman participants, (3) used nondiagnostic AI models (eg, prognostic model) for unclassified psychiatric disorders, and (4) used discriminatory AI algorithm that differentiated cohorts rather than diagnosed psychiatric conditions.

Data Extraction and Analysis

Data extraction adhered to the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA guidelines.^19,544 We used a closed-loop cross-sequential design for quality control on data extraction and screening (5 assessors). Details on data extraction are provided in eMethods 2 and 3 in Supplement 1.

We built on a global geospatial model to illustrate the distribution of the sample population for these AI models. Sample population was defined according to information in the original article if applicable (eMethods 3 in Supplement 1). Global geospatial map was constructed by first fine-grained classification, which included 251 countries or regions. Geospatial source data were adjusted by the United Nations Statistics Division M49 codes and EasyShu team.

The PROBAST²⁴ was used to assess the ROB of the neuroimaging-based AI models for psychiatric diagnosis. The PROBAST encompassed 20 signaling questions covering 4 ROB domains: participants, predictors, outcome, and analysis. Each question may be answered by yes, probably yes, no, probably no, or no information. Answering no or probably no to a signaling question indicated a potential high ROB. Overall ROB was rated high if a no or probably no answer was given for any signaling question. Overall low ROB was defined as answering all of the signaling questions with a yes or probably yes. The PROBAST criterion was modified to assess its applicability to each included study by adding 1 signaling question for the first 3 ROB domains (participants, predictors, and outcome) (eMethods 4 in Supplement 1)

We adopted the modified 25-item CLEAR statement²³ to evaluate the reporting quality (ie, feasibility) of the included studies, which included 4 domains (data, technique, technical assessment, and applications). To ensure applicability to neuroimage-based AI models, we removed 3 items (eMethods 5 in Supplement 1). The eligible 22 items were transformed for signaling questions to form the modified CLEAR statement (eTable 1 in Supplement 1). The CLEAR criteria for assessing the overall reporting quality of these studies were fully in line with the PROBAST.

To ensure adequate assessment validity, 5 independent assessors with multidisciplinary expertise were involved, including a clinical psychiatrist (Y.Y.), research fellows for psychiatry (X.L.) and AI (K.M.), a neuroscientist (Z.G.), and a psychologist (Z.C.). We built a closed-loop cross-sequential framework for validating assessment quality. Furthermore, another independent assessor (C.L.) was invited to validate assessment quality (eMethods 6 in Supplement 1).

Statistical Analysis

Geospatial models were built using R packages, version 4.0.5 (R Core Team). Interactive interfaces for sample population map were generated from EasyShu, version 2.4 (EasyShu) and coded with C++. Bootstrapping simulation (n = 2000) was performed to estimate 95% CIs for proportions using MATLAB (MathWorks Inc). Cohen κ test was also conducted to quantify the reliability of assessment quality across these 5 subsets. A prior 2-sided significance level for these statistics was set as P = .05.

Results

A total of 517 studies presenting 555 development-purpose AI models were eligible for inclusion in the analysis^{25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541} from the 50 916 records that were retrieved from the PubMed search in June 2022. Of the 555 models, 469 (84.5%) were for internal-sample validation^{25,26,27,28,29,31,33,35,37,38,39,40,41,43,45,47,48,49,50,51,52,54,55,56,57,58,62,64,65,66,67,68,71,73,74,75,76,77,78,79,80,83,84,85,86,87,89,90,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,118,119,120,121,122,123,124,125,126,127,129,130,131,132,133,134,136,139,140,141,142,143,144,145,147,148,150,152,153,154,155,157,158,159,160,161,165,166,167,168,171,173,174,175,176,177,178,179,181,183,184,185,186,188,189,190,191,192,193,194,195,196,198,199,200,201,202,203,204,205,206,207,208,209,210,211,214,215,216,217,218,219,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,248,249,250,251,252,253,255,256,257,259,262,264,265,266,267,268,270,271,273,274,275,276,277,279,281,282,286,288,289,291,292,293,294,295,296,297,298,299,300,302,303,304,306,308,309,310,311,312,313,314,315,316,317,318,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,354,357,358,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,408,409,410,411,412,413,414,415,416,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,444,445,446,447,448,449,450,451,453,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,471,472,473,474,475,476,477,478,479,480,481,482,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,504,505,506,507,508,509,511,512,513,514,515,516,517,518,520,521,522,523,524,525,527,528,529,530,531,533,535,536,537,538,539,540,541} and 86 (15.5%) were for external-sample validation^{30,32,34,36,42,44,46,53,59,60,61,63,69,70,72,81,82,88,91,117,128,135,137,138,146,149,151,156,162,163,164,169,170,172,180,182,187,197,212,213,220,221,247,254,258,260,261,263,269,272,278,280,283,284,285,287,290,301,305,307,319,335,352,353,355,356,359,385,407,417,422,443,452,454,470,483,484,503,510,519,526,532,534} (Figure 1). Based on the United Nations Statistics Division classification, the samples for training or testing these models covered 15.5% (39 of 251) of countries or areas globally (Figure 2), with 97.5% (38 of 39) of samples located in high-income or upper-middle-income areas.

Figure 2. — Total sample sizes for countries were geologically mapped into corresponding location in the global map. To improve readability, original total sample size underwent log-transformation.

Nearly half of the models (48.3% [268 of 555]) were found in studies authored by those with academic training in computers and data science^{27,32,33,34,35,36,37,39,40,41,42,44,45,46,47,48,50,52,58,70,71,72,73,75,76,79,82,85,87,89,90,92,93,95,96,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,121,123,128,129,130,132,133,134,135,136,139,141,142,144,146,151,152,156,161,162,163,164,166,167,168,170,173,175,176,177,179,180,182,184,185,187,190,193,195,200,202,204,205,206,208,209,210,212,214,220,224,225,226,227,228,230,232,233,234,235,239,240,245,247,250,251,256,257,259,260,263,266,271,272,275,276,277,283,288,289,293,297,301,302,303,306,308,311,313,314,316,318,319,320,323,324,325,326,328,329,330,331,332,333,334,335,336,339,340,341,344,345,348,350,353,354,357,358,359,362,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,392,394,399,401,402,403,410,411,413,414,415,416,425,430,431,432,433,436,440,445,446,447,448,449,451,452,455,456,457,462,463,465,466,469,470,472,476,478,479,480,484,488,489,490,491,496,510,511,512,513,516,519,520,523,525,526,530,531,533,534,535,537,539,541} (eTable 2 in Supplement 1). Schizophrenia (25.4% [141 of 555 models])^{247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381} and autism spectrum disorder (21.4% [119 of 555 models])^{29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147} were the most common psychiatric diseases to be diagnosed with these models (eTable 3 in Supplement 1). Full technical details are provided in eTables 3 to 9 in Supplement 1. Assessor reliability quality was found to be satisfied, with an acceptable Cohen κ coefficient mean of 0.68 (median [range], 0.96 [0.02-1.00]) across 5 subset pools (eMethods 6 in Supplement 1).

Overall ROB for AI Models

In total, 461 of 555 AI models (83.1%; 95% CI, 80.0%-86.2%) were rated as having high overall ROB.^{25,32,33,34,35,36,37,38,39,40,41,43,44,45,46,47,48,49,50,52,53,54,55,56,57,58,59,60,62,63,64,65,66,67,68,69,71,72,73,74,75,76,77,78,79,80,81,83,85,87,88,89,90,91,92,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,113,114,115,116,117,118,119,120,121,122,123,124,126,127,128,129,130,133,135,136,141,142,145,146,148,149,151,153,155,156,158,161,163,166,167,168,169,170,172,173,174,175,176,177,179,180,181,182,183,186,187,189,190,191,192,193,195,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,214,215,216,217,218,219,220,221,222,223,224,225,226,227,229,230,232,233,234,235,236,237,238,239,240,241,242,245,246,247,249,251,252,253,256,257,258,259,260,262,264,265,268,270,273,274,275,276,277,278,279,280,281,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,324,325,326,327,328,329,330,331,332,333,334,335,336,338,339,340,341,342,343,344,345,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,428,429,430,431,432,433,434,435,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,464,465,466,467,468,469,470,471,472,473,475,477,478,479,480,482,483,484,485,487,491,493,495,498,500,502,503,504,505,507,508,509,510,511,512,513,515,516,517,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,536,537,538,539,540,541} Two of 555 models (0.4%; 95% CI, 0.00%-0.01%) were rated with high overall ROB in the participants domain.^26,31 Overall, the ROB in the participants domain remained low for existing neuroimaging-based AI diagnostic models.

In the predictors domain, 187 of 555 models (33.7%; 95% CI, 29.9%- 37.6%) were rated with high ROB^{37,38,47,55,56,57,59,60,62,68,69,78,79,81,85,89,92,99,100,104,107,113,115,117,121,129,130,133,135,136,148,150,154,158,160,161,167,169,170,173,175,178,183,190,191,193,195,197,201,204,206,207,210,212,215,216,224,225,229,232,233,234,235,238,240,242,247,251,253,257,260,274,277,279,283,285,286,290,295,299,303,304,305,306,307,317,318,319,320,322,326,332,333,334,335,336,339,340,345,347,349,350,351,352,353,357,358,360,363,364,365,376,385,388,389,390,391,392,393,398,400,402,403,404,405,406,409,410,411,413,415,416,417,418,421,423,424,425,426,428,429,430,433,434,441,442,444,449,451,452,459,461,464,466,468,472,475,478,484,488,491,493,502,504,505,516,529,532,533,537,538,540,541} (Table 1). Defining predictors by knowing the outcome of these models was the unique source of the high ROB in this domain (ie, signaling question 2.2: were predictor assessments made without knowledge of outcome data?). In the outcome domain, high ROB was scored for 198 of 469 models (35.7%; 95% CI, 31.8%-39.7%)^{26,32,33,35,36,37,39,41,44,45,48,53,54,55,56,57,59,60,62,68,69,78,79,81,85,89,92,99,100,104,107,113,115,117,121,129,130,133,135,136,148,150,154,158,160,161,167,169,170,173,175,178,183,190,191,193,195,197,201,204,206,207,210,212,215,216,224,225,229,232,233,234,235,238,240,242,247,251,253,257,260,274,277,279,283,285,286,290,295,299,303,304,305,306,307,317,318,319,320,322,326,332,333,334,335,336,339,340,345,347,349,350,351,352,353,357,358,359,360,363,364,365,376,385,388,389,390,391,392,393,398,400,402,403,404,405,406,409,410,411,413,415,416,417,418,421,423,424,425,426,428,429,430,433,434,441,442,444,449,451,452,459,461,464,466,468,472,475,478,484,488,491,493,502,504,505,516,529,532,533,537,538,540,541} (Table 1). These models had a high ROB because the outcome knowledge of testing data sets was leaked into the predictors of the training set (ie, signaling question 3.3: were predictors excluded from the outcome definition?).

Table 1. ROB Assessment for All Included AI Models Using the PROBAST (N = 555).

Signaling question^a	Answers, No. (%)
Signaling question^a	Yes or probably yes	No information	No or probably no
Domain 1: participants
Were appropriate data sources used (eg, cohort, RCT, or nested case-control study data)?	555 (100.0)	0	0
Were all inclusions and exclusions of participants appropriate?	553 (99.6)	0	2 (0.4)
Domain 2: predictors
Were predictors defined and assessed in a similar way for all participants?	555 (100.0)	0	0
Were predictor assessments made without knowledge of outcome data?	368 (66.3)	0	187 (33.7)
Were all predictors available at the time the model was intended to be used?	555 (100.0)	0	0
Domain 3: outcome
Was the outcome determined appropriately?	555 (100.0)	0	0
Was a prespecified or standard outcome definition used?	555 (100.0)	0	0
Were predictors excluded from the outcome definition?	357 (64.3)	0	198 (35.7)
Was the outcome defined and determined in a similar way for all participants?	555 (100.0)	0	0
Was the outcome determined without knowledge of predictor information?	358 (64.5)	0	197 (35.5)
Was the time interval between predictor assessment and outcome determination appropriate?	555 (100.0)	0	0
Domain 4: analysis
Was there a reasonable number of participants with the outcome?	157 (28.2)	0	398 (71.7)
Were continuous and categorical predictors handled appropriately?	555 (100.0)	0	0
Were all enrolled participants included in the analysis?	393 (70.9)	0	162 (29.1)
Were participants with missing data handled appropriately?	44 (7.9)	37 (6.6)	474 (85.5)
Was selection of predictors based on univariable analysis avoided?	355 (63.9)	0	200 (36.1)
Were complexities in the data (eg, censoring, competing risks, and sampling of control participants) accounted for appropriately?	0	5 (0.9)	550 (99.1)
Were relevant model performance measures evaluated appropriately?	0	0	555 (100.0)
Were model overfitting and optimism in model performance accounted for?	497 (89.5)	16 (2.8)	42 (7.7)
Did predictors and their assigned weights in the final model correspond to the results from the reported multivariable analysis?	53 (9.6)	0	502 (90.4)

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Abbreviations: AI, artificial intelligence; PROBAST, Prediction Model Risk of Bias Assessment Tool; RCT, randomized clinical trial; ROB, risk of bias.

^{^a}

The criteria for answering the signaling questions largely adhered to the PROBAST but were slightly modified to fit the current study (eMethods 4 in Supplement 1).

In the analysis domain, most models were rated as having high overall ROB (461 of 555 [83.1%; 95% CI, 80.0%-86.2%]).^{25,32,33,34,35,36,37,38,39,40,41,43,44,45,46,47,48,49,50,52,53,54,55,56,57,58,59,60,62,63,64,65,66,67,68,69,71,72,73,74,75,76,77,78,79,80,81,83,85,87,88,89,90,91,92,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,113,114,115,116,117,118,119,120,121,122,123,124,126,127,128,129,130,133,135,136,141,142,145,146,148,149,151,153,155,156,158,161,163,166,167,168,169,170,172,173,174,175,176,177,179,180,181,182,183,186,187,189,190,191,192,193,195,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,214,215,216,217,218,219,220,221,222,223,224,225,226,227,229,230,232,233,234,235,236,237,238,239,240,241,242,245,246,247,249,251,252,253,256,257,258,259,260,262,264,265,268,270,273,274,275,276,277,278,279,280,281,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,324,325,326,327,328,329,330,331,332,333,334,335,336,338,339,340,341,342,343,344,345,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,423,424,425,426,428,429,430,431,432,433,434,435,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,464,465,466,467,468,469,470,471,472,473,475,477,478,479,480,482,483,484,485,487,491,493,495,498,500,502,503,504,505,507,508,509,510,511,512,513,515,516,517,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,536,537,538,539,540,541} Inadequate sample size (398 of 555 [71.7%; 95% CI, 68.0%-75.6%]),^{25,26,27,28,29,30,31,33,35,37,38,39,40,41,43,45,46,48,49,54,55,56,57,58,59,60,61,64,65,66,67,70,71,73,77,79,80,82,83,84,85,87,88,89,90,91,92,94,95,96,97,98,99,100,101,106,108,110,111,113,114,119,120,122,124,126,128,129,133,134,135,137,138,141,142,143,144,146,147,148,149,151,152,153,155,156,161,163,164,166,169,171,172,174,175,177,178,179,180,182,183,185,186,187,188,189,190,191,194,195,197,199,203,204,205,207,209,210,211,212,214,215,216,217,218,219,220,222,223,224,225,226,227,229,230,232,233,236,237,239,240,241,242,243,246,247,249,250,252,256,257,258,259,260,261,262,263,264,265,266,267,269,270,274,277,278,279,281,282,283,285,286,287,288,289,290,291,292,293,294,295,296,297,300,301,302,303,304,307,309,310,311,313,314,315,316,320,321,322,323,324,325,328,329,330,331,332,333,334,335,336,338,340,341,342,343,344,345,347,348,349,350,351,352,353,354,355,356,358,359,361,364,366,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,392,393,396,397,398,399,400,401,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,419,420,421,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,447,448,449,450,451,452,454,456,459,460,461,462,464,465,466,467,468,469,472,473,477,480,482,483,484,487,488,489,490,492,493,494,495,496,498,501,502,503,504,507,508,512,513,515,516,517,519,520,521,522,523,524,525,526,527,528,529,530,531,532,536,537,538,539,540} poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexities (550 of 555 [99.1%; 95% CI, 98.3%-99.9%],^{27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541} with the majority of models undergoing no adjustment on data complexities [eg, signal question 4.6: were complexities in the data, such as censoring, competing risks, and sampling of control participants, accounted for appropriately?]) all contributed to the potential high ROB of these AI models in the analysis domain (Table 1). Although almost all of the models were rated with high ROB on calibration and data complexities issues, 52 models^{27,28,31,42,51,61,70,82,84,86,93,112,132,139,171,184,213,228,231,243,244,248,250,254,255,261,263,266,269,271,272,282,323,337,346,371,422,436,437,438,463,474,476,481,486,506,514,518,534,535} were still rated with low overall ROB in the analysis domain because these issues were judged as having limited implications for the models.

Clinical Applicability Concerns in AI Models

In total, by using the PROBAST, a third of the models were assessed for high concerns about their clinical applicability in the predictors and outcome domains. High concern for clinical applicability in the participants domain was found for 2 of 555 models (0.4%; 95% CI, 0.00%-0.01%).^26,31 Furthermore, high concerns regarding clinical applicability were found for 199 of 555 models (35.9%; 95% CI, 31.9%-39.9%) in the predictors domain^{25,33,35,37,39,41,44,45,48,53,54,55,56,57,59,60,62,78,79,80,81,85,89,92,99,100,104,107,113,115,117,121,122,129,130,133,135,136,148,150,154,158,160,161,167,169,170,172,173,175,178,183,190,191,193,195,197,201,204,206,207,210,212,215,216,224,225,229,232,233,235,238,240,242,247,251,253,257,260,274,277,279,283,285,286,290,295,299,303,305,306,307,312,317,318,319,320,322,326,332,333,334,335,336,339,340,345,347,349,350,351,352,353,357,358,359,360,363,364,365,376,385,388,389,390,391,392,393,398,400,402,403,404,405,406,409,410,411,413,415,416,417,418,421,423,424,425,426,428,429,430,433,434,441,442,444,449,450,451,452,458,459,461,464,466,468,472,475,478,484,488,491,493,502,504,505,516,529,532,533,537,538,540,541} and for 217 of 555 models (39.1%; 95% CI, 35.0%-43.2%) in the outcome domain.^{26,32,34,35,36,37,39,41,44,45,48,53,54,55,56,57,59,60,62,68,69,75,78,79,81,85,89,92,99,100,104,107,113,115,116,117,121,129,130,133,135,136,148,150,154,158,160,161,167,169,170,173,175,178,183,190,191,193,195,197,201,204,206,207,210,212,215,216,224,225,229,232,233,234,235,238,240,242,247,251,253,257,260,274,277,279,283,285,286,288,290,295,297,298,299,300,303,304,305,306,307,308,311,315,316,317,318,319,320,322,326,332,333,334,335,336,339,340,343,345,347,349,350,351,352,353,357,358,360,363,364,365,367,373,376,382,385,388,389,390,391,392,393,398,400,402,403,404,405,406,409,410,411,413,415,416,417,418,420,421,422,423,424,425,426,428,429,430,433,434,436,441,442,444,446,449,451,452,459,461,464,466,468,472,475,478,484,488,491,493,502,504,505,516,520,529,532,533,537,538,540,541} In addition, none of these AI models were perceived to be applicable to clinical practice to date.

Reporting Quality of AI Models

Overall, all models were assessed for poor reporting quality based on the modified CLEAR statement. Reporting completeness (ie, number of reported items/number of total items) for these AI models was found to be insufficient at 61.2% (95% CI, 60.6%-61.8%). Full results for reporting quality are presented in Table 2.

Table 2. Reporting Quality for All Included AI Models Using the Modified CLEAR (N = 555).

Signaling question^a	Answers, No. (%)
Signaling question^a	Yes or probably yes	No information	No or probably no
Domain 1: data
Were neuroimaging types reported?	555 (100.0)	0	0
Was how to handle neuroimaging artifacts reported?	177 (31.9)	0	378 (68.1)
Were technical acquisition details reported?	532 (95.9)	0	23 (4.1)
Were preprocessing procedures reported?	501 (90.0)	0	54 (10.0)
Was synthetic neuroimaging made public if used?	17 (3.1)	0	538 (96.9)
Were public neuroimages referenced adequately?	188 (33.9)	0	367 (66.1)
Were patient-level metadata reported enough (eg, geographic location, sex and gender distribution, and race and ethnicity), and how it was extracted?	362 (65.2)	0	193 (34.8)
Were potential biases from using patient information and metadata reported?	356 (64.2)	0	199 (35.8)
Were data set partition methods reported?	555 (100.0)	0	0
Were sample sizes for subsets (ie, training, validation, and test) reported?	555 (100.0)	0	0
Were external test sets reported?	87 (15.7)	0	468 (84.3)
Were class distributions and balance processes reported?	364 (65.6)	0	191 (34.4)
Were out-of-distribution neuroimages reported?	0	0	555 (100.0)
Domain 2: technique
Were labeling methods reported appropriately?	555 (100.0)	0	0
Were references to common/accepted diagnostic labels reported?	555 (100.0)	0	0
Were details for algorithm development reported?	297 (53.5)	0	258 (46.5)
Domain 3: technical assessment
Was how to publicly evaluate algorithm reported?	11 (2.0)	0	544 (98.0)
Were model performance measures reported?	555 (100.0)	0	0
Were benchmarking, technical comparison, and novelty discussed?	322 (58.0)	0	233 (42.0)
Were bias assessments discussed?	0	0	555 (100.0)
Domain 4: applications
Were use cases and target conditions discussed?	529 (95.3)	0	26 (4.7)
Were potential impacts on the health care team and patients introduced?	405 (73.0)	0	150 (27.0)

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Abbreviations: AI, artificial intelligence; CLEAR, Checklist for Evaluation of Image-Based Artificial Intelligence Reports; PROBAST, Prediction Model Risk of Bias Assessment Tool.

^{^a}

All items were translated from modified CLEAR statements into signaling questions that adhered to the PROBAST. Based on the PROBAST rating criteria, each domain was rated as poor reporting quality if 1 or more items were answered with no or probably no. Overall reporting quality was determined by rating for each item, with 1 or more items answered by no or probably no being poor reporting quality.

All of the models showed overall poor reporting quality in the data domain across 13 signaling questions. Lack of reporting for how to handle out-of-distribution images was the main source of the poor reporting quality rating in the data domain. In addition, 378 of 555 models (68.1%; 95% CI, 64.2%-72.0%) lacked reports for quality control (eg, signaling question: was how to handle neuroimaging artifacts reported?), incurring a low reporting quality rating.^{27,28,31,32,33,35,36,37,38,39,40,41,42,46,49,50,51,53,54,56,57,58,61,63,64,68,69,70,71,72,73,74,75,76,77,78,80,87,88,89,90,91,94,96,97,98,100,101,103,105,112,113,114,115,121,122,125,127,129,134,137,138,141,142,143,144,147,149,154,155,156,157,158,160,161,162,164,165,170,171,172,173,174,175,176,177,179,180,181,182,185,186,187,188,189,190,191,193,194,195,196,199,203,207,209,210,214,215,217,219,222,223,226,227,228,229,230,231,232,233,234,235,236,237,238,239,242,243,244,245,246,248,249,250,251,252,253,254,255,256,257,258,259,260,261,263,264,265,268,269,271,272,273,274,275,276,277,278,279,280,281,282,283,284,286,287,288,289,290,291,293,294,295,297,298,299,300,301,302,303,305,306,308,309,310,311,312,313,314,315,321,323,324,325,326,327,332,333,334,335,336,337,338,339,340,341,342,344,345,346,347,349,351,353,355,356,357,358,360,361,362,363,364,365,366,368,369,370,372,373,374,375,377,378,379,380,382,383,384,385,387,388,389,390,391,392,394,395,396,397,398,399,400,401,402,404,405,406,407,409,410,411,412,413,414,415,416,417,418,419,420,421,425,426,427,428,429,430,433,434,435,436,437,438,440,441,442,443,444,445,446,447,449,450,451,452,453,454,457,458,459,460,461,462,463,464,465,466,467,469,471,472,475,476,477,479,480,481,482,483,485,487,489,490,491,492,493,494,496,497,498,501,504,508,509,511,513,514,515,516,518,519,520,526,528,530,531,532,533,534,535,536,537,539,540} Overall, a mean 58.9% (95% CI, 58.0%-59.7%) of models required contents in the data domain.

Approximately half of the models (258 of 555 [46.48%; 95% CI, 42.3%- 50.6%]) were assessed for poor reporting quality in the technique domain across 3 signaling questions.^{25,28,29,33,34,35,44,45,47,48,49,51,53,54,56,59,60,62,66,73,75,78,79,81,83,84,95,99,100,107,114,117,122,124,126,128,129,130,131,133,135,137,142,144,145,146,147,148,151,153,155,159,161,163,165,166,167,169,171,173,174,177,179,183,184,197,199,201,202,203,204,206,207,208,210,211,212,213,216,217,221,224,225,229,231,233,234,235,236,237,243,245,246,247,248,253,255,257,258,259,274,275,278,279,283,284,285,288,291,294,297,299,302,304,305,306,307,311,314,317,318,319,320,321,322,323,324,325,326,328,329,330,331,333,334,335,336,339,343,344,345,346,347,350,351,353,355,357,360,361,362,364,365,372,375,376,379,382,384,385,389,390,396,399,402,403,404,405,406,413,415,416,417,418,420,421,422,423,424,425,429,430,431,432,433,434,436,439,440,441,442,445,446,448,449,450,451,452,453,456,464,466,470,471,472,473,474,475,476,478,480,481,486,487,492,493,495,498,500,502,503,504,505,506,507,509,511,513,515,516,518,521,528,529,530,531,532,535,536,539,541} The unique source for the degraded reporting quality in this domain was the absence of algorithmic details, which limited reproducibility (eg, signaling question: were details for algorithm development reported?). The mean reporting completeness for these models in the technique domain was satisfied (84.5%; 95% CI, 83.1%-85.8%).

All models were rated for poor reporting quality in the technical assessment domain across 4 signaling questions. Of the 555 models, 544 (98.0%; 95% CI, 96.9%-99.2%) lacked reports for how to evaluate AI performance publicly.^{25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,472,473,474,475,476,477,478,479,481,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,503,504,505,506,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,538,539,540,541} Additionally, the absence-of-bias assessment was associated with degraded reporting quality in this domain (eg, signaling question: were bias assessments discussed?). Thus, the overall reporting completeness for these AI models was the poorest in the technical assessment domain (39.9%; 95% CI, 38.8%-41.1%).

One-hundred fifty of 555 models (27.0%; 95% CI, 23.3%-30.7%) were rated with poor reporting quality in the applications domain across 2 signaling questions.^{26,28,29,32,36,38,41,45,50,51,60,65,69,74,78,82,84,94,97,99,105,107,108,112,117,119,120,125,132,133,134,138,139,141,143,146,150,159,172,183,186,190,193,194,200,203,204,214,219,220,222,223,228,229,230,232,233,241,243,244,251,257,259,260,261,263,267,269,274,280,281,284,285,286,291,293,297,298,300,301,311,312,313,315,316,321,329,337,340,343,354,361,363,369,370,371,379,392,395,397,399,405,408,411,413,418,419,422,423,432,440,445,446,448,449,456,457,461,471,473,474,476,481,484,485,490,496,506,513,514,520,527,538,539,540} Lacking intrinsic reports or discussions of the clinical implications of these models was the main source of the poor rating in this domain (eg, signaling question: were use cases and target conditions discussed?). The mean overall reporting completeness in the applications domain was found to be favorable (84.6%; 95% CI, 81.8%-86.5%).

Discussion

Despite the tremendous potential for neuroimaging-based AI models in psychiatric research and clinical translation, the present study showed a high ROB that was associated with barriers to translation of the models into clinical application. To our surprise, the majority of models exhibited a high ROB. These models further exhibited low potential for both clinical and health care applicability based on the PROBAST. Thus, optimistic claims with respect to reliable AI-based psychiatric diagnosis should be considered with caution. Furthermore, poor reporting quality (with a mean reporting completeness of 61.2%) was found to be associated with additional barriers to clinical and practical translation of these models. Despite their high potential, the AI models presented to date do not fulfill the criteria for being recommended for use in psychiatric diagnostics, and several challenges remain on the way to translation.

In this study, we found that 83.1% of existing AI models exhibited a high ROB for psychiatric diagnosis. These findings align with results of a multivariable statistical model (eg, clinical prediction model [CPM]) for diagnosing both somatic and psychiatric disorders that showed 98% and 94% of the models, respectively, had a high ROB in diagnosis.^12,545 In addition, high ROB was observed for prognostication of oral health (75% of models) and cardiovascular health (81% of models).^546,547 Thus, compared with traditional statistical models, the current AI models did not fulfill the promise of superior technical (statistical) model merits (ie, unbiased prediction) in clinical practice. In addition, unlike neurological or physiological diseases with clear nosological landmarks, neuroimaging-based AI models may outperform traditional clinical models for psychiatric diagnosis only if these neuromarkers were associated with apparently differentiable actionability. This situation may be an additional source of high ROB in these models.

The main reason for incurring high ROB in these AI models was found in the analysis domain, especially the sample size and model configurations. Inadequate sample size represented a long-debated concern leading to inflated model performance in neuroimaging-based AI.^8,9,548 Despite the ongoing debate, a sample of more than 200 participants for building AI models is increasingly becoming commonly acceptable.^{549,550,551,552} However, about 80% of AI models for psychiatric diagnosis were trained on far smaller samples, leading to a high ROB and resulting in poor generalizability. On the other hand, model configurations, including data leakage, performance optimization, and absence of handling data complexities, represented key challenges to increasing model ROB. Data leakage, for instance, referred to the role that knowledge of outcome played in feature selection in the training set.⁵⁵³ Compared with traditional statistical models, the number of neuroimaging-based features (eg, voxel and functional connectivity) was large. For dimension reduction, univariate analysis was consistently used for feature selection in the whole data set, which made testing the data set no longer independent.^9,551 In addition, unlike CPM, the data structure for AI models allows for a substantial variation in optimizing and turning parameters to manipulate performance.^554,555 Fine-tuning the models may thus improve diagnostic performance, but such tailor-built configurations sacrifice generalizability for unseen patients.⁵⁵⁶ On balance, a high ROB in the analysis domain should be addressed to prompt clinical applications in future AI models.

Standardized and transparent reports for a clinical diagnostic model are increasingly advocated to improve clinical applicability and unbiased practice.^557,558 However, we found that the reporting completeness following a benchmark assessment was only 61.2%, critically limiting replicability and applicability. The finding of insufficient reporting resonated with the study by Heus et al⁵⁵⁹ that found low overall reporting completeness (ie, 44%) for CPM across clinical domains. With regard to AI models for other clinical diseases, the reporting completeness was still approximately 50%.^560,561 Going forward, complete and transparent reporting quality is imperative for applicable and feasible AI diagnostic models in clinical practice.

Taken together, a concise guideline that consists of 3 aspects is proposed in this study: (1) adopt appropriate reporting guidelines (eg, CLEAR) and benchmarks (eg, PROBAST-AI) before building models; (2) increase attention on the quality of models (eg, adequate sample size, rigorous calibration examination, and controls for data complexities); and (3) propel translational or accessible diagnostic models (eg, report more details for technical assessments and access enough model materials for clinical validations or applications).

Limitations

This study has limitations. One limitation is the potential overestimation of ROB due to the lack of a suitable benchmark. Despite the extension of the PROBAST to AI models, the applicable version is pending.⁵⁶² Results of the current study could be validated by the PROBAST-AI when it becomes available. Similarly, the findings regarding the reporting quality of these models could be enriched by the development of a psychiatry-specific image-based AI model reporting checklist. A second limitation is the rating heterogeneity of the benchmarks we used. Although it had acceptable reliability, a certain assessment was found to have heterogeneity by assessors with specific expertise, especially in different assessment domains (eMethods 6 in Supplement 1). Thus, it would be highly beneficial if experts with interdisciplinary scientific training validated the findings by rating the AI models. A third limitation is that we did not test the models’ clinical performance; thus, it is prudent for future studies to examine these models in clinical practice to determine their applicability.

Conclusions

In this systematic review of risks for available neuroimaging-based AI models for psychiatric diagnosis, most AI models had an overall high ROB, especially in the analysis domain. Furthermore, the overall reporting quality was poor, and the mean reporting completeness for these models was 61.2%. Existing AI models may not be as mature for direct clinical applications as is claimed given their high ROB and poor reporting quality. In the analysis domain, ROB in of AI diagnostic models should be addressed before clinical application.

Supplement 1.

eMethods 1. Searching Strategy and RSS Feed

eMethods 2. Literature Searching Pipeline and Data Extraction/Coding

eMethods 3. Geospatial Model

eMethods 4. PROBAST Assessment Criterion

eMethods 5. CLEAR Modification

eMethods 6. Cohen Kappa Test and Heterogeneity

eTable 1. Modified CLEAR Signaling Questions

eTable 2. Academic Background Distribution for Included Studies

eTable 3. Distribution for Psychiatric Categories in Existing AI Models

eTable 4. Distribution for AI Algorithms in These Models

eTable 5. Distribution for Toolkit of These AI Models

eTable 6. Distribution for Future Selection Methods of These AI Models

eTable 7. Distribution for Cross-Validation Regimes of These AI Models

eTable 8. Distribution for Neuroimaging-based Data Modality of These AI Models

eTable 9. Distribution for Preprocessing Method of These AI Models

eReferences

Click here for additional data file.^{(353.6KB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(18.1KB, pdf)}

References

1.Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises. Biol Psychiatry. 2020;88(11):818-828. doi: 10.1016/j.biopsych.2020.02.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage. 2017;145(Pt B):137-165. doi: 10.1016/j.neuroimage.2016.02.079 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Eyre HA, Singh AB, Reynolds C III. Tech giants enter mental health. World Psychiatry. 2016;15(1):21-22. doi: 10.1002/wps.20297 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255-260. doi: 10.1126/science.aaa8415 [DOI] [PubMed] [Google Scholar]
5.Yu JS, Xue AY, Redei EE, Bagheri N. A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder. Transl Psychiatry. 2016;6(10):e931. doi: 10.1038/tp.2016.198 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Hazlett HC, Gu H, Munsell BC, et al. ; IBIS Network; Clinical Sites; Data Coordinating Center; Image Processing Core; Statistical Analysis . Early brain development in infants at high risk for autism spectrum disorder. Nature. 2017;542(7641):348-351. doi: 10.1038/nature21369 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Lv J, Di Biase M, Cash RFH, et al. Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort. Mol Psychiatry. 2021;26(7):3512-3523. doi: 10.1038/s41380-020-00882-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Nielsen AN, Barch DM, Petersen SE, Schlaggar BL, Greene DJ. Machine learning with neuroimaging: evaluating its applications in psychiatry. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5(8):791-798. doi: 10.1016/j.bpsc.2019.11.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Varoquaux G, Raamana PR, Engemann DA, Hoyos-Idrobo A, Schwartz Y, Thirion B. Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage. 2017;145(pt B):166-179. doi: 10.1016/j.neuroimage.2016.10.038 [DOI] [PubMed] [Google Scholar]
10.Roberts M, Driggs D, Thorpe M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell. 2021;3:199-217. doi: 10.1038/s42256-021-00307-0 [DOI] [Google Scholar]
11.Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of Covid-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi: 10.1136/bmj.m1328 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Andaur Navarro CL, Damen JAA, Takada T, et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ. 2021;375(2281):n2281. doi: 10.1136/bmj.n2281 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Meehan AJ, Lewis SJ, Fazel S, et al. Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges. Mol Psychiatry. 2022;27(6):2700-2708. doi: 10.1038/s41380-022-01528-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Walter M, Alizadeh S, Jamalabadi H, et al. Translational machine learning for psychiatric neuroimaging. Prog Neuropsychopharmacol Biol Psychiatry. 2019;91:113-121. doi: 10.1016/j.pnpbp.2018.09.014 [DOI] [PubMed] [Google Scholar]
15.Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223-230. doi: 10.1016/j.bpsc.2017.11.007 [DOI] [PubMed] [Google Scholar]
16.Vasey B, Nagendran M, Campbell B, et al. ; DECIDE-AI expert group . Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022;28(5):924-933. doi: 10.1038/s41591-022-01772-9 [DOI] [PubMed] [Google Scholar]
17.Bossuyt PM, Reitsma JB, Bruns DE, et al. ; Standards for Reporting of Diagnostic Accuracy . Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ. 2003;326(7379):41-44. doi: 10.1136/bmj.326.7379.41 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. doi: 10.1136/bmj.g7594 [DOI] [PubMed] [Google Scholar]
19.Lu JH, Callahan A, Patel BS, et al. Assessment of adherence to reporting guidelines by commonly used clinical prediction models from a single vendor: a systematic review. JAMA Netw Open. 2022;5(8):e2227779. doi: 10.1001/jamanetworkopen.2022.27779 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Glasziou P, Altman DG, Bossuyt P, et al. Reducing waste from incomplete or unusable reports of biomedical research. Lancet. 2014;383(9913):267-276. doi: 10.1016/S0140-6736(13)62228-X [DOI] [PubMed] [Google Scholar]
21.Cai R, Wu X, Li C, Chao J. Prediction models for cardiovascular disease risk in the hypertensive population: a systematic review. J Hypertens. 2020;38(9):1632-1639. doi: 10.1097/HJH.0000000000002442 [DOI] [PubMed] [Google Scholar]
22.Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689. doi: 10.1136/bmj.m689 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Daneshjou R, Barata C, Betz-Stablein B, et al. Checklist for evaluation of image-based artificial intelligence reports in dermatology: CLEAR Derm consensus guidelines from the International Skin Imaging Collaboration Artificial Intelligence Working Group. JAMA Dermatol. 2022;158(1):90-96. doi: 10.1001/jamadermatol.2021.4915 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Wolff RF, Moons KGM, Riley RD, et al. ; PROBAST Group . PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51-58. doi: 10.7326/M18-1376 [DOI] [PubMed] [Google Scholar]
25.Knoth IS, Lajnef T, Rigoulot S, et al. Auditory repetition suppression alterations in relation to cognitive functioning in fragile X syndrome: a combined EEG and machine learning approach. J Neurodev Disord. 2018;10(1):4. doi: 10.1186/s11689-018-9223-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Pedersen M, Curwood EK, Archer JS, Abbott DF, Jackson GD. Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: multivariate analysis of task-free fMRI. Epilepsia. 2015;56(11):1767-1773. doi: 10.1111/epi.13135 [DOI] [PubMed] [Google Scholar]
27.Wang Y, Yuan L, Shi J, et al. Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis. Neuroimage. 2013;74:209-230. doi: 10.1016/j.neuroimage.2013.02.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Hoeft F, Walter E, Lightbody AA, et al. Neuroanatomical differences in toddler boys with fragile x syndrome and idiopathic autism. Arch Gen Psychiatry. 2011;68(3):295-305. doi: 10.1001/archgenpsychiatry.2010.153 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Parisot S, Ktena SI, Ferrante E, et al. Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med Image Anal. 2018;48:117-130. doi: 10.1016/j.media.2018.06.001 [DOI] [PubMed] [Google Scholar]
30.Matlis S, Boric K, Chu CJ, Kramer MA. Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism. BMC Neurol. 2015;15:97. doi: 10.1186/s12883-015-0355-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Ingalhalikar M, Parker D, Bloy L, Roberts TP, Verma R. Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD. Neuroimage. 2011;57(3):918-927. doi: 10.1016/j.neuroimage.2011.05.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Shahamat H, Saniee Abadeh M. Brain MRI analysis using a deep learning based evolutionary approach. Neural Netw. 2020;126:218-234. doi: 10.1016/j.neunet.2020.03.017 [DOI] [PubMed] [Google Scholar]
33.Bajestani GS, Behrooz M, Khani AG, Nouri-Baygi M, Mollaei A. Diagnosis of autism spectrum disorder based on complex network features. Comput Methods Programs Biomed. 2019;177:277-283. doi: 10.1016/j.cmpb.2019.06.006 [DOI] [PubMed] [Google Scholar]
34.Lanka P, Rangaprakash D, Dretsch MN, Katz JS, Denney TS Jr, Deshpande G. Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging Behav. 2020;14(6):2378-2416. doi: 10.1007/s11682-019-00191-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Zhang L, Wang XH, Li L. Diagnosing autism spectrum disorder using brain entropy: a fast entropy method. Comput Methods Programs Biomed. 2020;190:105240. doi: 10.1016/j.cmpb.2019.105240 [DOI] [PubMed] [Google Scholar]
36.Rakić M, Cabezas M, Kushibar K, Oliver A, Lladó X. Improving the detection of autism spectrum disorder by combining structural and functional MRI information. Neuroimage Clin. 2020;25:102181. doi: 10.1016/j.nicl.2020.102181 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Ahmadlou M, Adeli H, Adeli A. Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder. J Clin Neurophysiol. 2010;27(5):328-333. doi: 10.1097/WNP.0b013e3181f40dc8 [DOI] [PubMed] [Google Scholar]
38.Libero LE, DeRamus TP, Lahti AC, Deshpande G, Kana RK. Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates. Cortex. 2015;66:46-59. doi: 10.1016/j.cortex.2015.02.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Pham TH, Vicnesh J, Wei JKE, et al. Autism spectrum disorder diagnostic system using HOS bispectrum with EEG signals. Int J Environ Res Public Health. 2020;17(3):971. doi: 10.3390/ijerph17030971 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Graa O, Rekik I. Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes. J Neurosci Methods. 2019;327:108344. doi: 10.1016/j.jneumeth.2019.108344 [DOI] [PubMed] [Google Scholar]
41.Ingalhalikar M, Smith AR, Bloy L, Gur R, Roberts TPL, Verma R. Identifying sub-populations via unsupervised cluster analysis on multi-edge similarity graphs. In: Ayache N, Delingette H, Golland P, Mori K, eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science. Vol 7511. Springer; 2012:254-261. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Khosla M, Jamison K, Kuceyeski A, Sabuncu MR. Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction. Neuroimage. 2019;199:651-662. doi: 10.1016/j.neuroimage.2019.06.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Li H, Parikh NA, He L. A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front Neurosci. 2018;12:491. doi: 10.3389/fnins.2018.00491 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Sen B, Borle NC, Greiner R, Brown MRG. A general prediction model for the detection of ADHD and autism using structural and functional MRI. PLoS One. 2018;13(4):e0194856. doi: 10.1371/journal.pone.0194856 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Xu L, Hua Q, Yu J, Li J. Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal. Clin Neurophysiol. 2020;131(6):1365-1374. doi: 10.1016/j.clinph.2019.12.400 [DOI] [PubMed] [Google Scholar]
46.Ma X, Wang XH, Li L. Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony. Neurosci Lett. 2021;742:135519. doi: 10.1016/j.neulet.2020.135519 [DOI] [PubMed] [Google Scholar]
47.Rakhimberdina Z, Liu X, Murata AT. Population graph-based multi-model ensemble method for diagnosing autism spectrum disorder. Sensors (Basel). 2020;20(21):6001. doi: 10.3390/s20216001 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Tsiaras V, Simos PG, Rezaie R, et al. Extracting biomarkers of autism from MEG resting-state functional connectivity networks. Comput Biol Med. 2011;41(12):1166-1177. doi: 10.1016/j.compbiomed.2011.04.004 [DOI] [PubMed] [Google Scholar]
49.Wang H, Chen C, Fushing H. Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders. PLoS One. 2012;7(10):e45502. doi: 10.1371/journal.pone.0045502 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Hu J, Cao L, Li T, Liao B, Dong S, Li P. Interpretable learning approaches in resting-state functional connectivity analysis: the case of autism spectrum disorder. Comput Math Methods Med. 2020;2020:1394830. doi: 10.1155/2020/1394830 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Jung M, Tu Y, Park J, et al. Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder. Br J Psychiatry. 2019;214(6):339-344. doi: 10.1192/bjp.2018.248 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage Clin. 2017;17:16-23. doi: 10.1016/j.nicl.2017.08.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Bhaumik R, Pradhan A, Das S, Bhaumik DK. Predicting autism spectrum disorder using domain-adaptive cross-site evaluation. Neuroinformatics. 2018;16(2):197-205. doi: 10.1007/s12021-018-9366-0 [DOI] [PubMed] [Google Scholar]
54.Wang L, Wee CY, Tang X, Yap PT, Shen D. Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder. Brain Imaging Behav. 2016;10(1):33-40. doi: 10.1007/s11682-015-9360-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Ecker C, Rocha-Rego V, Johnston P, et al. ; MRC AIMS Consortium . Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage. 2010;49(1):44-56. doi: 10.1016/j.neuroimage.2009.08.024 [DOI] [PubMed] [Google Scholar]
56.Payabvash S, Palacios EM, Owen JP, et al. White matter connectome edge density in children with autism spectrum disorders: potential imaging biomarkers using machine-learning models. Brain Connect. 2019;9(2):209-220. doi: 10.1089/brain.2018.0658 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Price T, Wee CY, Gao W, Shen D. Multiple-network classification of childhood autism using functional connectivity dynamics. In: Golland P, Hata N, Barillot C, Hornegger J, Howe R, eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science. Vol 8675. Springer, Cham; 2014:177-184. [DOI] [PubMed] [Google Scholar]
58.Haweel R, Shalaby A, Mahmoud A, et al. A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI. Med Phys. 2021;48(5):2315-2326. doi: 10.1002/mp.14692 [DOI] [PubMed] [Google Scholar]
59.Chen CP, Keown CL, Jahedi A, et al. Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. Neuroimage Clin. 2015;8:238-245. doi: 10.1016/j.nicl.2015.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Anderson JS, Nielsen JA, Froehlich AL, et al. Functional connectivity magnetic resonance imaging classification of autism. Brain. 2011;134(Pt 12):3742-3754. doi: 10.1093/brain/awr263 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Uddin LQ, Supekar K, Lynch CJ, et al. Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry. 2013;70(8):869-879. doi: 10.1001/jamapsychiatry.2013.104 [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Nielsen JA, Zielinski BA, Fletcher PT, et al. Multisite functional connectivity MRI classification of autism: ABIDE results. Front Hum Neurosci. 2013;7:599. doi: 10.3389/fnhum.2013.00599 [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Jahedi A, Nasamran CA, Faires B, Fan J, Müller RA. Distributed intrinsic functional connectivity patterns predict diagnostic status in large autism cohort. Brain Connect. 2017;7(8):515-525. doi: 10.1089/brain.2017.0496 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Retico A, Giuliano A, Tancredi R, et al. The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study. Mol Autism. 2016;7:5. doi: 10.1186/s13229-015-0067-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Calderoni S, Retico A, Biagi L, Tancredi R, Muratori F, Tosetti M. Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses. Neuroimage. 2012;59(2):1013-1022. doi: 10.1016/j.neuroimage.2011.08.070 [DOI] [PubMed] [Google Scholar]
66.Yamagata B, Itahashi T, Fujino J, et al. Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder. Brain Imaging Behav. 2019;13(6):1689-1698. doi: 10.1007/s11682-018-9973-2 [DOI] [PubMed] [Google Scholar]
67.Gori I, Giuliano A, Muratori F, et al. Gray matter alterations in young children with autism spectrum disorders: comparing morphometry at the voxel and regional level. J Neuroimaging. 2015;25(6):866-874. doi: 10.1111/jon.12280 [DOI] [PubMed] [Google Scholar]
68.Leming M, Górriz JM, Suckling J. Ensemble deep learning on large, mixed-site fMRI datasets in autism and other tasks. Int J Neural Syst. 2020;30(7):2050012. doi: 10.1142/S0129065720500124 [DOI] [PubMed] [Google Scholar]
69.Shen MD, Nordahl CW, Li DD, et al. Extra-axial cerebrospinal fluid in high-risk and normal-risk children with autism aged 2-4 years: a case-control study. Lancet Psychiatry. 2018;5(11):895-904. doi: 10.1016/S2215-0366(18)30294-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Grossi E, Olivieri C, Buscema M. Diagnosis of autism through EEG processed by advanced computational algorithms: a pilot study. Comput Methods Programs Biomed. 2017;142:73-79. doi: 10.1016/j.cmpb.2017.02.002 [DOI] [PubMed] [Google Scholar]
71.Gupta S, Rajapakse JC, Welsch RE; Alzheimer’s Disease Neuroimaging Initiative . Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s disease and autism spectrum disorder. Neuroimage Clin. 2020;25:102186. doi: 10.1016/j.nicl.2020.102186 [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Ghiassian S, Greiner R, Jin P, Brown MR. Using functional or structural magnetic resonance images and personal characteristic data to identify ADHD and autism. PLoS One. 2016;11(12):e0166934. doi: 10.1371/journal.pone.0166934 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Zu C, Gao Y, Munsell B, et al. Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning. Brain Imaging Behav. 2019;13(4):879-892. doi: 10.1007/s11682-018-9899-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Katuwal GJ, Baum SA, Cahill ND, Michael AM. Divide and conquer: sub-grouping of ASD improves ASD detection based on brain morphometry. PLoS One. 2016;11(4):e0153331. doi: 10.1371/journal.pone.0153331 [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Li Q, Becker B, Jiang X, et al. Decreased interhemispheric functional connectivity rather than corpus callosum volume as a potential biomarker for autism spectrum disorder. Cortex. 2019;119:258-266. doi: 10.1016/j.cortex.2019.05.003 [DOI] [PubMed] [Google Scholar]
76.Dekhil O, Hajjdiab H, Shalaby A, et al. Using resting state functional MRI to build a personalized autism diagnosis system. PLoS One. 2018;13(10):e0206351. doi: 10.1371/journal.pone.0206351 [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Yamagata B, Itahashi T, Fujino J, et al. Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder. Psychiatry Clin Neurosci. 2019;73(7):409-415. doi: 10.1111/pcn.12854 [DOI] [PubMed] [Google Scholar]
78.Iidaka T. Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex. 2015;63:55-67. doi: 10.1016/j.cortex.2014.08.011 [DOI] [PubMed] [Google Scholar]
79.Jamal W, Das S, Oprescu IA, Maharatna K, Apicella F, Sicca F. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. J Neural Eng. 2014;11(4):046019. doi: 10.1088/1741-2560/11/4/046019 [DOI] [PubMed] [Google Scholar]
80.Zhang F, Savadjiev P, Cai W, et al. Whole brain white matter connectivity analysis using machine learning: an application to autism. Neuroimage. 2018;172:826-837. doi: 10.1016/j.neuroimage.2017.10.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Sujit SJ, Coronado I, Kamali A, Narayana PA, Gabr RE. Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks. J Magn Reson Imaging. 2019;50(4):1260-1267. doi: 10.1002/jmri.26693 [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Huang H, Liu X, Jin Y, Lee SW, Wee CY, Shen D. Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis. Hum Brain Mapp. 2019;40(3):833-854. doi: 10.1002/hbm.24415 [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Eill A, Jahedi A, Gao Y, et al. Functional connectivities are more informative than anatomical variables in diagnostic classification of autism. Brain Connect. 2019;9(8):604-612. doi: 10.1089/brain.2019.0689 [DOI] [PMC free article] [PubMed] [Google Scholar]
84.Xiao X, Fang H, Wu J, et al. Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder. Autism Res. 2017;10(4):620-630. doi: 10.1002/aur.1711 [DOI] [PubMed] [Google Scholar]
85.Kam TE, Suk HI, Lee SW. Multiple functional networks modeling for autism spectrum disorder diagnosis. Hum Brain Mapp. 2017;38(11):5804-5821. doi: 10.1002/hbm.23769 [DOI] [PMC free article] [PubMed] [Google Scholar]
86.Ktena SI, Parisot S, Ferrante E, et al. Metric learning with spectral graph convolutions on brain connectivity networks. Neuroimage. 2018;169:431-442. doi: 10.1016/j.neuroimage.2017.12.052 [DOI] [PubMed] [Google Scholar]
87.Aghdam MA, Sharifi A, Pedram MM. Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks. J Digit Imaging. 2019;32(6):899-918. doi: 10.1007/s10278-019-00196-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Sadeghi M, Khosrowabadi R, Bakouie F, Mahdavi H, Eslahchi C, Pouretemad H. Screening of autism based on task-free fMRI using graph theoretical approach. Psychiatry Res Neuroimaging. 2017;263:48-56. doi: 10.1016/j.pscychresns.2017.02.004 [DOI] [PubMed] [Google Scholar]
89.Chaddad A, Desrosiers C, Hassan L, Tanougast C. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neurosci. 2017;18(1):52. doi: 10.1186/s12868-017-0373-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Djemal R, AlSharabi K, Ibrahim S, Alsuwailem A. EEG-based computer aided diagnosis of autism spectrum disorder using wavelet, entropy, and ANN. Biomed Res Int. 2017;2017:9816591. doi: 10.1155/2017/9816591 [DOI] [PMC free article] [PubMed] [Google Scholar]
91.Yahata N, Morimoto J, Hashimoto R, et al. A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat Commun. 2016;7:11254. doi: 10.1038/ncomms11254 [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Heunis T, Aldrich C, Peters JM, et al. Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder: a systematic methodological exploration of technical and demographic confounders in the search for biomarkers. BMC Med. 2018;16(1):101. doi: 10.1186/s12916-018-1086-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
93.Chen H, Duan X, Liu F, et al. Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity: a multi-center study. Prog Neuropsychopharmacol Biol Psychiatry. 2016;64:1-9. doi: 10.1016/j.pnpbp.2015.06.014 [DOI] [PubMed] [Google Scholar]
94.Just MA, Cherkassky VL, Buchweitz A, Keller TA, Mitchell TM. Identifying autism from neural representations of social interactions: neurocognitive markers of autism. PLoS One. 2014;9(12):e113879. doi: 10.1371/journal.pone.0113879 [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Akhavan Aghdam M, Sharifi A, Pedram MM. Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J Digit Imaging. 2018;31(6):895-903. doi: 10.1007/s10278-018-0093-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
96.Alturki FA, AlSharabi K, Abdurraqeeb AM, Aljalal M. EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques. Sensors (Basel). 2020;20(9):2505. doi: 10.3390/s20092505 [DOI] [PMC free article] [PubMed] [Google Scholar]
97.Spiegel A, Mentch J, Haskins AJ, Robertson CE. Slower binocular rivalry in the autistic brain. Curr Biol. 2019;29(17):2948-2953.e3. doi: 10.1016/j.cub.2019.07.026 [DOI] [PubMed] [Google Scholar]
98.Conti E, Retico A, Palumbo L, et al. Autism spectrum disorder and childhood apraxia of speech: early language-related hallmarks across structural MRI study. J Pers Med. 2020;10(4):275. doi: 10.3390/jpm10040275 [DOI] [PMC free article] [PubMed] [Google Scholar]
99.Bi XA, Liu Y, Jiang Q, Shu Q, Sun Q, Dai J. The diagnosis of autism spectrum disorder based on the random neural network cluster. Front Hum Neurosci. 2018;12:257. doi: 10.3389/fnhum.2018.00257 [DOI] [PMC free article] [PubMed] [Google Scholar]
100.Pollonini L, Patidar U, Situ N, Rezaie R, Papanicolaou AC, Zouridakis G. Functional connectivity networks in the autistic and healthy brain assessed using Granger causality. Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1730-1733. doi: 10.1109/IEMBS.2010.5626702 [DOI] [PubMed] [Google Scholar]
101.Eldridge J, Lane AE, Belkin M, Dennis S. Robust features for the automatic identification of autism spectrum disorder in children. J Neurodev Disord. 2014;6(1):12. doi: 10.1186/1866-1955-6-12 [DOI] [PMC free article] [PubMed] [Google Scholar]
102.Khan NA, Waheeb SA, Riaz A, Shang X. A three-stage teacher, student neural networks and sequential feed forward selection-based feature selection approach for the classification of autism spectrum disorder. Brain Sci. 2020;10(10):754. doi: 10.3390/brainsci10100754 [DOI] [PMC free article] [PubMed] [Google Scholar]
103.Sherkatghanad Z, Akhondzadeh M, Salari S, et al. Automated detection of autism spectrum disorder using a convolutional neural network. Front Neurosci. 2020;13:1325. doi: 10.3389/fnins.2019.01325 [DOI] [PMC free article] [PubMed] [Google Scholar]
104.Gao J, Chen M, Li Y, et al. Multisite autism spectrum disorder classification using convolutional neural network classifier and individual morphological brain networks. Front Neurosci. 2021;14:629630. doi: 10.3389/fnins.2020.629630 [DOI] [PMC free article] [PubMed] [Google Scholar]
105.Liu Y, Xu L, Li J, Yu J, Yu X. Attentional connectivity-based prediction of autism using heterogeneous rs-fMRI data from CC200 atlas. Exp Neurobiol. 2020;29(1):27-37. doi: 10.5607/en.2020.29.1.27 [DOI] [PMC free article] [PubMed] [Google Scholar]
106.Yang M, Cao M, Chen Y, et al. Large-scale brain functional network integration for discrimination of autism using a 3-D deep learning model. Front Hum Neurosci. 2021;15:687288. doi: 10.3389/fnhum.2021.687288 [DOI] [PMC free article] [PubMed] [Google Scholar]
107.Huang ZA, Zhu Z, Yau CH, Tan KC. Identifying autism spectrum disorder from resting-state fMRI using deep belief network. IEEE Trans Neural Netw Learn Syst. 2021;32(7):2847-2861. doi: 10.1109/TNNLS.2020.3007943 [DOI] [PubMed] [Google Scholar]
108.Zhao J, Song J, Li X, Kang J. A study on EEG feature extraction and classification in autistic children based on singular spectrum analysis method. Brain Behav. 2020;10(12):e01721. doi: 10.1002/brb3.1721 [DOI] [PMC free article] [PubMed] [Google Scholar]
109.Almuqhim F, Saeed F. ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data. Front Comput Neurosci. 2021;15:654315. doi: 10.3389/fncom.2021.654315 [DOI] [PMC free article] [PubMed] [Google Scholar]
110.Xu L, Sun Z, Xie J, Yu J, Li J, Wang J. Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network. Clin Neurophysiol. 2021;132(2):457-468. doi: 10.1016/j.clinph.2020.11.037 [DOI] [PubMed] [Google Scholar]
111.Lu J, Kishida K, De Asis Cruz J, et al. Single stimulus fMRI produces a neural individual difference measure for autism spectrum disorder. Clin Psychol Sci. 2015;3(3):422-432. doi: 10.1177/2167702614562042 [DOI] [PMC free article] [PubMed] [Google Scholar]
112.Ahmed MR, Zhang Y, Liu Y, Liao H. Single volume image generator and deep learning-based ASD classification. IEEE J Biomed Health Inform. 2020;24(11):3044-3054. doi: 10.1109/JBHI.2020.2998603 [DOI] [PubMed] [Google Scholar]
113.Xu L, Geng X, He X, Li J, Yu J. Prediction in autism by deep learning short-time spontaneous Hemodynamic fluctuations. Front Neurosci. 2019;13:1120. doi: 10.3389/fnins.2019.01120 [DOI] [PMC free article] [PubMed] [Google Scholar]
114.Wee CY, Wang L, Shi F, Yap PT, Shen D. Diagnosis of autism spectrum disorders using regional and interregional morphological features. Hum Brain Mapp. 2014;35(7):3414-3430. doi: 10.1002/hbm.22411 [DOI] [PMC free article] [PubMed] [Google Scholar]
115.Sewani H, Kashef R. An autoencoder-based deep learning classifier for efficient diagnosis of autism. Children (Basel). 2020;7(10):182. doi: 10.3390/children7100182 [DOI] [PMC free article] [PubMed] [Google Scholar]
116.Shi C, Xin X, Zhang J. Domain adaptation using a three-way decision improves the identification of autism patients from multisite fMRI data. Brain Sci. 2021;11(5):603. doi: 10.3390/brainsci11050603 [DOI] [PMC free article] [PubMed] [Google Scholar]
117.Kazeminejad A, Sotero RC. The importance of anti-correlations in graph theory based classification of autism spectrum disorder. Front Neurosci. 2020;14:676. doi: 10.3389/fnins.2020.00676 [DOI] [PMC free article] [PubMed] [Google Scholar]
118.Yin W, Mostafa S, Wu FX. Diagnosis of autism spectrum disorder based on functional brain networks with deep learning. J Comput Biol. 2021;28(2):146-165. doi: 10.1089/cmb.2020.0252 [DOI] [PubMed] [Google Scholar]
119.Jiao Y, Chen R, Ke X, Chu K, Lu Z, Herskovits EH. Predictive models of autism spectrum disorder based on brain regional cortical thickness. Neuroimage. 2010;50(2):589-599. doi: 10.1016/j.neuroimage.2009.12.047 [DOI] [PMC free article] [PubMed] [Google Scholar]
120.Murdaugh DL, Shinkareva SV, Deshpande HR, Wang J, Pennick MR, Kana RK. Differential deactivation during mentalizing and classification of autism based on default mode network connectivity. PLoS One. 2012;7(11):e50064. doi: 10.1371/journal.pone.0050064 [DOI] [PMC free article] [PubMed] [Google Scholar]
121.Song Y, Epalle TM, Lu H. Characterizing and predicting autism spectrum disorder by performing resting-state functional network community pattern analysis. Front Hum Neurosci. 2019;13:203. doi: 10.3389/fnhum.2019.00203 [DOI] [PMC free article] [PubMed] [Google Scholar]
122.Irimia A, Lei X, Torgerson CM, Jacokes ZJ, Abe S, Van Horn JD. Support vector machines, multidimensional scaling and magnetic resonance imaging reveal structural brain abnormalities associated with the interaction between autism spectrum disorder and sex. Front Comput Neurosci. 2018;12:93. doi: 10.3389/fncom.2018.00093 [DOI] [PMC free article] [PubMed] [Google Scholar]
123.Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F. ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front Neuroinform. 2019;13:70. doi: 10.3389/fninf.2019.00070 [DOI] [PMC free article] [PubMed] [Google Scholar]
124.Sarovic D, Hadjikhani N, Schneiderman J, Lundström S, Gillberg C. Autism classified by magnetic resonance imaging: a pilot study of a potential diagnostic tool. Int J Methods Psychiatr Res. 2020;29(4):1-18. doi: 10.1002/mpr.1846 [DOI] [PMC free article] [PubMed] [Google Scholar]
125.Kazeminejad A, Sotero RC. Topological properties of resting-state fMRI functional networks improve machine learning-based autism classification. Front Neurosci. 2019;12:1018. doi: 10.3389/fnins.2018.01018 [DOI] [PMC free article] [PubMed] [Google Scholar]
126.Guo X, Dominick KC, Minai AA, Li H, Erickson CA, Lu LJ. Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method. Front Neurosci. 2017;11:460. doi: 10.3389/fnins.2017.00460 [DOI] [PMC free article] [PubMed] [Google Scholar]
127.Thomas RM, Gallo S, Cerliani L, Zhutovsky P, El-Gazzar A, van Wingen G. Classifying autism spectrum disorder using the temporal statistics of resting-state functional MRI data with 3D convolutional neural networks. Front Psychiatry. 2020;11:440. doi: 10.3389/fpsyt.2020.00440 [DOI] [PMC free article] [PubMed] [Google Scholar]
128.Li X, Zhou Y, Dvornek N, et al. BrainGNN: interpretable brain graph neural network for fMRI analysis. Med Image Anal. 2021;74:102233. doi: 10.1016/j.media.2021.102233 [DOI] [PMC free article] [PubMed] [Google Scholar]
129.Wang Z, Peng D, Shang Y, Gao J. Autistic spectrum disorder detection and structural biomarker identification using self-attention model and individual-level morphological covariance brain networks. Front Neurosci. 2021;15:756868. doi: 10.3389/fnins.2021.756868 [DOI] [PMC free article] [PubMed] [Google Scholar]
130.Haweel R, Seada N, Ghoniemy S, Alghamdi NS, El-Baz A. A CNN deep local and global ASD classification approach with continuous wavelet transform using task-based FMRI. Sensors (Basel). 2021;21(17):5822. doi: 10.3390/s21175822 [DOI] [PMC free article] [PubMed] [Google Scholar]
131.Squarcina L, Nosari G, Marin R, et al. Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine. Brain Behav. 2021;11(8):e2238. doi: 10.1002/brb3.2238 [DOI] [PMC free article] [PubMed] [Google Scholar]
132.Baygin M, Dogan S, Tuncer T, et al. Automated ASD detection using hybrid deep lightweight features extracted from EEG signals. Comput Biol Med. 2021;134:104548. doi: 10.1016/j.compbiomed.2021.104548 [DOI] [PubMed] [Google Scholar]
133.Yang C, Wang P, Tan J, Liu Q, Li X. Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks. Comput Biol Med. 2021;139:104963. doi: 10.1016/j.compbiomed.2021.104963 [DOI] [PubMed] [Google Scholar]
134.ElNakieb Y, Ali MT, Elnakib A, et al. The role of diffusion tensor MR imaging (DTI) of the brain in diagnosing autism spectrum disorder: promising results. Sensors (Basel). 2021;21(24):8171. doi: 10.3390/s21248171 [DOI] [PMC free article] [PubMed] [Google Scholar]
135.Okamoto N, Akama H. Extended invariant information clustering is effective for leave-one-site-out cross-validation in resting state functional connectivity modeling. Front Neuroinform. 2021;15:709179. doi: 10.3389/fninf.2021.709179 [DOI] [PMC free article] [PubMed] [Google Scholar]
136.Liao M, Duan H, Wang G. Application of machine learning techniques to detect the children with autism spectrum disorder. J Healthc Eng. 2022;2022:9340027. doi: 10.1155/2022/9340027 [DOI] [PMC free article] [PubMed] [Google Scholar]
137.Wang N, Yao D, Ma L, Liu M. Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI. Med Image Anal. 2022;75:102279. doi: 10.1016/j.media.2021.102279 [DOI] [PubMed] [Google Scholar]
138.Guo X, Wang J, Wang X, et al. Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms. Eur Radiol. 2022;32(2):761-770. doi: 10.1007/s00330-021-08239-4 [DOI] [PubMed] [Google Scholar]
139.Gao K, Fan Z, Su J, et al. Deep transfer learning for cerebral cortex using area-preserving geometry mapping. Cereb Cortex. 2022;32(14):2972-2984. doi: 10.1093/cercor/bhab394 [DOI] [PubMed] [Google Scholar]
140.Zhao L, Sun YK, Xue SW, Luo H, Lu XD, Zhang LH. Identifying boys with autism spectrum disorder based on whole-brain resting-state interregional functional connections using a boruta-based support vector machine approach. Front Neuroinform. 2022;16:761942. doi: 10.3389/fninf.2022.761942 [DOI] [PMC free article] [PubMed] [Google Scholar]
141.Shao L, Fu C, You Y, Fu D. Classification of ASD based on fMRI data with deep learning. Cogn Neurodyn. 2021;15(6):961-974. doi: 10.1007/s11571-021-09683-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
142.Li L, Jiang H, Wen G, et al. TE-HI-GCN: an ensemble of transfer hierarchical graph convolutional networks for disorder diagnosis. Neuroinformatics. 2022;20(2):353-375. doi: 10.1007/s12021-021-09548-1 [DOI] [PubMed] [Google Scholar]
143.Germann J, Gouveia FV, Brentani H, et al. Involvement of the habenula in the pathophysiology of autism spectrum disorder. Sci Rep. 2021;11(1):21168. doi: 10.1038/s41598-021-00603-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
144.Kang J, Jin Y, Liang G, Li X. Accurate assessment of low-function autistic children based on EEG feature fusion. J Clin Neurosci. 2021;90:351-358. doi: 10.1016/j.jocn.2021.06.022 [DOI] [PubMed] [Google Scholar]
145.Peng L, Liu X, Ma D, Chen X, Xu X, Gao X. The altered pattern of the functional connectome related to pathological biomarkers in individuals for autism spectrum disorder identification. Front Neurosci. 2022;16:913377. doi: 10.3389/fnins.2022.913377 [DOI] [PMC free article] [PubMed] [Google Scholar]
146.Hu J, Cao L, Li T, Dong S, Li P. GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification. BMC Bioinformatics. 2021;22(1):379. doi: 10.1186/s12859-021-04295-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
147.Grossi E, Valbusa G, Buscema M. Detection of an autism EEG signature from only two EEG channels through features extraction and advanced machine learning analysis. Clin EEG Neurosci. 2021;52(5):330-337. doi: 10.1177/1550059420982424 [DOI] [PubMed] [Google Scholar]
148.Chen H, Chen W, Song Y, Sun L, Li X. EEG characteristics of children with attention-deficit/hyperactivity disorder. Neuroscience. 2019;406:444-456. doi: 10.1016/j.neuroscience.2019.03.048 [DOI] [PubMed] [Google Scholar]
149.Guo X, Yao D, Cao Q, et al. Shared and distinct resting functional connectivity in children and adults with attention-deficit/hyperactivity disorder. Transl Psychiatry. 2020;10(1):65. doi: 10.1038/s41398-020-0740-y [DOI] [PMC free article] [PubMed] [Google Scholar]
150.Müller A, Vetsch S, Pershin I, et al. EEG/ERP-based biomarker/neuroalgorithms in adults with ADHD: Development, reliability, and application in clinical practice. World J Biol Psychiatry. 2020;21(3):172-182. doi: 10.1080/15622975.2019.1605198 [DOI] [PubMed] [Google Scholar]
151.Gao MS, Tsai FS, Lee CC. Learning a phenotypic-attribute attentional brain connectivity embedding for ADHD classification using rs-fMRI. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:5472-5475. doi: 10.1109/EMBC44109.2020.9175789 [DOI] [PubMed] [Google Scholar]
152.Chen Y, Tang Y, Wang C, Liu X, Zhao L, Wang Z. ADHD classification by dual subspace learning using resting-state functional connectivity. Artif Intell Med. 2020;103:101786. doi: 10.1016/j.artmed.2019.101786 [DOI] [PubMed] [Google Scholar]
153.Muthuraman M, Moliadze V, Boecher L, et al. Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders. Sci Rep. 2019;9(1):20028. doi: 10.1038/s41598-019-56398-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
154.McNorgan C, Judson C, Handzlik D, Holden JG. Linking ADHD and behavioral assessment through identification of shared diagnostic task-based functional connections. Front Physiol. 2020;11:583005. doi: 10.3389/fphys.2020.583005 [DOI] [PMC free article] [PubMed] [Google Scholar]
155.Vahid A, Bluschke A, Roessner V, Stober S, Beste C. Deep learning based on event-related EEG differentiates children with ADHD from healthy controls. J Clin Med. 2019;8(7):1055. doi: 10.3390/jcm8071055 [DOI] [PMC free article] [PubMed] [Google Scholar]
156.Riaz A, Asad M, Alonso E, Slabaugh G. DeepFMRI: end-to-end deep learning for functional connectivity and classification of ADHD using fMRI. J Neurosci Methods. 2020;335:108506. doi: 10.1016/j.jneumeth.2019.108506 [DOI] [PubMed] [Google Scholar]
157.Rostami M, Farashi S, Khosrowabadi R, Pouretemad H. Discrimination of ADHD subtypes using decision tree on behavioral, neuropsychological, and neural markers. Basic Clin Neurosci. 2020;11(3):359-367. [DOI] [PMC free article] [PubMed] [Google Scholar]
158.Kiiski H, Rueda-Delgado LM, Bennett M, et al. Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms. Clin Neurophysiol. 2020;131(1):330-342. doi: 10.1016/j.clinph.2019.08.010 [DOI] [PubMed] [Google Scholar]
159.Tang Y, Wang C, Chen Y, Sun N, Jiang A, Wang Z. Identifying ADHD individuals from resting-state functional connectivity using subspace clustering and binary hypothesis testing. J Atten Disord. 2021;25(5):736-748. doi: 10.1177/1087054719837749 [DOI] [PubMed] [Google Scholar]
160.Sun Y, Zhao L, Lan Z, Jia XZ, Xue SW. Differentiating boys with ADHD from those with typical development based on whole-brain functional connections using a machine learning approach. Neuropsychiatr Dis Treat. 2020;16:691-702. doi: 10.2147/NDT.S239013 [DOI] [PMC free article] [PubMed] [Google Scholar]
161.Sidhu G. Locally linear embedding and fMRI feature selection in psychiatric classification. IEEE J Transl Eng Health Med. 2019;7:2200211. doi: 10.1109/JTEHM.2019.2936348 [DOI] [PMC free article] [PubMed] [Google Scholar]
162.Riaz A, Asad M, Alonso E, Slabaugh G. Fusion of fMRI and non-imaging data for ADHD classification. Comput Med Imaging Graph. 2018;65:115-128. doi: 10.1016/j.compmedimag.2017.10.002 [DOI] [PubMed] [Google Scholar]
163.Kaur S, Singh S, Arun P, Kaur D, Bajaj M. Phase space reconstruction of EEG signals for classification of ADHD and control adults. Clin EEG Neurosci. 2020;51(2):102-113. doi: 10.1177/1550059419876525 [DOI] [PubMed] [Google Scholar]
164.Chen M, Li H, Wang J, Dillman JR, Parikh NA, He L. A multichannel deep neural network model analyzing multiscale functional brain connectome data for attention deficit hyperactivity disorder detection. Radiol Artif Intell. 2019;2(1):e190012. doi: 10.1148/ryai.2019190012 [DOI] [PMC free article] [PubMed] [Google Scholar]
165.Sutoko S, Monden Y, Tokuda T, et al. Exploring attentive task-based connectivity for screening attention deficit/hyperactivity disorder children: a functional near-infrared spectroscopy study. Neurophotonics. 2019;6(4):045013. doi: 10.1117/1.NPh.6.4.045013 [DOI] [PMC free article] [PubMed] [Google Scholar]
166.Luo Y, Alvarez TL, Halperin JM, Li X. Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques. Neuroimage Clin. 2020;26:102238. doi: 10.1016/j.nicl.2020.102238 [DOI] [PMC free article] [PubMed] [Google Scholar]
167.Wang XH, Jiao Y, Li L. Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity. Neurosci Lett. 2018;685:30-34. doi: 10.1016/j.neulet.2018.07.029 [DOI] [PubMed] [Google Scholar]
168.Peng X, Lin P, Zhang T, Wang J. Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS One. 2013;8(11):e79476. doi: 10.1371/journal.pone.0079476 [DOI] [PMC free article] [PubMed] [Google Scholar]
169.Yasumura A, Omori M, Fukuda A, et al. Applied machine learning method to predict children with ADHD using prefrontal cortex activity: a multicenter study in Japan. J Atten Disord. 2020;24(14):2012-2020. doi: 10.1177/1087054717740632 [DOI] [PubMed] [Google Scholar]
170.Qureshi MNI, Oh J, Min B, Jo HJ, Lee B. Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain MRI. Front Hum Neurosci. 2017;11:157. [DOI] [PMC free article] [PubMed] [Google Scholar]
171.Biederman J, Hammerness P, Sadeh B, et al. Diagnostic utility of brain activity flow patterns analysis in attention deficit hyperactivity disorder. Psychol Med. 2017;47(7):1259-1270. doi: 10.1017/S0033291716003329 [DOI] [PubMed] [Google Scholar]
172.Gehricke JG, Kruggel F, Thampipop T, et al. The brain anatomy of attention-deficit/hyperactivity disorder in young adults: a magnetic resonance imaging study. PLoS One. 2017;12(4):e0175433. doi: 10.1371/journal.pone.0175433 [DOI] [PMC free article] [PubMed] [Google Scholar]
173.Sato JR, Hoexter MQ, Fujita A, Rohde LA. Evaluation of pattern recognition and feature extraction methods in ADHD prediction. Front Syst Neurosci. 2012;6:68. doi: 10.3389/fnsys.2012.00068 [DOI] [PMC free article] [PubMed] [Google Scholar]
174.Iannaccone R, Hauser TU, Ball J, Brandeis D, Walitza S, Brem S. Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging. Eur Child Adolesc Psychiatry. 2015;24(10):1279-1289. doi: 10.1007/s00787-015-0678-4 [DOI] [PubMed] [Google Scholar]
175.Gu Y, Miao S, Han J, et al. Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy. J Neural Eng. 2018;15(3):035005. doi: 10.1088/1741-2552/aa9ee9 [DOI] [PubMed] [Google Scholar]
176.Du J, Wang L, Jie B, Zhang D. Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA. Comput Med Imaging Graph. 2016;52:82-88. doi: 10.1016/j.compmedimag.2016.04.004 [DOI] [PubMed] [Google Scholar]
177.Wang XH, Jiao Y, Li L. Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity. Sci Rep. 2018;8(1):11789. doi: 10.1038/s41598-018-30308-w [DOI] [PMC free article] [PubMed] [Google Scholar]
178.Hart H, Chantiluke K, Cubillo AI, et al. Pattern classification of response inhibition in ADHD: toward the development of neurobiological markers for ADHD. Hum Brain Mapp. 2014;35(7):3083-3094. doi: 10.1002/hbm.22386 [DOI] [PMC free article] [PubMed] [Google Scholar]
179.Dai D, Wang J, Hua J, He H. Classification of ADHD children through multimodal magnetic resonance imaging. Front Syst Neurosci. 2012;6:63. doi: 10.3389/fnsys.2012.00063 [DOI] [PMC free article] [PubMed] [Google Scholar]
180.Liu S, Zhao L, Wang X, et al. Deep spatio-temporal representation and ensemble classification for attention deficit/hyperactivity disorder. IEEE Trans Neural Syst Rehabil Eng. 2021;29:1-10. doi: 10.1109/TNSRE.2020.3019063 [DOI] [PubMed] [Google Scholar]
181.Wang X, Jiao Y, Tang T, Wang H, Lu Z. Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder. Eur J Radiol. 2013;82(9):1552-1557. doi: 10.1016/j.ejrad.2013.04.009 [DOI] [PubMed] [Google Scholar]
182.Sidhu GS, Asgarian N, Greiner R, Brown MR. Kernel principal component analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Front Syst Neurosci. 2012;6:74. doi: 10.3389/fnsys.2012.00074 [DOI] [PMC free article] [PubMed] [Google Scholar]
183.Liechti MD, Valko L, Müller UC, et al. Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan. Brain Topogr. 2013;26(1):135-151. doi: 10.1007/s10548-012-0258-6 [DOI] [PubMed] [Google Scholar]
184.Zhu CZ, Zang YF, Cao QJ, et al. Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. Neuroimage. 2008;40(1):110-120. doi: 10.1016/j.neuroimage.2007.11.029 [DOI] [PubMed] [Google Scholar]
185.Olivetti E, Greiner S, Avesani P. ADHD diagnosis from multiple data sources with batch effects. Front Syst Neurosci. 2012;6:70. doi: 10.3389/fnsys.2012.00070 [DOI] [PMC free article] [PubMed] [Google Scholar]
186.Johnston BA, Mwangi B, Matthews K, Coghill D, Konrad K, Steele JD. Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification. Hum Brain Mapp. 2014;35(10):5179-5189. doi: 10.1002/hbm.22542 [DOI] [PMC free article] [PubMed] [Google Scholar]
187.Aradhya AMS, Subbaraju V, Sundaram S, Sundararajan N. Regularized Spatial Filtering Method (R-SFM) for detection of attention deficit hyperactivity disorder (ADHD) from resting-state functional magnetic resonance imaging (rs-fMRI). Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:5541-5544. doi: 10.1109/EMBC.2018.8513522 [DOI] [PubMed] [Google Scholar]
188.Chang CW, Ho CC, Chen JH. ADHD classification by a texture analysis of anatomical brain MRI data. Front Syst Neurosci. 2012;6:66. doi: 10.3389/fnsys.2012.00066 [DOI] [PMC free article] [PubMed] [Google Scholar]
189.Smith JL, Johnstone SJ, Barry RJ. Aiding diagnosis of attention-deficit/hyperactivity disorder and its subtypes: discriminant function analysis of event-related potential data. J Child Psychol Psychiatry. 2003;44(7):1067-1075. doi: 10.1111/1469-7610.00191 [DOI] [PubMed] [Google Scholar]
190.dos Santos Siqueira A, Biazoli Junior CE, Comfort WE, Rohde LA, Sato JR. Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data. Biomed Res Int. 2014;2014:380531. doi: 10.1155/2014/380531 [DOI] [PMC free article] [PubMed] [Google Scholar]
191.Sun H, Chen Y, Huang Q, et al. Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: a radiomics analysis. Radiology. 2018;287(2):620-630. doi: 10.1148/radiol.2017170226 [DOI] [PubMed] [Google Scholar]
192.Mueller A, Candrian G, Kropotov JD, Ponomarev VA, Baschera GM. Classification of ADHD patients on the basis of independent ERP components using a machine learning system. Nonlinear Biomed Phys. 2010;4(Suppl 1)(suppl 1):S1. doi: 10.1186/1753-4631-4-S1-S1 [DOI] [PMC free article] [PubMed] [Google Scholar]
193.Cheng W, Ji X, Zhang J, Feng J. Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques. Front Syst Neurosci. 2012;6:58. doi: 10.3389/fnsys.2012.00058 [DOI] [PMC free article] [PubMed] [Google Scholar]
194.Ahmadlou M, Adeli H. Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD. Clin EEG Neurosci. 2010;41(1):1-10. doi: 10.1177/155005941004100103 [DOI] [PubMed] [Google Scholar]
195.Abibullaev B, An J. Decision support algorithm for diagnosis of ADHD using electroencephalograms. J Med Syst. 2012;36(4):2675-2688. doi: 10.1007/s10916-011-9742-x [DOI] [PubMed] [Google Scholar]
196.Colby JB, Rudie JD, Brown JA, Douglas PK, Cohen MS, Shehzad Z. Insights into multimodal imaging classification of ADHD. Front Syst Neurosci. 2012;6:59. doi: 10.3389/fnsys.2012.00059 [DOI] [PMC free article] [PubMed] [Google Scholar]
197.Mueller A, Candrian G, Grane VA, Kropotov JD, Ponomarev VA, Baschera GM. Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study. Nonlinear Biomed Phys. 2011;5:5. doi: 10.1186/1753-4631-5-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
198.Yu D. Additional brain functional network in adults with attention-deficit/hyperactivity disorder: a phase synchrony analysis. PLoS One. 2013;8(1):e54516. doi: 10.1371/journal.pone.0054516 [DOI] [PMC free article] [PubMed] [Google Scholar]
199.Poil SS, Bollmann S, Ghisleni C, et al. Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD). Clin Neurophysiol. 2014;125(8):1626-1638. doi: 10.1016/j.clinph.2013.12.118 [DOI] [PubMed] [Google Scholar]
200.Qureshi MN, Min B, Jo HJ, Lee B. Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: structural MRI study. PLoS One. 2016;11(8):e0160697. doi: 10.1371/journal.pone.0160697 [DOI] [PMC free article] [PubMed] [Google Scholar]
201.Hart H, Marquand AF, Smith A, et al. Predictive neurofunctional markers of attention-deficit/hyperactivity disorder based on pattern classification of temporal processing. J Am Acad Child Adolesc Psychiatry. 2014;53(5):569-78.e1. doi: 10.1016/j.jaac.2013.12.024 [DOI] [PubMed] [Google Scholar]
202.Qureshi MNI, Lee B. Classification of ADHD subgroup with recursive feature elimination for structural brain MRI. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:5929-5932. doi: 10.1109/EMBC.2016.7592078 [DOI] [PubMed] [Google Scholar]
203.Deshpande G, Wang P, Rangaprakash D, Wilamowski B. Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Trans Cybern. 2015;45(12):2668-2679. doi: 10.1109/TCYB.2014.2379621 [DOI] [PubMed] [Google Scholar]
204.Hammer R, Cooke GE, Stein MA, Booth JR. Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder. Neuroimage Clin. 2015;9:244-252. doi: 10.1016/j.nicl.2015.08.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
205.Moghaddari M, Lighvan MZ, Danishvar S. Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. Comput Methods Programs Biomed. 2020;197:105738. doi: 10.1016/j.cmpb.2020.105738 [DOI] [PubMed] [Google Scholar]
206.Pereda E, García-Torres M, Melián-Batista B, Mañas S, Méndez L, González JJ. The blessing of dimensionality: feature selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation. PLoS One. 2018;13(8):e0201660. doi: 10.1371/journal.pone.0201660 [DOI] [PMC free article] [PubMed] [Google Scholar]
207.Lim L, Marquand A, Cubillo AA, et al. Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging. PLoS One. 2013;8(5):e63660. doi: 10.1371/journal.pone.0063660 [DOI] [PMC free article] [PubMed] [Google Scholar]
208.Öztoprak H, Toycan M, Alp YK, Arıkan O, Doğutepe E, Karakaş S. Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity. Clin Neurophysiol. 2017;128(12):2400-2410. doi: 10.1016/j.clinph.2017.09.105 [DOI] [PubMed] [Google Scholar]
209.Tor HT, Ooi CP, Lim-Ashworth NS, et al. Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals. Comput Methods Programs Biomed. 2021;200:105941. doi: 10.1016/j.cmpb.2021.105941 [DOI] [PubMed] [Google Scholar]
210.Tosun M. Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning. Phys Eng Sci Med. 2021;44(3):693-702. doi: 10.1007/s13246-021-01018-x [DOI] [PubMed] [Google Scholar]
211.Johnstone SJ, Parrish L, Jiang H, Zhang DW, Williams V, Li S. Aiding diagnosis of childhood attention-deficit/hyperactivity disorder of the inattentive presentation: discriminant function analysis of multi-domain measures including EEG. Biol Psychol. 2021;161:108080. doi: 10.1016/j.biopsycho.2021.108080 [DOI] [PubMed] [Google Scholar]
212.Dey S, Rao AR, Shah M. Exploiting the brain’s network structure in identifying ADHD subjects. Front Syst Neurosci. 2012;6:75. doi: 10.3389/fnsys.2012.00075 [DOI] [PMC free article] [PubMed] [Google Scholar]
213.Yoo JH, Kim JI, Kim BN, Jeong B. Exploring characteristic features of attention-deficit/hyperactivity disorder: findings from multi-modal MRI and candidate genetic data. Brain Imaging Behav. 2020;14(6):2132-2147. doi: 10.1007/s11682-019-00164-x [DOI] [PubMed] [Google Scholar]
214.Tenev A, Markovska-Simoska S, Kocarev L, Pop-Jordanov J, Müller A, Candrian G. Machine learning approach for classification of ADHD adults. Int J Psychophysiol. 2014;93(1):162-166. doi: 10.1016/j.ijpsycho.2013.01.008 [DOI] [PubMed] [Google Scholar]
215.Rezaeezadeh M, Shamekhi S, Shamsi M. Attention deficit hyperactivity disorder diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study. Phys Eng Sci Med. 2020;43(2):577-592. doi: 10.1007/s13246-020-00858-3 [DOI] [PubMed] [Google Scholar]
216.Crippa A, Salvatore C, Molteni E, et al. The utility of a computerized algorithm based on a multi-domain profile of measures for the diagnosis of attention deficit/hyperactivity disorder. Front Psychiatry. 2017;8:189. doi: 10.3389/fpsyt.2017.00189 [DOI] [PMC free article] [PubMed] [Google Scholar]
217.Ishii-Takahashi A, Takizawa R, Nishimura Y, et al. Prefrontal activation during inhibitory control measured by near-infrared spectroscopy for differentiating between autism spectrum disorders and attention deficit hyperactivity disorder in adults. Neuroimage Clin. 2013;4:53-63. doi: 10.1016/j.nicl.2013.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
218.Zhang T, Li C, Li P, et al. Separated channel attention convolutional neural network (SC-CNN-Attention) to identify ADHD in multi-site Rs-fMRI dataset. Entropy (Basel). 2020;22(8):893. doi: 10.3390/e22080893 [DOI] [PMC free article] [PubMed] [Google Scholar]
219.Abramov DM, Lazarev VV, Gomes Junior SC, et al. Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples. PeerJ. 2019;7:e7074. doi: 10.7717/peerj.7074 [DOI] [PMC free article] [PubMed] [Google Scholar]
220.Dey S, Rao AR, Shah M. Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects. Front Neural Circuits. 2014;8:64. doi: 10.3389/fncir.2014.00064 [DOI] [PMC free article] [PubMed] [Google Scholar]
221.Helgadóttir H, Gudmundsson ÓÓ, Baldursson G, et al. Electroencephalography as a clinical tool for diagnosing and monitoring attention deficit hyperactivity disorder: a cross-sectional study. BMJ Open. 2015;5(1):e005500. doi: 10.1136/bmjopen-2014-005500 [DOI] [PMC free article] [PubMed] [Google Scholar]
222.Chow JC, Ouyang CS, Tsai CL, et al. Entropy-based quantitative electroencephalogram analysis for diagnosing attention-deficit hyperactivity disorder in girls. Clin EEG Neurosci. 2019;50(3):172-179. doi: 10.1177/1550059418814983 [DOI] [PubMed] [Google Scholar]
223.Chen H, Song Y, Li X. Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD. J Neural Eng. 2019;16(6):066046. doi: 10.1088/1741-2552/ab3a0a [DOI] [PubMed] [Google Scholar]
224.Jahanshahloo HR, Shamsi M, Ghasemi E, Kouhi A. Automated and ERP-based diagnosis of attention-deficit hyperactivity disorder in children. J Med Signals Sens. 2017;7(1):26-32. doi: 10.4103/2228-7477.199152 [DOI] [PMC free article] [PubMed] [Google Scholar]
225.Wolfers T, van Rooij D, Oosterlaan J, et al. Quantifying patterns of brain activity: distinguishing unaffected siblings from participants with ADHD and healthy individuals. Neuroimage Clin. 2016;12:227-233. doi: 10.1016/j.nicl.2016.06.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
226.Shao L, You Y, Du H, Fu D. Classification of ADHD with fMRI data and multi-objective optimization. Comput Methods Programs Biomed. 2020;196:105676. doi: 10.1016/j.cmpb.2020.105676 [DOI] [PubMed] [Google Scholar]
227.Itani S, Rossignol M, Lecron F, Fortemps P. Towards interpretable machine learning models for diagnosis aid: a case study on attention deficit/hyperactivity disorder. PLoS One. 2019;14(4):e0215720. doi: 10.1371/journal.pone.0215720 [DOI] [PMC free article] [PubMed] [Google Scholar]
228.Khoshnoud S, Nazari MA, Shamsi M. Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals. J Integr Neurosci. 2018;17(1):11-17. doi: 10.3233/JIN-170033 [DOI] [PubMed] [Google Scholar]
229.Chikara RK, Ko LW. Neural activities classification of human inhibitory control using hierarchical model. Sensors (Basel). 2019;19(17):3791. doi: 10.3390/s19173791 [DOI] [PMC free article] [PubMed] [Google Scholar]
230.Zhou X, Lin Q, Gui Y, Wang Z, Liu M, Lu H. Multimodal MR images-based diagnosis of early adolescent attention-deficit/hyperactivity disorder using multiple kernel learning. Front Neurosci. 2021;15:710133. doi: 10.3389/fnins.2021.710133 [DOI] [PMC free article] [PubMed] [Google Scholar]
231.Zhou D, Liao Z, Chen R. Deep learning enabled diagnosis of children’s ADHD based on the big data of video screen long-range EEG. J Healthc Eng. 2022;2022:5222136. doi: 10.1155/2022/5222136 [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
232.Tang Y, Sun J, Wang C, et al. ADHD classification using auto-encoding neural network and binary hypothesis testing. Artif Intell Med. 2022;123:102209. doi: 10.1016/j.artmed.2021.102209 [DOI] [PubMed] [Google Scholar]
233.Catherine Joy R, Thomas George S, Albert Rajan A, Subathra MSP. Detection of ADHD from EEG signals using different entropy measures and ANN. Clin EEG Neurosci. 2022;53(1):12-23. doi: 10.1177/15500594211036788 [DOI] [PubMed] [Google Scholar]
234.Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y. A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage. 2022;246:118774. doi: 10.1016/j.neuroimage.2021.118774 [DOI] [PMC free article] [PubMed] [Google Scholar]
235.Wang Z, Zhu Y, Shi H, Zhang Y, Yan C. A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images. Math Biosci Eng. 2021;18(5):6978-6994. doi: 10.3934/mbe.2021347 [DOI] [PubMed] [Google Scholar]
236.Dinkel PJ, Willmes K, Krinzinger H, Konrad K, Koten JW Jr. Diagnosing developmental dyscalculia on the basis of reliable single case FMRI methods: promises and limitations. PLoS One. 2013;8(12):e83722. doi: 10.1371/journal.pone.0083722 [DOI] [PMC free article] [PubMed] [Google Scholar]
237.Zahia S, Garcia-Zapirain B, Saralegui I, Fernandez-Ruanova B. Dyslexia detection using 3D convolutional neural networks and functional magnetic resonance imaging. Comput Methods Programs Biomed. 2020;197:105726. doi: 10.1016/j.cmpb.2020.105726 [DOI] [PubMed] [Google Scholar]
238.Płoński P, Gradkowski W, Altarelli I, et al. Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia. Hum Brain Mapp. 2017;38(2):900-908. doi: 10.1002/hbm.23426 [DOI] [PMC free article] [PubMed] [Google Scholar]
239.Martinez-Murcia FJ, Ortiz A, Gorriz JM, et al. EEG connectivity analysis using denoising autoencoders for the detection of dyslexia. Int J Neural Syst. 2020;30(7):2050037. doi: 10.1142/S0129065720500379 [DOI] [PubMed] [Google Scholar]
240.Zainuddin AZA, Mansor W, Lee KY, Mahmoodin Z. Comparison of extreme learning machine and K-nearest neighbour performance in classifying EEG signal of normal, poor and capable dyslexic children. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:4513-4516. doi: 10.1109/EMBC.2019.8857569 [DOI] [PubMed] [Google Scholar]
241.Serrallach B, Groß C, Bernhofs V, et al. Neural biomarkers for dyslexia, ADHD, and ADD in the auditory cortex of children. Front Neurosci. 2016;10:324. doi: 10.3389/fnins.2016.00324 [DOI] [PMC free article] [PubMed] [Google Scholar]
242.Cui Z, Xia Z, Su M, Shu H, Gong G. Disrupted white matter connectivity underlying developmental dyslexia: a machine learning approach. Hum Brain Mapp. 2016;37(4):1443-1458. doi: 10.1002/hbm.23112 [DOI] [PMC free article] [PubMed] [Google Scholar]
243.García Chimeno Y, García Zapirain B, Saralegui Prieto I, Fernandez-Ruanova B. Automatic classification of dyslexic children by applying machine learning to fMRI images. Biomed Mater Eng. 2014;24(6):2995-3002. doi: 10.3233/BME-141120 [DOI] [PubMed] [Google Scholar]
244.Bailey S, Hoeft F, Aboud K, Cutting L. Anomalous gray matter patterns in specific reading comprehension deficit are independent of dyslexia. Ann Dyslexia. 2016;66(3):256-274. doi: 10.1007/s11881-015-0114-y [DOI] [PMC free article] [PubMed] [Google Scholar]
245.Usman OL, Muniyandi RC, Omar K, Mohamad M. Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism. PLoS One. 2021;16(2):e0245579. doi: 10.1371/journal.pone.0245579 [DOI] [PMC free article] [PubMed] [Google Scholar]
246.Mascheretti S, Peruzzo D, Andreola C, et al. Selecting the most relevant brain regions to classify children with developmental dyslexia and typical readers by using complex magnocellular stimuli and multiple kernel learning. Brain Sci. 2021;11(6):722. doi: 10.3390/brainsci11060722 [DOI] [PMC free article] [PubMed] [Google Scholar]
247.Li A, Zalesky A, Yue W, et al. A neuroimaging biomarker for striatal dysfunction in schizophrenia. Nat Med. 2020;26(4):558-565. doi: 10.1038/s41591-020-0793-8 [DOI] [PubMed] [Google Scholar]
248.Jo YT, Joo SW, Shon SH, Kim H, Kim Y, Lee J. Diagnosing schizophrenia with network analysis and a machine learning method. Int J Methods Psychiatr Res. 2020;29(1):e1818. doi: 10.1002/mpr.1818 [DOI] [PMC free article] [PubMed] [Google Scholar]
249.Yassin W, Nakatani H, Zhu Y, et al. Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis. Transl Psychiatry. 2020;10(1):278. doi: 10.1038/s41398-020-00965-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
250.Chen Z, Yan T, Wang E, et al. Detecting abnormal brain regions in schizophrenia using structural MRI via machine learning. Comput Intell Neurosci. 2020;2020:6405930. doi: 10.1155/2020/6405930 [DOI] [PMC free article] [PubMed] [Google Scholar]
251.Jahmunah V, Lih Oh S, Rajinikanth V, et al. Automated detection of schizophrenia using nonlinear signal processing methods. Artif Intell Med. 2019;100:101698. doi: 10.1016/j.artmed.2019.07.006 [DOI] [PubMed] [Google Scholar]
252.Wang L, Li X, Zhu Y, et al. Discriminative analysis of symptom severity and ultra-high risk of schizophrenia using intrinsic functional connectivity. Int J Neural Syst. 2020;30(9):2050047. doi: 10.1142/S0129065720500471 [DOI] [PubMed] [Google Scholar]
253.Mikolas P, Hlinka J, Skoch A, et al. Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy. BMC Psychiatry. 2018;18(1):97. doi: 10.1186/s12888-018-1678-y [DOI] [PMC free article] [PubMed] [Google Scholar]
254.Schwarz E, Doan NT, Pergola G, et al. ; IMAGEMEND Consortium, Karolinska Schizophrenia Project (KaSP) Consortium . Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Transl Psychiatry. 2019;9(1):12. doi: 10.1038/s41398-018-0225-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
255.Baradits M, Bitter I, Czobor P. Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls. Psychiatry Res. 2020;288:112938. doi: 10.1016/j.psychres.2020.112938 [DOI] [PubMed] [Google Scholar]
256.Kim J, Kim MY, Kwon H, et al. Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography. J Neurosci Methods. 2020;338:108688. doi: 10.1016/j.jneumeth.2020.108688 [DOI] [PubMed] [Google Scholar]
257.de Moura AM, Pinaya WHL, Gadelha A, et al. Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach. Psychiatry Res Neuroimaging. 2018;275:14-20. doi: 10.1016/j.pscychresns.2018.03.003 [DOI] [PubMed] [Google Scholar]
258.Yamamoto M, Bagarinao E, Kushima I, et al. Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites. PLoS One. 2020;15(11):e0239615. doi: 10.1371/journal.pone.0239615 [DOI] [PMC free article] [PubMed] [Google Scholar]
259.Zou H, Yang J. Dynamic thresholding networks for schizophrenia diagnosis. Artif Intell Med. 2019;96:25-32. doi: 10.1016/j.artmed.2019.03.007 [DOI] [PubMed] [Google Scholar]
260.Rozycki M, Satterthwaite TD, Koutsouleris N, et al. Multisite machine learning analysis provides a robust structural imaging signature of schizophrenia detectable across diverse patient populations and within individuals. Schizophr Bull. 2018;44(5):1035-1044. doi: 10.1093/schbul/sbx137 [DOI] [PMC free article] [PubMed] [Google Scholar]
261.Liang S, Deng W, Li X, et al. Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: a machine learning study. Schizophr Res. 2020;220:187-193. doi: 10.1016/j.schres.2020.03.022 [DOI] [PubMed] [Google Scholar]
262.Alamian G, Pascarella A, Lajnef T, et al. Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia. Neuroimage Clin. 2020;28:102485. doi: 10.1016/j.nicl.2020.102485 [DOI] [PMC free article] [PubMed] [Google Scholar]
263.de Pierrefeu A, Löfstedt T, Laidi C, et al. Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity. Acta Psychiatr Scand. 2018;138(6):571-580. doi: 10.1111/acps.12964 [DOI] [PubMed] [Google Scholar]
264.Di Carlo P, Pergola G, Antonucci LA, et al. Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals. Psychol Med. 2020;50(9):1501-1509. doi: 10.1017/S0033291719001430 [DOI] [PubMed] [Google Scholar]
265.Bae Y, Kumarasamy K, Ali IM, Korfiatis P, Akkus Z, Erickson BJ. Differences between schizophrenic and normal subjects using network properties from fMRI. J Digit Imaging. 2018;31(2):252-261. doi: 10.1007/s10278-017-0020-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
266.Yu Y, Shen H, Zeng LL, Ma Q, Hu D. Convergent and divergent functional connectivity patterns in schizophrenia and depression. PLoS One. 2013;8(7):e68250. doi: 10.1371/journal.pone.0068250 [DOI] [PMC free article] [PubMed] [Google Scholar]
267.Dluhoš P, Schwarz D, Cahn W, et al. Multi-center machine learning in imaging psychiatry: a meta-model approach. Neuroimage. 2017;155:10-24. doi: 10.1016/j.neuroimage.2017.03.027 [DOI] [PubMed] [Google Scholar]
268.Lee J, Chon MW, Kim H, et al. Diagnostic value of structural and diffusion imaging measures in schizophrenia. Neuroimage Clin. 2018;18:467-474. doi: 10.1016/j.nicl.2018.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
269.Antonucci LA, Penzel N, Pergola G, et al. Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity. Neuropsychopharmacology. 2020;45(4):613-621. doi: 10.1038/s41386-019-0532-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
270.Lu X, Yang Y, Wu F, et al. Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images. Medicine (Baltimore). 2016;95(30):e3973. doi: 10.1097/MD.0000000000003973 [DOI] [PMC free article] [PubMed] [Google Scholar]
271.Chyzhyk D, Graña M, Öngür D, Shinn AK. Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI. Int J Neural Syst. 2015;25(3):1550007. doi: 10.1142/S0129065715500070 [DOI] [PMC free article] [PubMed] [Google Scholar]
272.Jing R, Li P, Ding Z, et al. Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients. Hum Brain Mapp. 2019;40(13):3930-3939. doi: 10.1002/hbm.24678 [DOI] [PMC free article] [PubMed] [Google Scholar]
273.Hua M, Peng Y, Zhou Y, Qin W, Yu C, Liang M. Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2020;99:109837. doi: 10.1016/j.pnpbp.2019.109837 [DOI] [PubMed] [Google Scholar]
274.Gould IC, Shepherd AM, Laurens KR, Cairns MJ, Carr VJ, Green MJ. Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: a support vector machine learning approach. Neuroimage Clin. 2014;6:229-236. doi: 10.1016/j.nicl.2014.09.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
275.Song H, Chen L, Gao R, et al. Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM. BMC Med Inform Decis Mak. 2017;17(suppl 3):166. doi: 10.1186/s12911-017-0559-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
276.Zhu Q, Huang J, Xu X. Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI. Biomed Eng Online. 2018;17(1):32. doi: 10.1186/s12938-018-0464-x [DOI] [PMC free article] [PubMed] [Google Scholar]
277.Zhao W, Guo S, Linli Z, Yang AC, Lin CP, Tsai SJ. Functional, anatomical, and morphological networks highlight the role of basal ganglia-thalamus-cortex circuits in schizophrenia. Schizophr Bull. 2020;46(2):422-431. [DOI] [PMC free article] [PubMed] [Google Scholar]
278.Chin R, You AX, Meng F, Zhou J, Sim K. Recognition of schizophrenia with regularized support vector machine and sequential region of interest selection using structural magnetic resonance imaging. Sci Rep. 2018;8(1):13858. doi: 10.1038/s41598-018-32290-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
279.Iwabuchi SJ, Palaniyappan L. Abnormalities in the effective connectivity of visuothalamic circuitry in schizophrenia. Psychol Med. 2017;47(7):1300-1310. doi: 10.1017/S0033291716003469 [DOI] [PubMed] [Google Scholar]
280.Winterburn JL, Voineskos AN, Devenyi GA, et al. Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? a multi-method and multi-dataset study. Schizophr Res. 2019;214:3-10. doi: 10.1016/j.schres.2017.11.038 [DOI] [PubMed] [Google Scholar]
281.Chen H, Uddin LQ, Duan X, et al. Shared atypical default mode and salience network functional connectivity between autism and schizophrenia. Autism Res. 2017;10(11):1776-1786. doi: 10.1002/aur.1834 [DOI] [PMC free article] [PubMed] [Google Scholar]
282.Xiao Y, Yan Z, Zhao Y, et al. Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI. Schizophr Res. 2019;214:11-17. doi: 10.1016/j.schres.2017.11.037 [DOI] [PubMed] [Google Scholar]
283.Zeng LL, Wang H, Hu P, et al. Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine. 2018;30:74-85. doi: 10.1016/j.ebiom.2018.03.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
284.Supekar K, Cai W, Krishnadas R, Palaniyappan L, Menon V. Dysregulated brain dynamics in a triple-network saliency model of schizophrenia and its relation to psychosis. Biol Psychiatry. 2019;85(1):60-69. doi: 10.1016/j.biopsych.2018.07.020 [DOI] [PubMed] [Google Scholar]
285.Mastrovito D, Hanson C, Hanson SJ. Differences in atypical resting-state effective connectivity distinguish autism from schizophrenia. Neuroimage Clin. 2018;18:367-376. doi: 10.1016/j.nicl.2018.01.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
286.Liu Y, Zhang Y, Lv L, Wu R, Zhao J, Guo W. Abnormal neural activity as a potential biomarker for drug-naive first-episode adolescent-onset schizophrenia with coherence regional homogeneity and support vector machine analyses. Schizophr Res. 2018;192:408-415. doi: 10.1016/j.schres.2017.04.028 [DOI] [PubMed] [Google Scholar]
287.Cui LB, Liu L, Wang HN, et al. Disease definition for schizophrenia by functional connectivity using radiomics strategy. Schizophr Bull. 2018;44(5):1053-1059. doi: 10.1093/schbul/sby007 [DOI] [PMC free article] [PubMed] [Google Scholar]
288.Pinaya WHL, Mechelli A, Sato JR. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: a large-scale multi-sample study. Hum Brain Mapp. 2019;40(3):944-954. doi: 10.1002/hbm.24423 [DOI] [PMC free article] [PubMed] [Google Scholar]
289.Chen X, Liu C, He H, et al. Transdiagnostic differences in the resting-state functional connectivity of the prefrontal cortex in depression and schizophrenia. J Affect Disord. 2017;217:118-124. doi: 10.1016/j.jad.2017.04.001 [DOI] [PubMed] [Google Scholar]
290.Schnack HG, Nieuwenhuis M, van Haren NE, et al. Can structural MRI aid in clinical classification? a machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. Neuroimage. 2014;84:299-306. doi: 10.1016/j.neuroimage.2013.08.053 [DOI] [PubMed] [Google Scholar]
291.Cheng H, Newman S, Goñi J, et al. Nodal centrality of functional network in the differentiation of schizophrenia. Schizophr Res. 2015;168(1-2):345-352. doi: 10.1016/j.schres.2015.08.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
292.Mikolas P, Melicher T, Skoch A, et al. Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study. Psychol Med. 2016;46(13):2695-2704. doi: 10.1017/S0033291716000878 [DOI] [PubMed] [Google Scholar]
293.Venkataraman A, Whitford TJ, Westin CF, Golland P, Kubicki M. Whole brain resting state functional connectivity abnormalities in schizophrenia. Schizophr Res. 2012;139(1-3):7-12. doi: 10.1016/j.schres.2012.04.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
294.Wang S, Zhang Y, Lv L, et al. Abnormal regional homogeneity as a potential imaging biomarker for adolescent-onset schizophrenia: a resting-state fMRI study and support vector machine analysis. Schizophr Res. 2018;192:179-184. doi: 10.1016/j.schres.2017.05.038 [DOI] [PubMed] [Google Scholar]
295.Cabral C, Kambeitz-Ilankovic L, Kambeitz J, et al. Classifying schizophrenia using multimodal multivariate pattern recognition analysis: evaluating the impact of individual clinical profiles on the neurodiagnostic performance. Schizophr Bull. 2016;42(Suppl 1)(suppl 1):S110-S117. doi: 10.1093/schbul/sbw053 [DOI] [PMC free article] [PubMed] [Google Scholar]
296.Ebdrup BH, Axelsen MC, Bak N, et al. Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naive schizophrenia patients. Psychol Med. 2019;49(16):2754-2763. doi: 10.1017/S0033291718003781 [DOI] [PMC free article] [PubMed] [Google Scholar]
297.Pinaya WH, Gadelha A, Doyle OM, et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci Rep. 2016;6:38897. doi: 10.1038/srep38897 [DOI] [PMC free article] [PubMed] [Google Scholar]
298.Kim J, Calhoun VD, Shim E, Lee JH. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage. 2016;124(Pt A):127-146. doi: 10.1016/j.neuroimage.2015.05.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
299.Viviano JD, Buchanan RW, Calarco N, et al. ; Social Processes Initiative in Neurobiology of the Schizophrenia(s) Group . Resting-state connectivity biomarkers of cognitive performance and social function in individuals with schizophrenia spectrum disorder and healthy control subjects. Biol Psychiatry. 2018;84(9):665-674. doi: 10.1016/j.biopsych.2018.03.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
300.Arbabshirani MR, Castro E, Calhoun VD. Accurate classification of schizophrenia patients based on novel resting-state fMRI features. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:6691-6694. doi: 10.1109/EMBC.2014.6945163 [DOI] [PubMed] [Google Scholar]
301.Yan W, Calhoun V, Song M, et al. Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data. EBioMedicine. 2019;47:543-552. doi: 10.1016/j.ebiom.2019.08.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
302.Orban P, Dansereau C, Desbois L, et al. Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity. Schizophr Res. 2018;192:167-171. doi: 10.1016/j.schres.2017.05.027 [DOI] [PubMed] [Google Scholar]
303.Yu Y, Shen H, Zhang H, Zeng LL, Xue Z, Hu D. Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern analysis of resting-state fMRI from schizophrenic patients and their healthy siblings. Biomed Eng Online. 2013;12:10. doi: 10.1186/1475-925X-12-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
304.Dillon K, Calhoun V, Wang YP. A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI. J Neurosci Methods. 2017;276:46-55. doi: 10.1016/j.jneumeth.2016.11.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
305.Pergola G, Trizio S, Di Carlo P, et al. Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia. Schizophr Res. 2017;180:13-20. doi: 10.1016/j.schres.2016.07.005 [DOI] [PubMed] [Google Scholar]
306.Shen H, Wang L, Liu Y, Hu D. Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage. 2010;49(4):3110-3121. doi: 10.1016/j.neuroimage.2009.11.011 [DOI] [PubMed] [Google Scholar]
307.Chu WL, Huang MW, Jian BL, Hsu CY, Cheng KS. A correlative classification study of schizophrenic patients with results of clinical evaluation and structural magnetic resonance images. Behav Neurol. 2016;2016:7849526. doi: 10.1155/2016/7849526 [DOI] [PMC free article] [PubMed] [Google Scholar]
308.Masychev K, Ciprian C, Ravan M, Reilly JP, MacCrimmon D. Advanced signal processing methods for characterization of schizophrenia. IEEE Trans Biomed Eng. 2021;68(4):1123-1130. doi: 10.1109/TBME.2020.3011842 [DOI] [PubMed] [Google Scholar]
309.Korda AI, Ruef A, Neufang S, et al. Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions. Psychiatry Res Neuroimaging. 2021;313:111303. doi: 10.1016/j.pscychresns.2021.111303 [DOI] [PMC free article] [PubMed] [Google Scholar]
310.Pina-Camacho L, Garcia-Prieto J, Parellada M, et al. Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model. Eur Child Adolesc Psychiatry. 2015;24(4):427-440. doi: 10.1007/s00787-014-0593-0 [DOI] [PubMed] [Google Scholar]
311.Castro E, Martínez-Ramón M, Pearlson G, Sui J, Calhoun VD. Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia. Neuroimage. 2011;58(2):526-536. doi: 10.1016/j.neuroimage.2011.06.044 [DOI] [PMC free article] [PubMed] [Google Scholar]
312.Koch SP, Hägele C, Haynes JD, Heinz A, Schlagenhauf F, Sterzer P. Diagnostic classification of schizophrenia patients on the basis of regional reward-related FMRI signal patterns. PLoS One. 2015;10(3):e0119089. doi: 10.1371/journal.pone.0119089 [DOI] [PMC free article] [PubMed] [Google Scholar]
313.Castro E, Gupta CN, Martínez-Ramón M, Calhoun VD, Arbabshirani MR, Turner J. Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:1513-1516. doi: 10.1109/EMBC.2014.6943889 [DOI] [PMC free article] [PubMed] [Google Scholar]
314.Arribas JI, Calhoun VD, Adali T. Automatic bayesian classification of healthy controls, bipolar disorder, and schizophrenia using intrinsic connectivity maps from fMRI data. IEEE Trans Biomed Eng. 2010;57(12):2850-2860. doi: 10.1109/TBME.2010.2080679 [DOI] [PMC free article] [PubMed] [Google Scholar]
315.Yoon JH, Nguyen DV, McVay LM, et al. Automated classification of fMRI during cognitive control identifies more severely disorganized subjects with schizophrenia. Schizophr Res. 2012;135(1-3):28-33. doi: 10.1016/j.schres.2012.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
316.Chyzhyk D, Savio A, Graña M. Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Netw. 2015;68:23-33. doi: 10.1016/j.neunet.2015.04.002 [DOI] [PubMed] [Google Scholar]
317.Wang P, Verma R. On classifying disease-induced patterns in the brain using diffusion tensor images. In: Metaxas D, Axel L, Fichtinger G, Székely G., eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science. Vol 5241. Springer; 2008:908-916. [DOI] [PubMed] [Google Scholar]
318.Ince NF, Goksu F, Pellizzer G, Tewfik A, Stephane M. Selection of spectro-temporal patterns in multichannel MEG with support vector machines for schizophrenia classification. Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:3554-3557. doi: 10.1109/IEMBS.2008.4649973 [DOI] [PubMed] [Google Scholar]
319.Sveinsson JR, Benediktsson JA, Stefansson SB, Davidsson K. Parallel principal component neural networks for classification of event-related potential waveforms. Med Eng Phys. 1997;19(1):15-20. doi: 10.1016/S1350-4533(96)00035-5 [DOI] [PubMed] [Google Scholar]
320.Calhas D, Romero E, Henriques R. On the use of pairwise distance learning for brain signal classification with limited observations. Artif Intell Med. 2020;105:101852. doi: 10.1016/j.artmed.2020.101852 [DOI] [PubMed] [Google Scholar]
321.Calhoun VD, Maciejewski PK, Pearlson GD, Kiehl KA. Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder. Hum Brain Mapp. 2008;29(11):1265-1275. doi: 10.1002/hbm.20463 [DOI] [PMC free article] [PubMed] [Google Scholar]
322.Neuhaus AH, Popescu FC, Bates JA, Goldberg TE, Malhotra AK. Single-subject classification of schizophrenia using event-related potentials obtained during auditory and visual oddball paradigms. Eur Arch Psychiatry Clin Neurosci. 2013;263(3):241-247. doi: 10.1007/s00406-012-0326-7 [DOI] [PubMed] [Google Scholar]
323.Xu T, Stephane M, Parhi KK. Abnormal neural oscillations in schizophrenia assessed by spectral power ratio of MEG during word processing. IEEE Trans Neural Syst Rehabil Eng. 2016;24(11):1148-1158. doi: 10.1109/TNSRE.2016.2551700 [DOI] [PubMed] [Google Scholar]
324.Ravan M, MacCrimmon D, Hasey G, Reilly JP, Khodayari-Rostamabad A. A machine learning approach using P300 responses to investigate effect of clozapine therapy. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5911-5914. doi: 10.1109/EMBC.2012.6347339 [DOI] [PubMed] [Google Scholar]
325.Castellani U, Rossato E, Murino V, et al. Classification of schizophrenia using feature-based morphometry. J Neural Transm (Vienna). 2012;119(3):395-404. doi: 10.1007/s00702-011-0693-7 [DOI] [PubMed] [Google Scholar]
326.Khare S, Bajaj V, Siuly S, Sinha PG. Classification of schizophrenia patients through empirical wavelet transformation using electroencephalogram signals. In: Bajaj V, Sinha GR, eds. Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1. IOP Publishing Ltd; 2020:1-26. [Google Scholar]
327.Yan W, Zhao M, Fu Z, Pearlson GD, Sui J, Calhoun VD. Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: a deep classification and clustering framework using fMRI time series. Schizophr Res. 2022;245:141-150. doi: 10.1016/j.schres.2021.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
328.Du Y, Hao H, Wang S, Pearlson GD, Calhoun VD. Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis. Neuroimage Clin. 2020;27:102284. doi: 10.1016/j.nicl.2020.102284 [DOI] [PMC free article] [PubMed] [Google Scholar]
329.Oh K, Kim W, Shen G, et al. Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization. Schizophr Res. 2019;212:186-195. doi: 10.1016/j.schres.2019.07.034 [DOI] [PubMed] [Google Scholar]
330.Guo Y, Qiu J, Lu W. Support vector machine-based schizophrenia classification using morphological information from amygdaloid and hippocampal subregions. Brain Sci. 2020;10(8):562. doi: 10.3390/brainsci10080562 [DOI] [PMC free article] [PubMed] [Google Scholar]
331.Qureshi MNI, Oh J, Cho D, Jo HJ, Lee B. Multimodal discrimination of schizophrenia using hybrid weighted feature concatenation of brain functional connectivity and anatomical features with an extreme learning machine. Front Neuroinform. 2017;11:59. doi: 10.3389/fninf.2017.00059 [DOI] [PMC free article] [PubMed] [Google Scholar]
332.Gallos IK, Gkiatis K, Matsopoulos GK, Siettos C. ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with schizophrenia. AIMS Neurosci. 2021;8(2):295-321. doi: 10.3934/Neuroscience.2021016 [DOI] [PMC free article] [PubMed] [Google Scholar]
333.Wang T, Bezerianos A, Cichocki A, Li J. Multikernel capsule network for schizophrenia identification. IEEE Trans Cybern. 2022;52(6):4741-4750. doi: 10.1109/TCYB.2020.3035282 [DOI] [PubMed] [Google Scholar]
334.Zang J, Huang Y, Kong L, et al. Effects of brain atlases and machine learning methods on the discrimination of schizophrenia patients: a multimodal MRI study. Front Neurosci. 2021;15:697168. doi: 10.3389/fnins.2021.697168 [DOI] [PMC free article] [PubMed] [Google Scholar]
335.Hu M, Qian X, Liu S, et al. Structural and diffusion MRI based schizophrenia classification using 2D pretrained and 3D naive convolutional neural networks. Schizophr Res. 2022;243:330-341. doi: 10.1016/j.schres.2021.06.011 [DOI] [PubMed] [Google Scholar]
336.Kim K, Duc NT, Choi M, Lee B. EEG microstate features for schizophrenia classification. PLoS One. 2021;16(5):e0251842. doi: 10.1371/journal.pone.0251842 [DOI] [PMC free article] [PubMed] [Google Scholar]
337.Salvador R, Canales-Rodríguez E, Guerrero-Pedraza A, et al. Multimodal integration of brain images for MRI-based diagnosis in schizophrenia. Front Neurosci. 2019;13:1203. doi: 10.3389/fnins.2019.01203 [DOI] [PMC free article] [PubMed] [Google Scholar]
338.Chou PH, Yao YH, Zheng RX, et al. Deep neural network to differentiate brain activity between patients with first-episode schizophrenia and healthy individuals: a multi-channel near infrared spectroscopy study. Front Psychiatry. 2021;12:655292. doi: 10.3389/fpsyt.2021.655292 [DOI] [PMC free article] [PubMed] [Google Scholar]
339.Li Z, Li W, Wei Y, et al. Deep learning based automatic diagnosis of first-episode psychosis, bipolar disorder and healthy controls. Comput Med Imaging Graph. 2021;89:101882. doi: 10.1016/j.compmedimag.2021.101882 [DOI] [PubMed] [Google Scholar]
340.Yang H, Liu J, Sui J, Pearlson G, Calhoun VD. A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia. Front Hum Neurosci. 2010;4:192. doi: 10.3389/fnhum.2010.00192 [DOI] [PMC free article] [PubMed] [Google Scholar]
341.Kalmady SV, Greiner R, Agrawal R, et al. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. NPJ Schizophr. 2019;5(1):2. doi: 10.1038/s41537-018-0070-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
342.Liu W, Zhang X, Qiao Y, et al. Functional connectivity combined with a machine learning algorithm can classify high-risk first-degree relatives of patients with schizophrenia and identify correlates of cognitive impairments. Front Neurosci. 2020;14:577568. doi: 10.3389/fnins.2020.577568 [DOI] [PMC free article] [PubMed] [Google Scholar]
343.Bansal R, Staib LH, Laine AF, et al. Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses. PLoS One. 2012;7(12):e50698. doi: 10.1371/journal.pone.0050698 [DOI] [PMC free article] [PubMed] [Google Scholar]
344.Li F, Wang J, Liao Y, et al. Differentiation of schizophrenia by combining the spatial EEG brain network patterns of rest and task P300. IEEE Trans Neural Syst Rehabil Eng. 2019;27(4):594-602. doi: 10.1109/TNSRE.2019.2900725 [DOI] [PubMed] [Google Scholar]
345.Zhao Z, Li J, Niu Y, et al. Classification of schizophrenia by combination of brain effective and functional connectivity. Front Neurosci. 2021;15:651439. doi: 10.3389/fnins.2021.651439 [DOI] [PMC free article] [PubMed] [Google Scholar]
346.Park SM, Jeong B, Oh DY, et al. Identification of major psychiatric disorders from resting-state electroencephalography using a machine learning approach. Front Psychiatry. 2021;12:707581. doi: 10.3389/fpsyt.2021.707581 [DOI] [PMC free article] [PubMed] [Google Scholar]
347.Lei D, Pinaya WHL, Young J, et al. Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual. Hum Brain Mapp. 2020;41(5):1119-1135. doi: 10.1002/hbm.24863 [DOI] [PMC free article] [PubMed] [Google Scholar]
348.Kim JY, Lee HS, Lee SH. EEG source network for the diagnosis of schizophrenia and the identification of subtypes based on symptom severity-a machine learning approach. J Clin Med. 2020;9(12):3934. doi: 10.3390/jcm9123934 [DOI] [PMC free article] [PubMed] [Google Scholar]
349.Shi D, Li Y, Zhang H, et al. Machine learning of schizophrenia detection with structural and functional neuroimaging. Dis Markers. 2021;2021:9963824. doi: 10.1155/2021/9963824 [DOI] [PMC free article] [PubMed] [Google Scholar]
350.Shalbaf A, Bagherzadeh S, Maghsoudi A. Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med. 2020;43(4):1229-1239. doi: 10.1007/s13246-020-00925-9 [DOI] [PubMed] [Google Scholar]
351.Janousova E, Montana G, Kasparek T, Schwarz D. Supervised, multivariate, whole-brain reduction did not help to achieve high classification performance in schizophrenia research. Front Neurosci. 2016;10:392. doi: 10.3389/fnins.2016.00392 [DOI] [PMC free article] [PubMed] [Google Scholar]
352.Lieslehto J, Jääskeläinen E, Kiviniemi V, et al. The progression of disorder-specific brain pattern expression in schizophrenia over 9 years. NPJ Schizophr. 2021;7(1):32. doi: 10.1038/s41537-021-00157-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
353.Ke PF, Xiong DS, Li JH, et al. An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data. Sci Rep. 2021;11(1):14636. doi: 10.1038/s41598-021-94007-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
354.Gheiratmand M, Rish I, Cecchi GA, et al. Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms. NPJ Schizophr. 2017;3:22. doi: 10.1038/s41537-017-0022-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
355.Chen J, Li X, Calhoun VD, et al. Sparse deep neural networks on imaging genetics for schizophrenia case-control classification. Hum Brain Mapp. 2021;42(8):2556-2568. doi: 10.1002/hbm.25387 [DOI] [PMC free article] [PubMed] [Google Scholar]
356.Oh J, Oh BL, Lee KU, Chae JH, Yun K. Identifying schizophrenia using structural MRI with a deep learning algorithm. Front Psychiatry. 2020;11:16. doi: 10.3389/fpsyt.2020.00016 [DOI] [PMC free article] [PubMed] [Google Scholar]
357.Singh K, Singh S, Malhotra J. Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proc Inst Mech Eng H. 2021;235(2):167-184. doi: 10.1177/0954411920966937 [DOI] [PubMed] [Google Scholar]
358.Sun J, Cao R, Zhou M, et al. A hybrid deep neural network for classification of schizophrenia using EEG data. Sci Rep. 2021;11(1):4706. doi: 10.1038/s41598-021-83350-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
359.Li YJ, Fan FY. Classification of schizophrenia and depression by EEG with ANNs*. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:2679-2682. [DOI] [PubMed] [Google Scholar]
360.Johannesen JK, Bi J, Jiang R, Kenney JG, Chen CA. Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatr Electrophysiol. 2016;2:3. doi: 10.1186/s40810-016-0017-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
361.Jin K, Xu D, Shen Z, et al. Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis. Acta Neuropsychiatr. 2021;33(4):182-190. doi: 10.1017/neu.2021.9 [DOI] [PubMed] [Google Scholar]
362.Siuly S, Khare SK, Bajaj V, Wang H, Zhang Y. A computerized method for automatic detection of schizophrenia using EEG signals. IEEE Trans Neural Syst Rehabil Eng. 2020;28(11):2390-2400. doi: 10.1109/TNSRE.2020.3022715 [DOI] [PubMed] [Google Scholar]
363.Jang KI, Kim S, Kim SY, Lee C, Chae JH. Machine learning-based electroencephalographic phenotypes of schizophrenia and major depressive disorder. Front Psychiatry. 2021;12:745458. doi: 10.3389/fpsyt.2021.745458 [DOI] [PMC free article] [PubMed] [Google Scholar]
364.Zheng J, Wei X, Wang J, Lin H, Pan H, Shi Y. Diagnosis of schizophrenia based on deep learning using fMRI. Comput Math Methods Med. 2021;2021:8437260. doi: 10.1155/2021/8437260 [DOI] [PMC free article] [PubMed] [Google Scholar]
365.Geenjaar E, Lewis N, Fu Z, Venkatdas R, Plis S, Calhoun V. Fusing multimodal neuroimaging data with a variational autoencoder. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:3630-3633. doi: 10.1109/EMBC46164.2021.9630806 [DOI] [PubMed] [Google Scholar]
366.Najafzadeh H, Esmaeili M, Farhang S, Sarbaz Y, Rasta SH. Automatic classification of schizophrenia patients using resting-state EEG signals. Phys Eng Sci Med. 2021;44(3):855-870. doi: 10.1007/s13246-021-01038-7 [DOI] [PubMed] [Google Scholar]
367.Shoeibi A, Sadeghi D, Moridian P, et al. Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models. Front Neuroinform. 2021;15:777977. doi: 10.3389/fninf.2021.777977 [DOI] [PMC free article] [PubMed] [Google Scholar]
368.Khare SK, Bajaj V. A self-learned decomposition and classification model for schizophrenia diagnosis. Comput Methods Programs Biomed. 2021;211:106450. doi: 10.1016/j.cmpb.2021.106450 [DOI] [PubMed] [Google Scholar]
369.Lee LH, Chen CH, Chang WC, et al. Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants. Eur Psychiatry. 2021;65(1):e1. doi: 10.1192/j.eurpsy.2021.2248 [DOI] [PMC free article] [PubMed] [Google Scholar]
370.Wu J, Lyu G, Wang K, Tang X. A hybrid learning pipeline for automated diagnosis of first-episode schizophrenia utilizing T1-weighted images. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:2761-2764. doi: 10.1109/EMBC46164.2021.9630313 [DOI] [PubMed] [Google Scholar]
371.Hashimoto Y, Ogata Y, Honda M, Yamashita Y. Deep feature extraction for resting-state functional MRI by self-supervised learning and application to schizophrenia diagnosis. Front Neurosci. 2021;15:696853. doi: 10.3389/fnins.2021.696853 [DOI] [PMC free article] [PubMed] [Google Scholar]
372.Sharma M, Acharya UR. Automated detection of schizophrenia using optimal wavelet-based l1 norm features extracted from single-channel EEG. Cogn Neurodyn. 2021;15(4):661-674. doi: 10.1007/s11571-020-09655-w [DOI] [PMC free article] [PubMed] [Google Scholar]
373.Al-Hiyali MI, Yahya N, Faye I, Hussein AF. Identification of autism subtypes based on wavelet coherence of BOLD fMRI signals using convolutional neural network. Sensors (Basel). 2021;21(16):5256. doi: 10.3390/s21165256 [DOI] [PMC free article] [PubMed] [Google Scholar]
374.Elad D, Cetin-Karayumak S, Zhang F, et al. Improving the predictive potential of diffusion MRI in schizophrenia using normative models-towards subject-level classification. Hum Brain Mapp. 2021;42(14):4658-4670. doi: 10.1002/hbm.25574 [DOI] [PMC free article] [PubMed] [Google Scholar]
375.Wu Y, Ren P, Chen R, et al. Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression. Brain Imaging Behav. 2022;16(1):281-290. doi: 10.1007/s11682-021-00501-z [DOI] [PMC free article] [PubMed] [Google Scholar]
376.Cruz-Martinez C, Reyes-Garcia CA, Vanello N. A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis. Comput Methods Programs Biomed. 2022;213:106509. doi: 10.1016/j.cmpb.2021.106509 [DOI] [PubMed] [Google Scholar]
377.Algumaei AH, Algunaid RF, Rushdi MA, Yassine IA. Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data. PLoS One. 2022;17(5):e0265300. doi: 10.1371/journal.pone.0265300 [DOI] [PMC free article] [PubMed] [Google Scholar]
378.Tian Q, Yang NB, Fan Y, et al. Detection of schizophrenia cases from healthy controls with combination of neurocognitive and electrophysiological features. Front Psychiatry. 2022;13:810362. doi: 10.3389/fpsyt.2022.810362 [DOI] [PMC free article] [PubMed] [Google Scholar]
379.Prabhakar SK, Rajaguru H, Kim C, Won DO. A fusion-based technique with hybrid swarm algorithm and deep learning for biosignal classification. Front Hum Neurosci. 2022;16:895761. doi: 10.3389/fnhum.2022.895761 [DOI] [PMC free article] [PubMed] [Google Scholar]
380.Khare SK, Bajaj V. A hybrid decision support system for automatic detection of schizophrenia using EEG signals. Comput Biol Med. 2022;141:105028. doi: 10.1016/j.compbiomed.2021.105028 [DOI] [PubMed] [Google Scholar]
381.Zhou Z, Wang K, Tang J, et al. Cortical thickness distinguishes between major depression and schizophrenia in adolescents. BMC Psychiatry. 2021;21(1):361. doi: 10.1186/s12888-021-03373-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
382.Li H, Cui L, Cao L, et al. Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques. BMC Psychiatry. 2020;20(1):488. doi: 10.1186/s12888-020-02886-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
383.Linke JO, Adleman NE, Sarlls J, et al. White matter microstructure in pediatric bipolar disorder and disruptive mood dysregulation disorder. J Am Acad Child Adolesc Psychiatry. 2020;59(10):1135-1145. doi: 10.1016/j.jaac.2019.05.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
384.Squarcina L, Dagnew TM, Rivolta MW, Bellani M, Sassi R, Brambilla P. Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method. J Affect Disord. 2019;256:416-423. doi: 10.1016/j.jad.2019.06.019 [DOI] [PubMed] [Google Scholar]
385.Matsuo K, Harada K, Fujita Y, et al. Distinctive neuroanatomical substrates for depression in bipolar disorder versus major depressive disorder. Cereb Cortex. 2019;29(1):202-214. doi: 10.1093/cercor/bhx319 [DOI] [PMC free article] [PubMed] [Google Scholar]
386.Frangou S, Dima D, Jogia J. Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder. Neuroimage. 2017;145(Pt B):230-237. doi: 10.1016/j.neuroimage.2016.08.066 [DOI] [PMC free article] [PubMed] [Google Scholar]
387.Doan NT, Kaufmann T, Bettella F, et al. Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders. Neuroimage Clin. 2017;15:719-731. doi: 10.1016/j.nicl.2017.06.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
388.Reavis EA, Lee J, Wynn JK, et al. Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data. Neuroimage Clin. 2017;16:491-497. doi: 10.1016/j.nicl.2017.08.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
389.Mwangi B, Wu MJ, Bauer IE, et al. Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines. Psychiatry Res. 2015;234(2):265-271. doi: 10.1016/j.pscychresns.2015.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
390.Mwangi B, Spiker D, Zunta-Soares GB, Soares JC. Prediction of pediatric bipolar disorder using neuroanatomical signatures of the amygdala. Bipolar Disord. 2014;16(7):713-721. doi: 10.1111/bdi.12222 [DOI] [PMC free article] [PubMed] [Google Scholar]
391.Costafreda SG, Fu CH, Picchioni M, et al. Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder. BMC Psychiatry. 2011;11:18. doi: 10.1186/1471-244X-11-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
392.Rashid B, Arbabshirani MR, Damaraju E, et al. Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. Neuroimage. 2016;134:645-657. doi: 10.1016/j.neuroimage.2016.04.051 [DOI] [PMC free article] [PubMed] [Google Scholar]
393.Kaufmann T, Alnæs D, Brandt CL, et al. Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets. Neuroimage. 2017;147:243-252. doi: 10.1016/j.neuroimage.2016.11.073 [DOI] [PubMed] [Google Scholar]
394.Mourão-Miranda J, Almeida JR, Hassel S, et al. Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression. Bipolar Disord. 2012;14(4):451-460. doi: 10.1111/j.1399-5618.2012.01019.x [DOI] [PMC free article] [PubMed] [Google Scholar]
395.Wu MJ, Mwangi B, Bauer IE, et al. Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning. Neuroimage. 2017;145(Pt B):254-264. doi: 10.1016/j.neuroimage.2016.02.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
396.Bürger C, Redlich R, Grotegerd D, et al. Differential abnormal pattern of anterior cingulate gyrus activation in unipolar and bipolar depression: an fMRI and pattern classification approach. Neuropsychopharmacology. 2017;42(7):1399-1408. doi: 10.1038/npp.2017.36 [DOI] [PMC free article] [PubMed] [Google Scholar]
397.Anticevic A, Cole MW, Repovs G, et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb Cortex. 2014;24(12):3116-3130. doi: 10.1093/cercor/bht165 [DOI] [PMC free article] [PubMed] [Google Scholar]
398.Besga A, Termenon M, Graña M, Echeveste J, Pérez JM, Gonzalez-Pinto A. Discovering Alzheimer’s disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neurosci Lett. 2012;520(1):71-76. doi: 10.1016/j.neulet.2012.05.033 [DOI] [PubMed] [Google Scholar]
399.Chen Y, Storrs J, Tan L, Mazlack LJ, Lee JH, Lu LJ. Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach. J Neurosci Methods. 2014;221:22-31. doi: 10.1016/j.jneumeth.2013.09.001 [DOI] [PubMed] [Google Scholar]
400.Xi C, Lai J, Du Y, et al. Abnormal functional connectivity within the reward network: a potential neuroimaging endophenotype of bipolar disorder. J Affect Disord. 2021;280(Pt B):49-56. doi: 10.1016/j.jad.2020.11.072 [DOI] [PubMed] [Google Scholar]
401.Teng S, Lu CF, Wang PS, et al. Classification of bipolar disorder using basal-ganglia-related functional connectivity in the resting state. Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:1057-1060. [DOI] [PubMed] [Google Scholar]
402.Nazhvani AD, Boostani R, Afrasiabi S, Sadatnezhad K. Classification of ADHD and BMD patients using visual evoked potential. Clin Neurol Neurosurg. 2013;115(11):2329-2335. doi: 10.1016/j.clineuro.2013.08.009 [DOI] [PubMed] [Google Scholar]
403.Chen YL, Tu PC, Huang TH, et al. Using minimal-redundant and maximal-relevant whole-brain functional connectivity to classify bipolar disorder. Front Neurosci. 2020;14:563368. doi: 10.3389/fnins.2020.563368 [DOI] [PMC free article] [PubMed] [Google Scholar]
404.Mwangi B, Wu MJ, Cao B, et al. Individualized prediction and clinical staging of bipolar disorders using neuroanatomical biomarkers. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(2):186-194. doi: 10.1016/j.bpsc.2016.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
405.Qiu Y, Yang M, Li S, et al. Altered fractional amplitude of low-frequency fluctuation in major depressive disorder and bipolar disorder. Front Psychiatry. 2021;12:739210. doi: 10.3389/fpsyt.2021.739210 [DOI] [PMC free article] [PubMed] [Google Scholar]
406.Sánchez-Morla EM, Fuentes JL, Miguel-Jiménez JM, et al. Automatic diagnosis of bipolar disorder using optical coherence tomography data and artificial intelligence. J Pers Med. 2021;11(8):803. doi: 10.3390/jpm11080803 [DOI] [PMC free article] [PubMed] [Google Scholar]
407.Yamashita A, Sakai Y, Yamada T, et al. Generalizable brain network markers of major depressive disorder across multiple imaging sites. PLoS Biol. 2020;18(12):e3000966. doi: 10.1371/journal.pbio.3000966 [DOI] [PMC free article] [PubMed] [Google Scholar]
408.Maglanoc LA, Kaufmann T, Jonassen R, et al. Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. Hum Brain Mapp. 2020;41(1):241-255. doi: 10.1002/hbm.24802 [DOI] [PMC free article] [PubMed] [Google Scholar]
409.Shim M, Jin MJ, Im CH, Lee SH. Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features. Neuroimage Clin. 2019;24:102001. doi: 10.1016/j.nicl.2019.102001 [DOI] [PMC free article] [PubMed] [Google Scholar]
410.Chun JY, Sendi MSE, Sui J, Zhi D, Calhoun VD. Visualizing functional network connectivity difference between healthy control and major depressive disorder using an explainable machine-learning method. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:1424-1427. doi: 10.1109/EMBC44109.2020.9175685 [DOI] [PubMed] [Google Scholar]
411.Uyulan C, Ergüzel TT, Unubol H, et al. Major depressive disorder classification based on different convolutional neural network models: deep learning approach. Clin EEG Neurosci. 2021;52(1):38-51. doi: 10.1177/1550059420916634 [DOI] [PubMed] [Google Scholar]
412.Shi Y, Zhang L, Wang Z, et al. Multivariate machine learning analyses in identification of major depressive disorder using resting-state functional connectivity: a multicentral study. ACS Chem Neurosci. 2021;12(15):2878-2886. doi: 10.1021/acschemneuro.1c00256 [DOI] [PubMed] [Google Scholar]
413.Mahato S, Goyal N, Ram D, Paul S. Detection of depression and scaling of severity using six channel EEG data. J Med Syst. 2020;44(7):118. doi: 10.1007/s10916-020-01573-y [DOI] [PubMed] [Google Scholar]
414.Guo H, Cao X, Liu Z, Li H, Chen J, Zhang K. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder. Neuroreport. 2012;23(17):1006-1011. doi: 10.1097/WNR.0b013e32835a650c [DOI] [PubMed] [Google Scholar]
415.Guo H, Li Y, Mensah GK, et al. Resting-state functional network scale effects and statistical significance-based feature selection in machine learning classification. Comput Math Methods Med. 2019;2019:9108108. doi: 10.1155/2019/9108108 [DOI] [PMC free article] [PubMed] [Google Scholar]
416.Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds CF III, Aizenstein HJ. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry. 2015;30(10):1056-1067. doi: 10.1002/gps.4262 [DOI] [PMC free article] [PubMed] [Google Scholar]
417.Yang J, Zhang M, Ahn H, et al. Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp. 2018;39(11):4420-4439. doi: 10.1002/hbm.24282 [DOI] [PMC free article] [PubMed] [Google Scholar]
418.Ahmadlou M, Adeli H, Adeli A. Fractality analysis of frontal brain in major depressive disorder. Int J Psychophysiol. 2012;85(2):206-211. doi: 10.1016/j.ijpsycho.2012.05.001 [DOI] [PubMed] [Google Scholar]
419.Sacchet MD, Livermore EE, Iglesias JE, Glover GH, Gotlib IH. Subcortical volumes differentiate major depressive disorder, bipolar disorder, and remitted major depressive disorder. J Psychiatr Res. 2015;68:91-98. doi: 10.1016/j.jpsychires.2015.06.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
420.Ramasubbu R, Brown EC, Marcil LD, Talai AS, Forkert ND. Automatic classification of major depression disorder using arterial spin labeling MRI perfusion measurements. Psychiatry Clin Neurosci. 2019;73(8):486-493. doi: 10.1111/pcn.12862 [DOI] [PubMed] [Google Scholar]
421.Li H, Song S, Wang D, et al. Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features. BMC Psychiatry. 2021;21(1):415. doi: 10.1186/s12888-021-03414-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
422.Zhong X, Shi H, Ming Q, et al. Whole-brain resting-state functional connectivity identified major depressive disorder: a multivariate pattern analysis in two independent samples. J Affect Disord. 2017;218:346-352. doi: 10.1016/j.jad.2017.04.040 [DOI] [PubMed] [Google Scholar]
423.Liu W, Zhang C, Wang X, et al. Functional connectivity of major depression disorder using ongoing EEG during music perception. Clin Neurophysiol. 2020;131(10):2413-2422. doi: 10.1016/j.clinph.2020.06.031 [DOI] [PubMed] [Google Scholar]
424.Liao SC, Wu CT, Huang HC, Cheng WT, Liu YH. Major depression detection from EEG signals using kernel Eigen-filter-bank common spatial patterns. Sensors (Basel). 2017;17(6):1385. doi: 10.3390/s17061385 [DOI] [PMC free article] [PubMed] [Google Scholar]
425.Rosa MJ, Portugal L, Hahn T, et al. Sparse network-based models for patient classification using fMRI. Neuroimage. 2015;105:493-506. doi: 10.1016/j.neuroimage.2014.11.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
426.Schnyer DM, Clasen PC, Gonzalez C, Beevers CG. Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Res Neuroimaging. 2017;264:1-9. doi: 10.1016/j.pscychresns.2017.03.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
427.Johnston BA, Steele JD, Tolomeo S, Christmas D, Matthews K. Structural MRI-based predictions in patients with treatment-refractory depression (TRD). PLoS One. 2015;10(7):e0132958. doi: 10.1371/journal.pone.0132958 [DOI] [PMC free article] [PubMed] [Google Scholar]
428.Liu F, Guo W, Yu D, et al. Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans. PLoS One. 2012;7(7):e40968. doi: 10.1371/journal.pone.0040968 [DOI] [PMC free article] [PubMed] [Google Scholar]
429.Wei M, Qin J, Yan R, Li H, Yao Z, Lu Q. Identifying major depressive disorder using Hurst exponent of resting-state brain networks. Psychiatry Res. 2013;214(3):306-312. doi: 10.1016/j.pscychresns.2013.09.008 [DOI] [PubMed] [Google Scholar]
430.Chu SH, Lenglet C, Schreiner MW, Klimes-Dougan B, Cullen K, Parhi KK. Anatomical biomarkers for adolescent major depressive disorder from diffusion weighted imaging using SVM classifier. Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:2740-2743. doi: 10.1109/EMBC.2018.8512852 [DOI] [PubMed] [Google Scholar]
431.Guo H, Qin M, Chen J, Xu Y, Xiang J. Machine-learning classifier for patients with major depressive disorder: multifeature approach based on a high-order minimum spanning tree functional brain network. Comput Math Methods Med. 2017;2017:4820935. doi: 10.1155/2017/4820935 [DOI] [PMC free article] [PubMed] [Google Scholar]
432.Zeng LL, Shen H, Liu L, Hu D. Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp. 2014;35(4):1630-1641. doi: 10.1002/hbm.22278 [DOI] [PMC free article] [PubMed] [Google Scholar]
433.Fang P, Zeng LL, Shen H, et al. Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging. PLoS One. 2012;7(9):e45972. doi: 10.1371/journal.pone.0045972 [DOI] [PMC free article] [PubMed] [Google Scholar]
434.Tan W, Liu Z, Xi C, et al. Decreased integration of the frontoparietal network during a working memory task in major depressive disorder. Aust N Z J Psychiatry. 2021;55(6):577-587. doi: 10.1177/0004867420978284 [DOI] [PubMed] [Google Scholar]
435.Cao L, Guo S, Xue Z, et al. Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci. 2014;68(2):110-119. doi: 10.1111/pcn.12106 [DOI] [PubMed] [Google Scholar]
436.Zeng LL, Shen H, Liu L, et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain. 2012;135(Pt 5):1498-1507. doi: 10.1093/brain/aws059 [DOI] [PubMed] [Google Scholar]
437.Ramasubbu R, Brown MR, Cortese F, et al. Accuracy of automated classification of major depressive disorder as a function of symptom severity. Neuroimage Clin. 2016;12:320-331. doi: 10.1016/j.nicl.2016.07.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
438.Qiu L, Huang X, Zhang J, et al. Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images. J Psychiatry Neurosci. 2014;39(2):78-86. [DOI] [PMC free article] [PubMed] [Google Scholar]
439.Mwangi B, Ebmeier KP, Matthews K, Steele JD. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain. 2012;135(Pt 5):1508-1521. doi: 10.1093/brain/aws084 [DOI] [PubMed] [Google Scholar]
440.Zhao J, Huang J, Zhi D, et al. Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders. J Neurosci Methods. 2020;341:108756. doi: 10.1016/j.jneumeth.2020.108756 [DOI] [PMC free article] [PubMed] [Google Scholar]
441.Bi K, Hua L, Wei M, Qin J, Lu Q, Yao Z. Dynamic functional-structural coupling within acute functional state change phases: evidence from a depression recognition study. J Affect Disord. 2016;191:145-155. doi: 10.1016/j.jad.2015.11.041 [DOI] [PubMed] [Google Scholar]
442.Zhu X, Yuan F, Zhou G, et al. Cross-network interaction for diagnosis of major depressive disorder based on resting state functional connectivity. Brain Imaging Behav. 2021;15(3):1279-1289. doi: 10.1007/s11682-020-00326-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
443.Sundermann B, Feder S, Wersching H, et al. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm (Vienna). 2017;124(5):589-605. doi: 10.1007/s00702-016-1673-8 [DOI] [PubMed] [Google Scholar]
444.Qin J, Wei M, Liu H, et al. Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification. Magn Reson Imaging. 2014;32(10):1314-1320. doi: 10.1016/j.mri.2014.08.037 [DOI] [PubMed] [Google Scholar]
445.Guo M, Wang T, Zhang Z, et al. Diagnosis of major depressive disorder using whole-brain effective connectivity networks derived from resting-state functional MRI. J Neural Eng. 2020;17(5):056038. doi: 10.1088/1741-2552/abbc28 [DOI] [PubMed] [Google Scholar]
446.Khan D, Yahya N, Kamel N, Faye I. Automated diagnosis of major depressive disorder using brain effective connectivity and 3D convolutional neural network. IEEE Access. 2021;(9):8835-8846. doi: 10.1109/ACCESS.2021.3049427 [DOI] [Google Scholar]
447.Zhang B, Yan G, Yang Z, Su Y, Wang J, Lei T. Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification. IEEE Trans Neural Syst Rehabil Eng. 2021;29:215-229. doi: 10.1109/TNSRE.2020.3043426 [DOI] [PubMed] [Google Scholar]
448.Zhu Y, Jayagopal JK, Mehta RK, et al. Classifying major depressive disorder using fNIRS during motor rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2020;28(4):961-969. doi: 10.1109/TNSRE.2020.2972270 [DOI] [PubMed] [Google Scholar]
449.Geng X, Xu J, Liu B, Shi Y. Multivariate classification of major depressive disorder using the effective connectivity and functional connectivity. Front Neurosci. 2018;12:38. doi: 10.3389/fnins.2018.00038 [DOI] [PMC free article] [PubMed] [Google Scholar]
450.Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH. Elucidating brain connectivity networks in major depressive disorder using classification-based scoring. Proc IEEE Int Symp Biomed Imaging. 2014;2014:246-249. doi: 10.1109/ISBI.2014.6867855 [DOI] [PMC free article] [PubMed] [Google Scholar]
451.Yan B, Xu X, Liu M, et al. Quantitative identification of major depression based on resting-state dynamic functional connectivity: a machine learning approach. Front Neurosci. 2020;14:191. doi: 10.3389/fnins.2020.00191 [DOI] [PMC free article] [PubMed] [Google Scholar]
452.Nakano T, Takamura M, Ichikawa N, et al. Enhancing multi-center generalization of machine learning-based depression diagnosis from resting-state fMRI. Front Psychiatry. 2020;11:400. doi: 10.3389/fpsyt.2020.00400 [DOI] [PMC free article] [PubMed] [Google Scholar]
453.Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH. Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front Psychiatry. 2015;6:21. doi: 10.3389/fpsyt.2015.00021 [DOI] [PMC free article] [PubMed] [Google Scholar]
454.Shi Y, Zhang L, He C, et al. Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: a multicenter study based on machine learning. J Affect Disord. 2021;295:148-155. doi: 10.1016/j.jad.2021.08.027 [DOI] [PubMed] [Google Scholar]
455.Duan L, Duan H, Qiao Y, et al. Machine learning approaches for MDD detection and emotion decoding using EEG signals. Front Hum Neurosci. 2020;14:284. doi: 10.3389/fnhum.2020.00284 [DOI] [PMC free article] [PubMed] [Google Scholar]
456.Saeedi A, Saeedi M, Maghsoudi A, Shalbaf A. Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn Neurodyn. 2021;15(2):239-252. doi: 10.1007/s11571-020-09619-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
457.Bi K, Chattun MR, Liu X, et al. Abnormal early dynamic individual patterns of functional networks in low gamma band for depression recognition. J Affect Disord. 2018;238:366-374. doi: 10.1016/j.jad.2018.05.078 [DOI] [PubMed] [Google Scholar]
458.Qin J, Wei M, Liu H, et al. Altered anatomical patterns of depression in relation to antidepressant treatment: evidence from a pattern recognition analysis on the topological organization of brain networks. J Affect Disord. 2015;180:129-137. doi: 10.1016/j.jad.2015.03.059 [DOI] [PubMed] [Google Scholar]
459.Knott V, Mahoney C, Kennedy S, Evans K. EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res. 2001;106(2):123-140. doi: 10.1016/S0925-4927(00)00080-9 [DOI] [PubMed] [Google Scholar]
460.Marquand AF, Mourão-Miranda J, Brammer MJ, Cleare AJ, Fu CH. Neuroanatomy of verbal working memory as a diagnostic biomarker for depression. Neuroreport. 2008;19(15):1507-1511. doi: 10.1097/WNR.0b013e328310425e [DOI] [PubMed] [Google Scholar]
461.Lu Q, Bi K, Liu C, Luo G, Tang H, Yao Z. Predicting depression based on dynamic regional connectivity: a windowed Granger causality analysis of MEG recordings. Brain Res. 2013;1535:52-60. doi: 10.1016/j.brainres.2013.08.033 [DOI] [PubMed] [Google Scholar]
462.Craddock RC, Holtzheimer PE III, Hu XP, Mayberg HS. Disease state prediction from resting state functional connectivity. Magn Reson Med. 2009;62(6):1619-1628. doi: 10.1002/mrm.22159 [DOI] [PMC free article] [PubMed] [Google Scholar]
463.Kang M, Kwon H, Park JH, Kang S, Lee Y. Deep-asymmetry: asymmetry matrix image for deep learning method in pre-screening depression. Sensors (Basel). 2020;20(22):6526. doi: 10.3390/s20226526 [DOI] [PMC free article] [PubMed] [Google Scholar]
464.Lois G, Wessa M. Differential association of default mode network connectivity and rumination in healthy individuals and remitted MDD patients. Soc Cogn Affect Neurosci. 2016;11(11):1792-1801. doi: 10.1093/scan/nsw085 [DOI] [PMC free article] [PubMed] [Google Scholar]
465.Hasanzadeh F, Mohebbi M, Rostami R. Graph theory analysis of directed functional brain networks in major depressive disorder based on EEG signal. J Neural Eng. 2020;17(2):026010. doi: 10.1088/1741-2552/ab7613 [DOI] [PubMed] [Google Scholar]
466.Mumtaz W, Ali SSA, Yasin MAM, Malik AS. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput. 2018;56(2):233-246. doi: 10.1007/s11517-017-1685-z [DOI] [PubMed] [Google Scholar]
467.Sen B, Cullen KR, Parhi KK. Classification of adolescent major depressive disorder via static and dynamic connectivity. IEEE J Biomed Health Inform. 2021;25(7):2604-2614. doi: 10.1109/JBHI.2020.3043427 [DOI] [PubMed] [Google Scholar]
468.Movahed RA, Jahromi GP, Shahyad S, Meftahi GH. A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis. J Neurosci Methods. 2021;358:109209. doi: 10.1016/j.jneumeth.2021.109209 [DOI] [PubMed] [Google Scholar]
469.Li Y, Dai X, Wu H, Wang L. Establishment of effective biomarkers for depression diagnosis with fusion of multiple resting-state connectivity measures. Front Neurosci. 2021;15:729958. doi: 10.3389/fnins.2021.729958 [DOI] [PMC free article] [PubMed] [Google Scholar]
470.Wu CT, Huang HC, Huang S, et al. Resting-state EEG signal for major depressive disorder detection: a systematic validation on a large and diverse dataset. Biosensors (Basel). 2021;11(12):499. doi: 10.3390/bios11120499 [DOI] [PMC free article] [PubMed] [Google Scholar]
471.Gao Y, Wang X, Xiong Z, et al. Abnormal fractional amplitude of low-frequency fluctuation as a potential imaging biomarker for first-episode major depressive disorder: a resting-state fMRI study and support vector machine analysis. Front Neurol. 2021;12:751400. doi: 10.3389/fneur.2021.751400 [DOI] [PMC free article] [PubMed] [Google Scholar]
472.Lin H, Jian C, Cao Y, et al. MDD-TSVM: a novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals. Comput Biol Med. 2021;140:105039. doi: 10.1016/j.compbiomed.2021.105039 [DOI] [PubMed] [Google Scholar]
473.Liu B, Chang H, Peng K, Wang X. An end-to-end depression recognition method based on EEGNet. Front Psychiatry. 2022;13:864393. doi: 10.3389/fpsyt.2022.864393 [DOI] [PMC free article] [PubMed] [Google Scholar]
474.Frick A, Gingnell M, Marquand AF, et al. Classifying social anxiety disorder using multivoxel pattern analyses of brain function and structure. Behav Brain Res. 2014;259:330-335. doi: 10.1016/j.bbr.2013.11.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
475.Zhang W, Yang X, Lui S, et al. Diagnostic prediction for social anxiety disorder via multivariate pattern analysis of the regional homogeneity. Biomed Res Int. 2015;2015:763965. doi: 10.1155/2015/763965 [DOI] [PMC free article] [PubMed] [Google Scholar]
476.Liu F, Guo W, Fouche JP, et al. Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Struct Funct. 2015;220(1):101-115. doi: 10.1007/s00429-013-0641-4 [DOI] [PubMed] [Google Scholar]
477.Xing M, Fitzgerald JM, Klumpp H. Classification of social anxiety disorder with support vector machine analysis using neural correlates of social signals of threat. Front Psychiatry. 2020;11:144. doi: 10.3389/fpsyt.2020.00144 [DOI] [PMC free article] [PubMed] [Google Scholar]
478.Gavrilescu M, Vizireanu N. Predicting depression, anxiety, and stress levels from videos using the facial action coding system. Sensors (Basel). 2019;19(17):3693. doi: 10.3390/s19173693 [DOI] [PMC free article] [PubMed] [Google Scholar]
479.Xie Y, Yang B, Lu X, et al. Anxiety and depression diagnosis method based on brain networks and convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:1503-1506. doi: 10.1109/EMBC44109.2020.9176471 [DOI] [PubMed] [Google Scholar]
480.Qiao J, Li A, Cao C, Wang Z, Sun J, Xu G. Aberrant functional network connectivity as a biomarker of generalized anxiety disorder. Front Hum Neurosci. 2017;11:626. doi: 10.3389/fnhum.2017.00626 [DOI] [PMC free article] [PubMed] [Google Scholar]
481.Xing X, Jin L, Li Q, et al. Modeling essential connections in obsessive-compulsive disorder patients using functional MRI. Brain Behav. 2020;10(2):e01499. doi: 10.1002/brb3.1499 [DOI] [PMC free article] [PubMed] [Google Scholar]
482.Yang X, Hu X, Tang W, et al. Multivariate classification of drug-naive obsessive-compulsive disorder patients and healthy controls by applying an SVM to resting-state functional MRI data. BMC Psychiatry. 2019;19(1):210. doi: 10.1186/s12888-019-2184-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
483.Bruin WB, Taylor L, Thomas RM, et al. ; ENIGMA-OCD Working Group . Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters. Transl Psychiatry. 2020;10(1):342. doi: 10.1038/s41398-020-01013-y [DOI] [PMC free article] [PubMed] [Google Scholar]
484.Takagi Y, Sakai Y, Lisi G, et al. A neural marker of obsessive-compulsive disorder from whole-brain functional connectivity. Sci Rep. 2017;7(1):7538. doi: 10.1038/s41598-017-07792-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
485.Zhou C, Cheng Y, Ping L, et al. Support vector machine classification of obsessive-compulsive disorder based on whole-brain volumetry and diffusion tensor imaging. Front Psychiatry. 2018;9:524. doi: 10.3389/fpsyt.2018.00524 [DOI] [PMC free article] [PubMed] [Google Scholar]
486.Hu X, Liu Q, Li B, et al. Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy. Eur Neuropsychopharmacol. 2016;26(2):246-254. doi: 10.1016/j.euroneuro.2015.12.014 [DOI] [PubMed] [Google Scholar]
487.Bu X, Hu X, Zhang L, et al. Investigating the predictive value of different resting-state functional MRI parameters in obsessive-compulsive disorder. Transl Psychiatry. 2019;9(1):17. doi: 10.1038/s41398-018-0362-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
488.Sen B, Bernstein GA, Tingting X, et al. Classification of obsessive-compulsive disorder from resting-state fMRI. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:3606-3609. doi: 10.1109/EMBC.2016.7591508 [DOI] [PubMed] [Google Scholar]
489.Trambaiolli LR, Biazoli CE Jr, Balardin JB, Hoexter MQ, Sato JR. The relevance of feature selection methods to the classification of obsessive-compulsive disorder based on volumetric measures. J Affect Disord. 2017;222:49-56. doi: 10.1016/j.jad.2017.06.061 [DOI] [PubMed] [Google Scholar]
490.Li F, Huang X, Tang W, et al. Multivariate pattern analysis of DTI reveals differential white matter in individuals with obsessive-compulsive disorder. Hum Brain Mapp. 2014;35(6):2643-2651. doi: 10.1002/hbm.22357 [DOI] [PMC free article] [PubMed] [Google Scholar]
491.Shenas SK, Halici U, Çiçek M. A comparative analysis of functional connectivity data in resting and task-related conditions of the brain for disease signature of OCD. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:978-981. doi: 10.1109/EMBC.2014.6943756 [DOI] [PubMed] [Google Scholar]
492.Liu J, Bu X, Hu X, et al. Temporal variability of regional intrinsic neural activity in drug-naïve patients with obsessive-compulsive disorder. Hum Brain Mapp. 2021;42(12):3792-3803. doi: 10.1002/hbm.25465 [DOI] [PMC free article] [PubMed] [Google Scholar]
493.Liu W, Hua M, Qin J, et al. Disrupted pathways from frontal-parietal cortex to basal ganglia and cerebellum in patients with unmedicated obsessive compulsive disorder as observed by whole-brain resting-state effective connectivity analysis: a small sample pilot study. Brain Imaging Behav. 2021;15(3):1344-1354. doi: 10.1007/s11682-020-00333-3 [DOI] [PubMed] [Google Scholar]
494.Chen Y, Ou Y, Lv D, et al. Decreased nucleus accumbens connectivity at rest in medication-free patients with obsessive-compulsive disorder. Neural Plast. 2021;2021:9966378. doi: 10.1155/2021/9966378 [DOI] [PMC free article] [PubMed] [Google Scholar]
495.Kalatzis I, Piliouras N, Glotsos D, et al. Identifying differences in the P600 component of ERP-signals between OCD patients and controls employing a PNN-based majority vote classification scheme. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:3994-3997. doi: 10.1109/IEMBS.2005.1615337 [DOI] [PubMed] [Google Scholar]
496.Aydin S, Arica N, Ergul E, Tan O. Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements. Int J Neural Syst. 2015;25(3):1550010. doi: 10.1142/S0129065715500100 [DOI] [PubMed] [Google Scholar]
497.Zilcha-Mano S, Zhu X, Suarez-Jimenez B, et al. Diagnostic and predictive neuroimaging biomarkers for posttraumatic stress disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5(7):688-696. doi: 10.1016/j.bpsc.2020.03.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
498.Nicholson AA, Densmore M, McKinnon MC, et al. Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach. Psychol Med. 2019;49(12):2049-2059. doi: 10.1017/S0033291718002866 [DOI] [PubMed] [Google Scholar]
499.Zhu H, Yuan M, Qiu C, et al. Multivariate classification of earthquake survivors with post-traumatic stress disorder based on large-scale brain networks. Acta Psychiatr Scand. 2020;141(3):285-298. doi: 10.1111/acps.13150 [DOI] [PubMed] [Google Scholar]
500.Nicholson AA, Harricharan S, Densmore M, et al. Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning. Neuroimage Clin. 2020;27:102262. doi: 10.1016/j.nicl.2020.102262 [DOI] [PMC free article] [PubMed] [Google Scholar]
501.Harricharan S, Nicholson AA, Thome J, et al. PTSD and its dissociative subtype through the lens of the insula: anterior and posterior insula resting-state functional connectivity and its predictive validity using machine learning. Psychophysiology. 2020;57(1):e13472. doi: 10.1111/psyp.13472 [DOI] [PubMed] [Google Scholar]
502.Yang J, Lei D, Qin K, et al. Using deep learning to classify pediatric posttraumatic stress disorder at the individual level. BMC Psychiatry. 2021;21(1):535. doi: 10.1186/s12888-021-03503-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
503.Chen Z, Feng P, Becker B, et al. Neural connectome prospectively encodes the risk of post-traumatic stress disorder (PTSD) symptom during the COVID-19 pandemic. Neurobiol Stress. 2021;15:100378. doi: 10.1016/j.ynstr.2021.100378 [DOI] [PMC free article] [PubMed] [Google Scholar]
504.Eken A, Çolak B, Bal NB, et al. Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivity. J Neural Eng. 2019;17(1):016012. doi: 10.1088/1741-2552/ab50b2 [DOI] [PubMed] [Google Scholar]
505.Lavagnino L, Amianto F, Mwangi B, et al. Identifying neuroanatomical signatures of anorexia nervosa: a multivariate machine learning approach. Psychol Med. 2015;45(13):2805-2812. doi: 10.1017/S0033291715000768 [DOI] [PubMed] [Google Scholar]
506.Lavagnino L, Mwangi B, Cao B, Shott ME, Soares JC, Frank GKW. Cortical thickness patterns as state biomarker of anorexia nervosa. Int J Eat Disord. 2018;51(3):241-249. doi: 10.1002/eat.22828 [DOI] [PMC free article] [PubMed] [Google Scholar]
507.Geisler D, Borchardt V, Boehm I, et al. Altered global brain network topology as a trait marker in patients with anorexia nervosa. Psychol Med. 2020;50(1):107-115. doi: 10.1017/S0033291718004002 [DOI] [PubMed] [Google Scholar]
508.Weygandt M, Schaefer A, Schienle A, Haynes JD. Diagnosing different binge-eating disorders based on reward-related brain activation patterns. Hum Brain Mapp. 2012;33(9):2135-2146. doi: 10.1002/hbm.21345 [DOI] [PMC free article] [PubMed] [Google Scholar]
509.Lee MH, Kim N, Yoo J, et al. Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder. Sci Rep. 2021;11(1):9402. doi: 10.1038/s41598-021-88845-w [DOI] [PMC free article] [PubMed] [Google Scholar]
510.Jansen C, Penzel T, Hodel S, Breuer S, Spott M, Krefting D. Network physiology in insomnia patients: assessment of relevant changes in network topology with interpretable machine learning models. Chaos. 2019;29(12):123129. doi: 10.1063/1.5128003 [DOI] [PubMed] [Google Scholar]
511.Zhang J, Liu Y, Luo R, et al. Classification of pure conduct disorder from healthy controls based on indices of brain networks during resting state. Med Biol Eng Comput. 2020;58(9):2071-2082. doi: 10.1007/s11517-020-02215-8 [DOI] [PubMed] [Google Scholar]
512.Zhang J, Cao W, Wang M, Wang N, Yao S, Huang B. Multivoxel pattern analysis of structural MRI in children and adolescents with conduct disorder. Brain Imaging Behav. 2019;13(5):1273-1280. doi: 10.1007/s11682-018-9953-6 [DOI] [PubMed] [Google Scholar]
513.Zhang J, Liu W, Zhang J, et al. Distinguishing adolescents with conduct disorder from typically developing youngsters based on pattern classification of brain structural MRI. Front Hum Neurosci. 2018;12:152. doi: 10.3389/fnhum.2018.00152 [DOI] [PMC free article] [PubMed] [Google Scholar]
514.Tang Y, Jiang W, Liao J, Wang W, Luo A. Identifying individuals with antisocial personality disorder using resting-state FMRI. PLoS One. 2013;8(4):e60652. doi: 10.1371/journal.pone.0060652 [DOI] [PMC free article] [PubMed] [Google Scholar]
515.Tang Y, Liu W, Chen J, Liao J, Hu D, Wang W. Altered spontaneous activity in antisocial personality disorder revealed by regional homogeneity. Neuroreport. 2013;24(11):590-595. doi: 10.1097/WNR.0b013e3283627993 [DOI] [PubMed] [Google Scholar]
516.Sato JR, de Oliveira-Souza R, Thomaz CE, et al. Identification of psychopathic individuals using pattern classification of MRI images. Soc Neurosci. 2011;6(5-6):627-639. doi: 10.1080/17470919.2011.562687 [DOI] [PubMed] [Google Scholar]
517.Wetherill RR, Rao H, Hager N, Wang J, Franklin TR, Fan Y. Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI. Addict Biol. 2019;24(4):811-821. doi: 10.1111/adb.12644 [DOI] [PMC free article] [PubMed] [Google Scholar]
518.Li Y, Cui Z, Liao Q, et al. Support vector machine-based multivariate pattern classification of methamphetamine dependence using arterial spin labeling. Addict Biol. 2019;24(6):1254-1262. doi: 10.1111/adb.12705 [DOI] [PubMed] [Google Scholar]
519.Ding X, Li Y, Li D, Li L, Liu X. Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment. Brain Behav. 2020;10(11):e01814. doi: 10.1002/brb3.1814 [DOI] [PMC free article] [PubMed] [Google Scholar]
520.Mete M, Sakoglu U, Spence JS, Devous MD Sr, Harris TS, Adinoff B. Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach. BMC Bioinformatics. 2016;17(suppl 13):357. doi: 10.1186/s12859-016-1218-z [DOI] [PMC free article] [PubMed] [Google Scholar]
521.Adeli E, Zahr NM, Pfefferbaum A, Sullivan EV, Pohl KM. Novel machine learning identifies brain patterns distinguishing diagnostic membership of human immunodeficiency virus, alcoholism, and their comorbidity of individuals. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4(6):589-599. doi: 10.1016/j.bpsc.2019.02.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
522.Guggenmos M, Schmack K, Veer IM, et al. A multimodal neuroimaging classifier for alcohol dependence. Sci Rep. 2020;10(1):298. doi: 10.1038/s41598-019-56923-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
523.Mehla VK, Singhal A, Singh P. A novel approach for automated alcoholism detection using Fourier decomposition method. J Neurosci Methods. 2020;346:108945. doi: 10.1016/j.jneumeth.2020.108945 [DOI] [PubMed] [Google Scholar]
524.Zhu X, Du X, Kerich M, Lohoff FW, Momenan R. Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI. Neurosci Lett. 2018;676:27-33. doi: 10.1016/j.neulet.2018.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
525.Mumtaz W, Saad MNBM, Kamel N, Ali SSA, Malik AS. An EEG-based functional connectivity measure for automatic detection of alcohol use disorder. Artif Intell Med. 2018;84:79-89. doi: 10.1016/j.artmed.2017.11.002 [DOI] [PubMed] [Google Scholar]
526.Wang SH, Lv YD, Sui Y, Liu S, Wang SJ, Zhang YD. Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. J Med Syst. 2017;42(1):2. doi: 10.1007/s10916-017-0845-x [DOI] [PubMed] [Google Scholar]
527.Kinreich S, McCutcheon VV, Aliev F, et al. Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach. Transl Psychiatry. 2021;11(1):166. doi: 10.1038/s41398-021-01281-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
528.Bae Y, Yoo BW, Lee JC, Kim HC. Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism. Physiol Meas. 2017;38(5):759-773. doi: 10.1088/1361-6579/aa6b4c [DOI] [PubMed] [Google Scholar]
529.Kumar S, Ghosh S, Tetarway S, Sinha RK. Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism. Med Biol Eng Comput. 2015;53(7):609-622. doi: 10.1007/s11517-015-1264-0 [DOI] [PubMed] [Google Scholar]
530.Khan DM, Yahya N, Kamel N, Faye I. Effective connectivity in default mode network for alcoholism diagnosis. IEEE Trans Neural Syst Rehabil Eng. 2021;29:796-808. doi: 10.1109/TNSRE.2021.3075737 [DOI] [PubMed] [Google Scholar]
531.Mumtaz W, Vuong PL, Xia L, Malik AS, Rashid RBA. An EEG-based machine learning method to screen alcohol use disorder. Cogn Neurodyn. 2017;11(2):161-171. doi: 10.1007/s11571-016-9416-y [DOI] [PMC free article] [PubMed] [Google Scholar]
532.Hahn S, Mackey S, Cousijn J, et al. Predicting alcohol dependence from multi-site brain structural measures. Hum Brain Mapp. 2022;43(1):555-565. doi: 10.1002/hbm.25248 [DOI] [PMC free article] [PubMed] [Google Scholar]
533.Zhang H, Silva FHS, Ohata EF, Medeiros AG, Rebouças Filho PP. Bi-dimensional approach based on transfer learning for alcoholism pre-disposition classification via EEG signals. Front Hum Neurosci. 2020;14:365. doi: 10.3389/fnhum.2020.00365 [DOI] [PMC free article] [PubMed] [Google Scholar]
534.Wang SH, Xie S, Chen X, et al. Alcoholism identification based on an AlexNet transfer learning model. Front Psychiatry. 2019;10:205. doi: 10.3389/fpsyt.2019.00205 [DOI] [PMC free article] [PubMed] [Google Scholar]
535.Prabhakar SK, Rajaguru H. Alcoholic EEG signal classification with correlation dimension based distance metrics approach and modified Adaboost classification. Heliyon. 2020;6(12):e05689. doi: 10.1016/j.heliyon.2020.e05689 [DOI] [PMC free article] [PubMed] [Google Scholar]
536.Dai X, Gao L, Zhang H, Wei X, Liu Z. A combination of support vector machine and voxel-based morphometry in adult male alcohol use disorder patients with cognitive deficits. Brain Res. 2021;1771:147644. doi: 10.1016/j.brainres.2021.147644 [DOI] [PubMed] [Google Scholar]
537.Erguzel TT, Noyan CO, Eryilmaz G, et al. Binomial logistic regression and artificial neural network methods to classify opioid-dependent subjects and control group using quantitative EEG power measures. Clin EEG Neurosci. 2019;50(5):303-310. doi: 10.1177/1550059418824450 [DOI] [PubMed] [Google Scholar]
538.Cremers H, van Zutphen L, Duken S, et al. Borderline personality disorder classification based on brain network measures during emotion regulation. Eur Arch Psychiatry Clin Neurosci. 2021;271(6):1169-1178. doi: 10.1007/s00406-020-01201-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
539.Xu T, Cullen KR, Houri A, Lim KO, Schulz SC, Parhi KK. Classification of borderline personality disorder based on spectral power of resting-state fMRI. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:5036-5039. [DOI] [PMC free article] [PubMed] [Google Scholar]
540.Grecucci A, Lapomarda G, Messina I, Monachesi B, Sorella S, Siugzdaite R. Structural features related to affective instability correctly classify patients with borderline personality disorder: a supervised machine learning approach. Front Psychiatry. 2022;13:804440. doi: 10.3389/fpsyt.2022.804440 [DOI] [PMC free article] [PubMed] [Google Scholar]
541.Menon SS, Krishnamurthy K. Multimodal ensemble deep learning to predict disruptive behavior disorders in children. Front Neuroinform. 2021;15:742807. doi: 10.3389/fninf.2021.742807 [DOI] [PMC free article] [PubMed] [Google Scholar]
542.Page MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372(160):n160. doi: 10.1136/bmj.n160 [DOI] [PMC free article] [PubMed] [Google Scholar]
543.Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283(15):2008-2012. doi: 10.1001/jama.283.15.2008 [DOI] [PubMed] [Google Scholar]
544.Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10):e1001744. doi: 10.1371/journal.pmed.1001744 [DOI] [PMC free article] [PubMed] [Google Scholar]
545.Venema E, Wessler BS, Paulus JK, et al. Large-scale validation of the prediction model risk of bias assessment tool (PROBAST) using a short form: high risk of bias models show poorer discrimination. J Clin Epidemiol. 2021;138:32-39. doi: 10.1016/j.jclinepi.2021.06.017 [DOI] [PubMed] [Google Scholar]
546.Di Tanna GL, Wirtz H, Burrows KL, Globe G. Evaluating risk prediction models for adults with heart failure: a systematic literature review. PLoS One. 2020;15(1):e0224135. doi: 10.1371/journal.pone.0224135 [DOI] [PMC free article] [PubMed] [Google Scholar]
547.Du M, Haag D, Song Y, Lynch J, Mittinty M. Examining bias and reporting in oral health prediction modeling studies. J Dent Res. 2020;99(4):374-387. doi: 10.1177/0022034520903725 [DOI] [PubMed] [Google Scholar]
548.Varoquaux G. Cross-validation failure: small sample sizes lead to large error bars. NeuroImage. 2018;180(pt A):68-77. doi: 10.1016/j.neuroimage.2017.06.061 [DOI] [PubMed] [Google Scholar]
549.Janssen RJ, Mourão-Miranda J, Schnack HG. Making individual prognoses in psychiatry using neuroimaging and machine learning. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(9):798-808. doi: 10.1016/j.bpsc.2018.04.004 [DOI] [PubMed] [Google Scholar]
550.Jollans L, Boyle R, Artiges E, et al. Quantifying performance of machine learning methods for neuroimaging data. Neuroimage. 2019;199:351-365. doi: 10.1016/j.neuroimage.2019.05.082 [DOI] [PMC free article] [PubMed] [Google Scholar]
551.Poldrack RA, Huckins G, Varoquaux G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry. 2020;77(5):534-540. doi: 10.1001/jamapsychiatry.2019.3671 [DOI] [PMC free article] [PubMed] [Google Scholar]
552.Flint C, Cearns M, Opel N, et al. Systematic misestimation of machine learning performance in neuroimaging studies of depression. Neuropsychopharmacology. 2021;46(8):1510-1517. doi: 10.1038/s41386-021-01020-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
553.Samala RK, Chan H-P, Hadjiiski L, Koneru S. Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks. In: Hahn HK, Mazurowski MA, eds. Medical Imaging 2020: Computer-Aided Diagnosis. Proceedings of the SPIE. Volume 11314. SPIE; 2020:1131416.
554.Chapelle O, Vapnik VN, Bousquet O, Mukherjee SJML. Choosing multiple parameters for support vector machines. Machine Language. 2002;46(1):131-159. [Google Scholar]
555.Baydin AG, Pearlmutter BA, Siskind JM. Tricks from deep learning. arXiv. Preprint posted online November 10, 2016.
556.Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding deep learning (still) requires rethinking generalization. Commun ACM. 2021;64(3):107-115. doi: 10.1145/3446776 [DOI] [Google Scholar]
557.Breck E, Cai S, Nielsen E, Salib M, Sculley D. The ML test score: a rubric for ML production readiness and technical debt reduction. Proceedings of IEEE Big Data. 2017:1123-1132.
558.Sendak MP, Gao M, Brajer N, Balu S. Presenting machine learning model information to clinical end users with model facts labels. NPJ Digit Med. 2020;3(1):41. doi: 10.1038/s41746-020-0253-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
559.Heus P, Damen JAAG, Pajouheshnia R, et al. Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. BMC Med. 2018;16(1):120. doi: 10.1186/s12916-018-1099-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
560.Groot OQ, Ogink PT, Lans A, et al. Machine learning prediction models in orthopedic surgery: a systematic review in transparent reporting. J Orthop Res. 2022;40(2):475-483. doi: 10.1002/jor.25036 [DOI] [PMC free article] [PubMed] [Google Scholar]
561.Dhiman P, Ma J, Navarro CA, et al. Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. J Clin Epidemiol. 2021;138:60-72. doi: 10.1016/j.jclinepi.2021.06.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
562.Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11(7):e048008. doi: 10.1136/bmjopen-2020-048008 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eMethods 1. Searching Strategy and RSS Feed

eMethods 2. Literature Searching Pipeline and Data Extraction/Coding

eMethods 3. Geospatial Model

eMethods 4. PROBAST Assessment Criterion

eMethods 5. CLEAR Modification

eMethods 6. Cohen Kappa Test and Heterogeneity

eTable 1. Modified CLEAR Signaling Questions

eTable 2. Academic Background Distribution for Included Studies

eTable 3. Distribution for Psychiatric Categories in Existing AI Models

eTable 4. Distribution for AI Algorithms in These Models

eTable 5. Distribution for Toolkit of These AI Models

eTable 6. Distribution for Future Selection Methods of These AI Models

eTable 7. Distribution for Cross-Validation Regimes of These AI Models

eTable 8. Distribution for Neuroimaging-based Data Modality of These AI Models

eTable 9. Distribution for Preprocessing Method of These AI Models

eReferences

Click here for additional data file.^{(353.6KB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(18.1KB, pdf)}

[zoi230081r1] 1.Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises. Biol Psychiatry. 2020;88(11):818-828. doi: 10.1016/j.biopsych.2020.02.016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r2] 2.Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage. 2017;145(Pt B):137-165. doi: 10.1016/j.neuroimage.2016.02.079 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r3] 3.Eyre HA, Singh AB, Reynolds C III. Tech giants enter mental health. World Psychiatry. 2016;15(1):21-22. doi: 10.1002/wps.20297 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r4] 4.Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255-260. doi: 10.1126/science.aaa8415 [DOI] [PubMed] [Google Scholar]

[zoi230081r5] 5.Yu JS, Xue AY, Redei EE, Bagheri N. A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder. Transl Psychiatry. 2016;6(10):e931. doi: 10.1038/tp.2016.198 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r6] 6.Hazlett HC, Gu H, Munsell BC, et al. ; IBIS Network; Clinical Sites; Data Coordinating Center; Image Processing Core; Statistical Analysis . Early brain development in infants at high risk for autism spectrum disorder. Nature. 2017;542(7641):348-351. doi: 10.1038/nature21369 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r7] 7.Lv J, Di Biase M, Cash RFH, et al. Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort. Mol Psychiatry. 2021;26(7):3512-3523. doi: 10.1038/s41380-020-00882-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r8] 8.Nielsen AN, Barch DM, Petersen SE, Schlaggar BL, Greene DJ. Machine learning with neuroimaging: evaluating its applications in psychiatry. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5(8):791-798. doi: 10.1016/j.bpsc.2019.11.007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r9] 9.Varoquaux G, Raamana PR, Engemann DA, Hoyos-Idrobo A, Schwartz Y, Thirion B. Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage. 2017;145(pt B):166-179. doi: 10.1016/j.neuroimage.2016.10.038 [DOI] [PubMed] [Google Scholar]

[zoi230081r10] 10.Roberts M, Driggs D, Thorpe M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell. 2021;3:199-217. doi: 10.1038/s42256-021-00307-0 [DOI] [Google Scholar]

[zoi230081r11] 11.Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of Covid-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi: 10.1136/bmj.m1328 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r12] 12.Andaur Navarro CL, Damen JAA, Takada T, et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ. 2021;375(2281):n2281. doi: 10.1136/bmj.n2281 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r13] 13.Meehan AJ, Lewis SJ, Fazel S, et al. Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges. Mol Psychiatry. 2022;27(6):2700-2708. doi: 10.1038/s41380-022-01528-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r14] 14.Walter M, Alizadeh S, Jamalabadi H, et al. Translational machine learning for psychiatric neuroimaging. Prog Neuropsychopharmacol Biol Psychiatry. 2019;91:113-121. doi: 10.1016/j.pnpbp.2018.09.014 [DOI] [PubMed] [Google Scholar]

[zoi230081r15] 15.Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223-230. doi: 10.1016/j.bpsc.2017.11.007 [DOI] [PubMed] [Google Scholar]

[zoi230081r16] 16.Vasey B, Nagendran M, Campbell B, et al. ; DECIDE-AI expert group . Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022;28(5):924-933. doi: 10.1038/s41591-022-01772-9 [DOI] [PubMed] [Google Scholar]

[zoi230081r17] 17.Bossuyt PM, Reitsma JB, Bruns DE, et al. ; Standards for Reporting of Diagnostic Accuracy . Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ. 2003;326(7379):41-44. doi: 10.1136/bmj.326.7379.41 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r18] 18.Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. doi: 10.1136/bmj.g7594 [DOI] [PubMed] [Google Scholar]

[zoi230081r19] 19.Lu JH, Callahan A, Patel BS, et al. Assessment of adherence to reporting guidelines by commonly used clinical prediction models from a single vendor: a systematic review. JAMA Netw Open. 2022;5(8):e2227779. doi: 10.1001/jamanetworkopen.2022.27779 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r20] 20.Glasziou P, Altman DG, Bossuyt P, et al. Reducing waste from incomplete or unusable reports of biomedical research. Lancet. 2014;383(9913):267-276. doi: 10.1016/S0140-6736(13)62228-X [DOI] [PubMed] [Google Scholar]

[zoi230081r21] 21.Cai R, Wu X, Li C, Chao J. Prediction models for cardiovascular disease risk in the hypertensive population: a systematic review. J Hypertens. 2020;38(9):1632-1639. doi: 10.1097/HJH.0000000000002442 [DOI] [PubMed] [Google Scholar]

[zoi230081r22] 22.Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689. doi: 10.1136/bmj.m689 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r23] 23.Daneshjou R, Barata C, Betz-Stablein B, et al. Checklist for evaluation of image-based artificial intelligence reports in dermatology: CLEAR Derm consensus guidelines from the International Skin Imaging Collaboration Artificial Intelligence Working Group. JAMA Dermatol. 2022;158(1):90-96. doi: 10.1001/jamadermatol.2021.4915 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r24] 24.Wolff RF, Moons KGM, Riley RD, et al. ; PROBAST Group . PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51-58. doi: 10.7326/M18-1376 [DOI] [PubMed] [Google Scholar]

[zoi230081r25] 25.Knoth IS, Lajnef T, Rigoulot S, et al. Auditory repetition suppression alterations in relation to cognitive functioning in fragile X syndrome: a combined EEG and machine learning approach. J Neurodev Disord. 2018;10(1):4. doi: 10.1186/s11689-018-9223-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r26] 26.Pedersen M, Curwood EK, Archer JS, Abbott DF, Jackson GD. Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: multivariate analysis of task-free fMRI. Epilepsia. 2015;56(11):1767-1773. doi: 10.1111/epi.13135 [DOI] [PubMed] [Google Scholar]

[zoi230081r27] 27.Wang Y, Yuan L, Shi J, et al. Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis. Neuroimage. 2013;74:209-230. doi: 10.1016/j.neuroimage.2013.02.011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r28] 28.Hoeft F, Walter E, Lightbody AA, et al. Neuroanatomical differences in toddler boys with fragile x syndrome and idiopathic autism. Arch Gen Psychiatry. 2011;68(3):295-305. doi: 10.1001/archgenpsychiatry.2010.153 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r29] 29.Parisot S, Ktena SI, Ferrante E, et al. Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med Image Anal. 2018;48:117-130. doi: 10.1016/j.media.2018.06.001 [DOI] [PubMed] [Google Scholar]

[zoi230081r30] 30.Matlis S, Boric K, Chu CJ, Kramer MA. Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism. BMC Neurol. 2015;15:97. doi: 10.1186/s12883-015-0355-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r31] 31.Ingalhalikar M, Parker D, Bloy L, Roberts TP, Verma R. Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD. Neuroimage. 2011;57(3):918-927. doi: 10.1016/j.neuroimage.2011.05.023 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r32] 32.Shahamat H, Saniee Abadeh M. Brain MRI analysis using a deep learning based evolutionary approach. Neural Netw. 2020;126:218-234. doi: 10.1016/j.neunet.2020.03.017 [DOI] [PubMed] [Google Scholar]

[zoi230081r33] 33.Bajestani GS, Behrooz M, Khani AG, Nouri-Baygi M, Mollaei A. Diagnosis of autism spectrum disorder based on complex network features. Comput Methods Programs Biomed. 2019;177:277-283. doi: 10.1016/j.cmpb.2019.06.006 [DOI] [PubMed] [Google Scholar]

[zoi230081r34] 34.Lanka P, Rangaprakash D, Dretsch MN, Katz JS, Denney TS Jr, Deshpande G. Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging Behav. 2020;14(6):2378-2416. doi: 10.1007/s11682-019-00191-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r35] 35.Zhang L, Wang XH, Li L. Diagnosing autism spectrum disorder using brain entropy: a fast entropy method. Comput Methods Programs Biomed. 2020;190:105240. doi: 10.1016/j.cmpb.2019.105240 [DOI] [PubMed] [Google Scholar]

[zoi230081r36] 36.Rakić M, Cabezas M, Kushibar K, Oliver A, Lladó X. Improving the detection of autism spectrum disorder by combining structural and functional MRI information. Neuroimage Clin. 2020;25:102181. doi: 10.1016/j.nicl.2020.102181 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r37] 37.Ahmadlou M, Adeli H, Adeli A. Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder. J Clin Neurophysiol. 2010;27(5):328-333. doi: 10.1097/WNP.0b013e3181f40dc8 [DOI] [PubMed] [Google Scholar]

[zoi230081r38] 38.Libero LE, DeRamus TP, Lahti AC, Deshpande G, Kana RK. Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates. Cortex. 2015;66:46-59. doi: 10.1016/j.cortex.2015.02.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r39] 39.Pham TH, Vicnesh J, Wei JKE, et al. Autism spectrum disorder diagnostic system using HOS bispectrum with EEG signals. Int J Environ Res Public Health. 2020;17(3):971. doi: 10.3390/ijerph17030971 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r40] 40.Graa O, Rekik I. Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes. J Neurosci Methods. 2019;327:108344. doi: 10.1016/j.jneumeth.2019.108344 [DOI] [PubMed] [Google Scholar]

[zoi230081r41] 41.Ingalhalikar M, Smith AR, Bloy L, Gur R, Roberts TPL, Verma R. Identifying sub-populations via unsupervised cluster analysis on multi-edge similarity graphs. In: Ayache N, Delingette H, Golland P, Mori K, eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science. Vol 7511. Springer; 2012:254-261. [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r42] 42.Khosla M, Jamison K, Kuceyeski A, Sabuncu MR. Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction. Neuroimage. 2019;199:651-662. doi: 10.1016/j.neuroimage.2019.06.012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r43] 43.Li H, Parikh NA, He L. A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front Neurosci. 2018;12:491. doi: 10.3389/fnins.2018.00491 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r44] 44.Sen B, Borle NC, Greiner R, Brown MRG. A general prediction model for the detection of ADHD and autism using structural and functional MRI. PLoS One. 2018;13(4):e0194856. doi: 10.1371/journal.pone.0194856 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r45] 45.Xu L, Hua Q, Yu J, Li J. Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal. Clin Neurophysiol. 2020;131(6):1365-1374. doi: 10.1016/j.clinph.2019.12.400 [DOI] [PubMed] [Google Scholar]

[zoi230081r46] 46.Ma X, Wang XH, Li L. Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony. Neurosci Lett. 2021;742:135519. doi: 10.1016/j.neulet.2020.135519 [DOI] [PubMed] [Google Scholar]

[zoi230081r47] 47.Rakhimberdina Z, Liu X, Murata AT. Population graph-based multi-model ensemble method for diagnosing autism spectrum disorder. Sensors (Basel). 2020;20(21):6001. doi: 10.3390/s20216001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r48] 48.Tsiaras V, Simos PG, Rezaie R, et al. Extracting biomarkers of autism from MEG resting-state functional connectivity networks. Comput Biol Med. 2011;41(12):1166-1177. doi: 10.1016/j.compbiomed.2011.04.004 [DOI] [PubMed] [Google Scholar]

[zoi230081r49] 49.Wang H, Chen C, Fushing H. Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders. PLoS One. 2012;7(10):e45502. doi: 10.1371/journal.pone.0045502 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r50] 50.Hu J, Cao L, Li T, Liao B, Dong S, Li P. Interpretable learning approaches in resting-state functional connectivity analysis: the case of autism spectrum disorder. Comput Math Methods Med. 2020;2020:1394830. doi: 10.1155/2020/1394830 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r51] 51.Jung M, Tu Y, Park J, et al. Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder. Br J Psychiatry. 2019;214(6):339-344. doi: 10.1192/bjp.2018.248 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r52] 52.Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage Clin. 2017;17:16-23. doi: 10.1016/j.nicl.2017.08.017 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r53] 53.Bhaumik R, Pradhan A, Das S, Bhaumik DK. Predicting autism spectrum disorder using domain-adaptive cross-site evaluation. Neuroinformatics. 2018;16(2):197-205. doi: 10.1007/s12021-018-9366-0 [DOI] [PubMed] [Google Scholar]

[zoi230081r54] 54.Wang L, Wee CY, Tang X, Yap PT, Shen D. Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder. Brain Imaging Behav. 2016;10(1):33-40. doi: 10.1007/s11682-015-9360-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r55] 55.Ecker C, Rocha-Rego V, Johnston P, et al. ; MRC AIMS Consortium . Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage. 2010;49(1):44-56. doi: 10.1016/j.neuroimage.2009.08.024 [DOI] [PubMed] [Google Scholar]

[zoi230081r56] 56.Payabvash S, Palacios EM, Owen JP, et al. White matter connectome edge density in children with autism spectrum disorders: potential imaging biomarkers using machine-learning models. Brain Connect. 2019;9(2):209-220. doi: 10.1089/brain.2018.0658 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r57] 57.Price T, Wee CY, Gao W, Shen D. Multiple-network classification of childhood autism using functional connectivity dynamics. In: Golland P, Hata N, Barillot C, Hornegger J, Howe R, eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science. Vol 8675. Springer, Cham; 2014:177-184. [DOI] [PubMed] [Google Scholar]

[zoi230081r58] 58.Haweel R, Shalaby A, Mahmoud A, et al. A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI. Med Phys. 2021;48(5):2315-2326. doi: 10.1002/mp.14692 [DOI] [PubMed] [Google Scholar]

[zoi230081r59] 59.Chen CP, Keown CL, Jahedi A, et al. Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. Neuroimage Clin. 2015;8:238-245. doi: 10.1016/j.nicl.2015.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r60] 60.Anderson JS, Nielsen JA, Froehlich AL, et al. Functional connectivity magnetic resonance imaging classification of autism. Brain. 2011;134(Pt 12):3742-3754. doi: 10.1093/brain/awr263 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r61] 61.Uddin LQ, Supekar K, Lynch CJ, et al. Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry. 2013;70(8):869-879. doi: 10.1001/jamapsychiatry.2013.104 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r62] 62.Nielsen JA, Zielinski BA, Fletcher PT, et al. Multisite functional connectivity MRI classification of autism: ABIDE results. Front Hum Neurosci. 2013;7:599. doi: 10.3389/fnhum.2013.00599 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r63] 63.Jahedi A, Nasamran CA, Faires B, Fan J, Müller RA. Distributed intrinsic functional connectivity patterns predict diagnostic status in large autism cohort. Brain Connect. 2017;7(8):515-525. doi: 10.1089/brain.2017.0496 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r64] 64.Retico A, Giuliano A, Tancredi R, et al. The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study. Mol Autism. 2016;7:5. doi: 10.1186/s13229-015-0067-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r65] 65.Calderoni S, Retico A, Biagi L, Tancredi R, Muratori F, Tosetti M. Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses. Neuroimage. 2012;59(2):1013-1022. doi: 10.1016/j.neuroimage.2011.08.070 [DOI] [PubMed] [Google Scholar]

[zoi230081r66] 66.Yamagata B, Itahashi T, Fujino J, et al. Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder. Brain Imaging Behav. 2019;13(6):1689-1698. doi: 10.1007/s11682-018-9973-2 [DOI] [PubMed] [Google Scholar]

[zoi230081r67] 67.Gori I, Giuliano A, Muratori F, et al. Gray matter alterations in young children with autism spectrum disorders: comparing morphometry at the voxel and regional level. J Neuroimaging. 2015;25(6):866-874. doi: 10.1111/jon.12280 [DOI] [PubMed] [Google Scholar]

[zoi230081r68] 68.Leming M, Górriz JM, Suckling J. Ensemble deep learning on large, mixed-site fMRI datasets in autism and other tasks. Int J Neural Syst. 2020;30(7):2050012. doi: 10.1142/S0129065720500124 [DOI] [PubMed] [Google Scholar]

[zoi230081r69] 69.Shen MD, Nordahl CW, Li DD, et al. Extra-axial cerebrospinal fluid in high-risk and normal-risk children with autism aged 2-4 years: a case-control study. Lancet Psychiatry. 2018;5(11):895-904. doi: 10.1016/S2215-0366(18)30294-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r70] 70.Grossi E, Olivieri C, Buscema M. Diagnosis of autism through EEG processed by advanced computational algorithms: a pilot study. Comput Methods Programs Biomed. 2017;142:73-79. doi: 10.1016/j.cmpb.2017.02.002 [DOI] [PubMed] [Google Scholar]

[zoi230081r71] 71.Gupta S, Rajapakse JC, Welsch RE; Alzheimer’s Disease Neuroimaging Initiative . Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s disease and autism spectrum disorder. Neuroimage Clin. 2020;25:102186. doi: 10.1016/j.nicl.2020.102186 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r72] 72.Ghiassian S, Greiner R, Jin P, Brown MR. Using functional or structural magnetic resonance images and personal characteristic data to identify ADHD and autism. PLoS One. 2016;11(12):e0166934. doi: 10.1371/journal.pone.0166934 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r73] 73.Zu C, Gao Y, Munsell B, et al. Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning. Brain Imaging Behav. 2019;13(4):879-892. doi: 10.1007/s11682-018-9899-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r74] 74.Katuwal GJ, Baum SA, Cahill ND, Michael AM. Divide and conquer: sub-grouping of ASD improves ASD detection based on brain morphometry. PLoS One. 2016;11(4):e0153331. doi: 10.1371/journal.pone.0153331 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r75] 75.Li Q, Becker B, Jiang X, et al. Decreased interhemispheric functional connectivity rather than corpus callosum volume as a potential biomarker for autism spectrum disorder. Cortex. 2019;119:258-266. doi: 10.1016/j.cortex.2019.05.003 [DOI] [PubMed] [Google Scholar]

[zoi230081r76] 76.Dekhil O, Hajjdiab H, Shalaby A, et al. Using resting state functional MRI to build a personalized autism diagnosis system. PLoS One. 2018;13(10):e0206351. doi: 10.1371/journal.pone.0206351 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r77] 77.Yamagata B, Itahashi T, Fujino J, et al. Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder. Psychiatry Clin Neurosci. 2019;73(7):409-415. doi: 10.1111/pcn.12854 [DOI] [PubMed] [Google Scholar]

[zoi230081r78] 78.Iidaka T. Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex. 2015;63:55-67. doi: 10.1016/j.cortex.2014.08.011 [DOI] [PubMed] [Google Scholar]

[zoi230081r79] 79.Jamal W, Das S, Oprescu IA, Maharatna K, Apicella F, Sicca F. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. J Neural Eng. 2014;11(4):046019. doi: 10.1088/1741-2560/11/4/046019 [DOI] [PubMed] [Google Scholar]

[zoi230081r80] 80.Zhang F, Savadjiev P, Cai W, et al. Whole brain white matter connectivity analysis using machine learning: an application to autism. Neuroimage. 2018;172:826-837. doi: 10.1016/j.neuroimage.2017.10.029 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r81] 81.Sujit SJ, Coronado I, Kamali A, Narayana PA, Gabr RE. Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks. J Magn Reson Imaging. 2019;50(4):1260-1267. doi: 10.1002/jmri.26693 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r82] 82.Huang H, Liu X, Jin Y, Lee SW, Wee CY, Shen D. Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis. Hum Brain Mapp. 2019;40(3):833-854. doi: 10.1002/hbm.24415 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r83] 83.Eill A, Jahedi A, Gao Y, et al. Functional connectivities are more informative than anatomical variables in diagnostic classification of autism. Brain Connect. 2019;9(8):604-612. doi: 10.1089/brain.2019.0689 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r84] 84.Xiao X, Fang H, Wu J, et al. Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder. Autism Res. 2017;10(4):620-630. doi: 10.1002/aur.1711 [DOI] [PubMed] [Google Scholar]

[zoi230081r85] 85.Kam TE, Suk HI, Lee SW. Multiple functional networks modeling for autism spectrum disorder diagnosis. Hum Brain Mapp. 2017;38(11):5804-5821. doi: 10.1002/hbm.23769 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r86] 86.Ktena SI, Parisot S, Ferrante E, et al. Metric learning with spectral graph convolutions on brain connectivity networks. Neuroimage. 2018;169:431-442. doi: 10.1016/j.neuroimage.2017.12.052 [DOI] [PubMed] [Google Scholar]

[zoi230081r87] 87.Aghdam MA, Sharifi A, Pedram MM. Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks. J Digit Imaging. 2019;32(6):899-918. doi: 10.1007/s10278-019-00196-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r88] 88.Sadeghi M, Khosrowabadi R, Bakouie F, Mahdavi H, Eslahchi C, Pouretemad H. Screening of autism based on task-free fMRI using graph theoretical approach. Psychiatry Res Neuroimaging. 2017;263:48-56. doi: 10.1016/j.pscychresns.2017.02.004 [DOI] [PubMed] [Google Scholar]

[zoi230081r89] 89.Chaddad A, Desrosiers C, Hassan L, Tanougast C. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neurosci. 2017;18(1):52. doi: 10.1186/s12868-017-0373-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r90] 90.Djemal R, AlSharabi K, Ibrahim S, Alsuwailem A. EEG-based computer aided diagnosis of autism spectrum disorder using wavelet, entropy, and ANN. Biomed Res Int. 2017;2017:9816591. doi: 10.1155/2017/9816591 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r91] 91.Yahata N, Morimoto J, Hashimoto R, et al. A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat Commun. 2016;7:11254. doi: 10.1038/ncomms11254 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r92] 92.Heunis T, Aldrich C, Peters JM, et al. Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder: a systematic methodological exploration of technical and demographic confounders in the search for biomarkers. BMC Med. 2018;16(1):101. doi: 10.1186/s12916-018-1086-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r93] 93.Chen H, Duan X, Liu F, et al. Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity: a multi-center study. Prog Neuropsychopharmacol Biol Psychiatry. 2016;64:1-9. doi: 10.1016/j.pnpbp.2015.06.014 [DOI] [PubMed] [Google Scholar]

[zoi230081r94] 94.Just MA, Cherkassky VL, Buchweitz A, Keller TA, Mitchell TM. Identifying autism from neural representations of social interactions: neurocognitive markers of autism. PLoS One. 2014;9(12):e113879. doi: 10.1371/journal.pone.0113879 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r95] 95.Akhavan Aghdam M, Sharifi A, Pedram MM. Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J Digit Imaging. 2018;31(6):895-903. doi: 10.1007/s10278-018-0093-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r96] 96.Alturki FA, AlSharabi K, Abdurraqeeb AM, Aljalal M. EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques. Sensors (Basel). 2020;20(9):2505. doi: 10.3390/s20092505 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r97] 97.Spiegel A, Mentch J, Haskins AJ, Robertson CE. Slower binocular rivalry in the autistic brain. Curr Biol. 2019;29(17):2948-2953.e3. doi: 10.1016/j.cub.2019.07.026 [DOI] [PubMed] [Google Scholar]

[zoi230081r98] 98.Conti E, Retico A, Palumbo L, et al. Autism spectrum disorder and childhood apraxia of speech: early language-related hallmarks across structural MRI study. J Pers Med. 2020;10(4):275. doi: 10.3390/jpm10040275 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r99] 99.Bi XA, Liu Y, Jiang Q, Shu Q, Sun Q, Dai J. The diagnosis of autism spectrum disorder based on the random neural network cluster. Front Hum Neurosci. 2018;12:257. doi: 10.3389/fnhum.2018.00257 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r100] 100.Pollonini L, Patidar U, Situ N, Rezaie R, Papanicolaou AC, Zouridakis G. Functional connectivity networks in the autistic and healthy brain assessed using Granger causality. Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1730-1733. doi: 10.1109/IEMBS.2010.5626702 [DOI] [PubMed] [Google Scholar]

[zoi230081r101] 101.Eldridge J, Lane AE, Belkin M, Dennis S. Robust features for the automatic identification of autism spectrum disorder in children. J Neurodev Disord. 2014;6(1):12. doi: 10.1186/1866-1955-6-12 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r102] 102.Khan NA, Waheeb SA, Riaz A, Shang X. A three-stage teacher, student neural networks and sequential feed forward selection-based feature selection approach for the classification of autism spectrum disorder. Brain Sci. 2020;10(10):754. doi: 10.3390/brainsci10100754 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r103] 103.Sherkatghanad Z, Akhondzadeh M, Salari S, et al. Automated detection of autism spectrum disorder using a convolutional neural network. Front Neurosci. 2020;13:1325. doi: 10.3389/fnins.2019.01325 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r104] 104.Gao J, Chen M, Li Y, et al. Multisite autism spectrum disorder classification using convolutional neural network classifier and individual morphological brain networks. Front Neurosci. 2021;14:629630. doi: 10.3389/fnins.2020.629630 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r105] 105.Liu Y, Xu L, Li J, Yu J, Yu X. Attentional connectivity-based prediction of autism using heterogeneous rs-fMRI data from CC200 atlas. Exp Neurobiol. 2020;29(1):27-37. doi: 10.5607/en.2020.29.1.27 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r106] 106.Yang M, Cao M, Chen Y, et al. Large-scale brain functional network integration for discrimination of autism using a 3-D deep learning model. Front Hum Neurosci. 2021;15:687288. doi: 10.3389/fnhum.2021.687288 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r107] 107.Huang ZA, Zhu Z, Yau CH, Tan KC. Identifying autism spectrum disorder from resting-state fMRI using deep belief network. IEEE Trans Neural Netw Learn Syst. 2021;32(7):2847-2861. doi: 10.1109/TNNLS.2020.3007943 [DOI] [PubMed] [Google Scholar]

[zoi230081r108] 108.Zhao J, Song J, Li X, Kang J. A study on EEG feature extraction and classification in autistic children based on singular spectrum analysis method. Brain Behav. 2020;10(12):e01721. doi: 10.1002/brb3.1721 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r109] 109.Almuqhim F, Saeed F. ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data. Front Comput Neurosci. 2021;15:654315. doi: 10.3389/fncom.2021.654315 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r110] 110.Xu L, Sun Z, Xie J, Yu J, Li J, Wang J. Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network. Clin Neurophysiol. 2021;132(2):457-468. doi: 10.1016/j.clinph.2020.11.037 [DOI] [PubMed] [Google Scholar]

[zoi230081r111] 111.Lu J, Kishida K, De Asis Cruz J, et al. Single stimulus fMRI produces a neural individual difference measure for autism spectrum disorder. Clin Psychol Sci. 2015;3(3):422-432. doi: 10.1177/2167702614562042 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r112] 112.Ahmed MR, Zhang Y, Liu Y, Liao H. Single volume image generator and deep learning-based ASD classification. IEEE J Biomed Health Inform. 2020;24(11):3044-3054. doi: 10.1109/JBHI.2020.2998603 [DOI] [PubMed] [Google Scholar]

[zoi230081r113] 113.Xu L, Geng X, He X, Li J, Yu J. Prediction in autism by deep learning short-time spontaneous Hemodynamic fluctuations. Front Neurosci. 2019;13:1120. doi: 10.3389/fnins.2019.01120 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r114] 114.Wee CY, Wang L, Shi F, Yap PT, Shen D. Diagnosis of autism spectrum disorders using regional and interregional morphological features. Hum Brain Mapp. 2014;35(7):3414-3430. doi: 10.1002/hbm.22411 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r115] 115.Sewani H, Kashef R. An autoencoder-based deep learning classifier for efficient diagnosis of autism. Children (Basel). 2020;7(10):182. doi: 10.3390/children7100182 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r116] 116.Shi C, Xin X, Zhang J. Domain adaptation using a three-way decision improves the identification of autism patients from multisite fMRI data. Brain Sci. 2021;11(5):603. doi: 10.3390/brainsci11050603 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r117] 117.Kazeminejad A, Sotero RC. The importance of anti-correlations in graph theory based classification of autism spectrum disorder. Front Neurosci. 2020;14:676. doi: 10.3389/fnins.2020.00676 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r118] 118.Yin W, Mostafa S, Wu FX. Diagnosis of autism spectrum disorder based on functional brain networks with deep learning. J Comput Biol. 2021;28(2):146-165. doi: 10.1089/cmb.2020.0252 [DOI] [PubMed] [Google Scholar]

[zoi230081r119] 119.Jiao Y, Chen R, Ke X, Chu K, Lu Z, Herskovits EH. Predictive models of autism spectrum disorder based on brain regional cortical thickness. Neuroimage. 2010;50(2):589-599. doi: 10.1016/j.neuroimage.2009.12.047 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r120] 120.Murdaugh DL, Shinkareva SV, Deshpande HR, Wang J, Pennick MR, Kana RK. Differential deactivation during mentalizing and classification of autism based on default mode network connectivity. PLoS One. 2012;7(11):e50064. doi: 10.1371/journal.pone.0050064 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r121] 121.Song Y, Epalle TM, Lu H. Characterizing and predicting autism spectrum disorder by performing resting-state functional network community pattern analysis. Front Hum Neurosci. 2019;13:203. doi: 10.3389/fnhum.2019.00203 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r122] 122.Irimia A, Lei X, Torgerson CM, Jacokes ZJ, Abe S, Van Horn JD. Support vector machines, multidimensional scaling and magnetic resonance imaging reveal structural brain abnormalities associated with the interaction between autism spectrum disorder and sex. Front Comput Neurosci. 2018;12:93. doi: 10.3389/fncom.2018.00093 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r123] 123.Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F. ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front Neuroinform. 2019;13:70. doi: 10.3389/fninf.2019.00070 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r124] 124.Sarovic D, Hadjikhani N, Schneiderman J, Lundström S, Gillberg C. Autism classified by magnetic resonance imaging: a pilot study of a potential diagnostic tool. Int J Methods Psychiatr Res. 2020;29(4):1-18. doi: 10.1002/mpr.1846 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r125] 125.Kazeminejad A, Sotero RC. Topological properties of resting-state fMRI functional networks improve machine learning-based autism classification. Front Neurosci. 2019;12:1018. doi: 10.3389/fnins.2018.01018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r126] 126.Guo X, Dominick KC, Minai AA, Li H, Erickson CA, Lu LJ. Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method. Front Neurosci. 2017;11:460. doi: 10.3389/fnins.2017.00460 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r127] 127.Thomas RM, Gallo S, Cerliani L, Zhutovsky P, El-Gazzar A, van Wingen G. Classifying autism spectrum disorder using the temporal statistics of resting-state functional MRI data with 3D convolutional neural networks. Front Psychiatry. 2020;11:440. doi: 10.3389/fpsyt.2020.00440 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r128] 128.Li X, Zhou Y, Dvornek N, et al. BrainGNN: interpretable brain graph neural network for fMRI analysis. Med Image Anal. 2021;74:102233. doi: 10.1016/j.media.2021.102233 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r129] 129.Wang Z, Peng D, Shang Y, Gao J. Autistic spectrum disorder detection and structural biomarker identification using self-attention model and individual-level morphological covariance brain networks. Front Neurosci. 2021;15:756868. doi: 10.3389/fnins.2021.756868 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r130] 130.Haweel R, Seada N, Ghoniemy S, Alghamdi NS, El-Baz A. A CNN deep local and global ASD classification approach with continuous wavelet transform using task-based FMRI. Sensors (Basel). 2021;21(17):5822. doi: 10.3390/s21175822 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r131] 131.Squarcina L, Nosari G, Marin R, et al. Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine. Brain Behav. 2021;11(8):e2238. doi: 10.1002/brb3.2238 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r132] 132.Baygin M, Dogan S, Tuncer T, et al. Automated ASD detection using hybrid deep lightweight features extracted from EEG signals. Comput Biol Med. 2021;134:104548. doi: 10.1016/j.compbiomed.2021.104548 [DOI] [PubMed] [Google Scholar]

[zoi230081r133] 133.Yang C, Wang P, Tan J, Liu Q, Li X. Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks. Comput Biol Med. 2021;139:104963. doi: 10.1016/j.compbiomed.2021.104963 [DOI] [PubMed] [Google Scholar]

[zoi230081r134] 134.ElNakieb Y, Ali MT, Elnakib A, et al. The role of diffusion tensor MR imaging (DTI) of the brain in diagnosing autism spectrum disorder: promising results. Sensors (Basel). 2021;21(24):8171. doi: 10.3390/s21248171 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r135] 135.Okamoto N, Akama H. Extended invariant information clustering is effective for leave-one-site-out cross-validation in resting state functional connectivity modeling. Front Neuroinform. 2021;15:709179. doi: 10.3389/fninf.2021.709179 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r136] 136.Liao M, Duan H, Wang G. Application of machine learning techniques to detect the children with autism spectrum disorder. J Healthc Eng. 2022;2022:9340027. doi: 10.1155/2022/9340027 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r137] 137.Wang N, Yao D, Ma L, Liu M. Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI. Med Image Anal. 2022;75:102279. doi: 10.1016/j.media.2021.102279 [DOI] [PubMed] [Google Scholar]

[zoi230081r138] 138.Guo X, Wang J, Wang X, et al. Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms. Eur Radiol. 2022;32(2):761-770. doi: 10.1007/s00330-021-08239-4 [DOI] [PubMed] [Google Scholar]

[zoi230081r139] 139.Gao K, Fan Z, Su J, et al. Deep transfer learning for cerebral cortex using area-preserving geometry mapping. Cereb Cortex. 2022;32(14):2972-2984. doi: 10.1093/cercor/bhab394 [DOI] [PubMed] [Google Scholar]

[zoi230081r140] 140.Zhao L, Sun YK, Xue SW, Luo H, Lu XD, Zhang LH. Identifying boys with autism spectrum disorder based on whole-brain resting-state interregional functional connections using a boruta-based support vector machine approach. Front Neuroinform. 2022;16:761942. doi: 10.3389/fninf.2022.761942 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r141] 141.Shao L, Fu C, You Y, Fu D. Classification of ASD based on fMRI data with deep learning. Cogn Neurodyn. 2021;15(6):961-974. doi: 10.1007/s11571-021-09683-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r142] 142.Li L, Jiang H, Wen G, et al. TE-HI-GCN: an ensemble of transfer hierarchical graph convolutional networks for disorder diagnosis. Neuroinformatics. 2022;20(2):353-375. doi: 10.1007/s12021-021-09548-1 [DOI] [PubMed] [Google Scholar]

[zoi230081r143] 143.Germann J, Gouveia FV, Brentani H, et al. Involvement of the habenula in the pathophysiology of autism spectrum disorder. Sci Rep. 2021;11(1):21168. doi: 10.1038/s41598-021-00603-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r144] 144.Kang J, Jin Y, Liang G, Li X. Accurate assessment of low-function autistic children based on EEG feature fusion. J Clin Neurosci. 2021;90:351-358. doi: 10.1016/j.jocn.2021.06.022 [DOI] [PubMed] [Google Scholar]

[zoi230081r145] 145.Peng L, Liu X, Ma D, Chen X, Xu X, Gao X. The altered pattern of the functional connectome related to pathological biomarkers in individuals for autism spectrum disorder identification. Front Neurosci. 2022;16:913377. doi: 10.3389/fnins.2022.913377 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r146] 146.Hu J, Cao L, Li T, Dong S, Li P. GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification. BMC Bioinformatics. 2021;22(1):379. doi: 10.1186/s12859-021-04295-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r147] 147.Grossi E, Valbusa G, Buscema M. Detection of an autism EEG signature from only two EEG channels through features extraction and advanced machine learning analysis. Clin EEG Neurosci. 2021;52(5):330-337. doi: 10.1177/1550059420982424 [DOI] [PubMed] [Google Scholar]

[zoi230081r148] 148.Chen H, Chen W, Song Y, Sun L, Li X. EEG characteristics of children with attention-deficit/hyperactivity disorder. Neuroscience. 2019;406:444-456. doi: 10.1016/j.neuroscience.2019.03.048 [DOI] [PubMed] [Google Scholar]

[zoi230081r149] 149.Guo X, Yao D, Cao Q, et al. Shared and distinct resting functional connectivity in children and adults with attention-deficit/hyperactivity disorder. Transl Psychiatry. 2020;10(1):65. doi: 10.1038/s41398-020-0740-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r150] 150.Müller A, Vetsch S, Pershin I, et al. EEG/ERP-based biomarker/neuroalgorithms in adults with ADHD: Development, reliability, and application in clinical practice. World J Biol Psychiatry. 2020;21(3):172-182. doi: 10.1080/15622975.2019.1605198 [DOI] [PubMed] [Google Scholar]

[zoi230081r151] 151.Gao MS, Tsai FS, Lee CC. Learning a phenotypic-attribute attentional brain connectivity embedding for ADHD classification using rs-fMRI. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:5472-5475. doi: 10.1109/EMBC44109.2020.9175789 [DOI] [PubMed] [Google Scholar]

[zoi230081r152] 152.Chen Y, Tang Y, Wang C, Liu X, Zhao L, Wang Z. ADHD classification by dual subspace learning using resting-state functional connectivity. Artif Intell Med. 2020;103:101786. doi: 10.1016/j.artmed.2019.101786 [DOI] [PubMed] [Google Scholar]

[zoi230081r153] 153.Muthuraman M, Moliadze V, Boecher L, et al. Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders. Sci Rep. 2019;9(1):20028. doi: 10.1038/s41598-019-56398-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r154] 154.McNorgan C, Judson C, Handzlik D, Holden JG. Linking ADHD and behavioral assessment through identification of shared diagnostic task-based functional connections. Front Physiol. 2020;11:583005. doi: 10.3389/fphys.2020.583005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r155] 155.Vahid A, Bluschke A, Roessner V, Stober S, Beste C. Deep learning based on event-related EEG differentiates children with ADHD from healthy controls. J Clin Med. 2019;8(7):1055. doi: 10.3390/jcm8071055 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r156] 156.Riaz A, Asad M, Alonso E, Slabaugh G. DeepFMRI: end-to-end deep learning for functional connectivity and classification of ADHD using fMRI. J Neurosci Methods. 2020;335:108506. doi: 10.1016/j.jneumeth.2019.108506 [DOI] [PubMed] [Google Scholar]

[zoi230081r157] 157.Rostami M, Farashi S, Khosrowabadi R, Pouretemad H. Discrimination of ADHD subtypes using decision tree on behavioral, neuropsychological, and neural markers. Basic Clin Neurosci. 2020;11(3):359-367. [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r158] 158.Kiiski H, Rueda-Delgado LM, Bennett M, et al. Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms. Clin Neurophysiol. 2020;131(1):330-342. doi: 10.1016/j.clinph.2019.08.010 [DOI] [PubMed] [Google Scholar]

[zoi230081r159] 159.Tang Y, Wang C, Chen Y, Sun N, Jiang A, Wang Z. Identifying ADHD individuals from resting-state functional connectivity using subspace clustering and binary hypothesis testing. J Atten Disord. 2021;25(5):736-748. doi: 10.1177/1087054719837749 [DOI] [PubMed] [Google Scholar]

[zoi230081r160] 160.Sun Y, Zhao L, Lan Z, Jia XZ, Xue SW. Differentiating boys with ADHD from those with typical development based on whole-brain functional connections using a machine learning approach. Neuropsychiatr Dis Treat. 2020;16:691-702. doi: 10.2147/NDT.S239013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r161] 161.Sidhu G. Locally linear embedding and fMRI feature selection in psychiatric classification. IEEE J Transl Eng Health Med. 2019;7:2200211. doi: 10.1109/JTEHM.2019.2936348 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r162] 162.Riaz A, Asad M, Alonso E, Slabaugh G. Fusion of fMRI and non-imaging data for ADHD classification. Comput Med Imaging Graph. 2018;65:115-128. doi: 10.1016/j.compmedimag.2017.10.002 [DOI] [PubMed] [Google Scholar]

[zoi230081r163] 163.Kaur S, Singh S, Arun P, Kaur D, Bajaj M. Phase space reconstruction of EEG signals for classification of ADHD and control adults. Clin EEG Neurosci. 2020;51(2):102-113. doi: 10.1177/1550059419876525 [DOI] [PubMed] [Google Scholar]

[zoi230081r164] 164.Chen M, Li H, Wang J, Dillman JR, Parikh NA, He L. A multichannel deep neural network model analyzing multiscale functional brain connectome data for attention deficit hyperactivity disorder detection. Radiol Artif Intell. 2019;2(1):e190012. doi: 10.1148/ryai.2019190012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r165] 165.Sutoko S, Monden Y, Tokuda T, et al. Exploring attentive task-based connectivity for screening attention deficit/hyperactivity disorder children: a functional near-infrared spectroscopy study. Neurophotonics. 2019;6(4):045013. doi: 10.1117/1.NPh.6.4.045013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r166] 166.Luo Y, Alvarez TL, Halperin JM, Li X. Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques. Neuroimage Clin. 2020;26:102238. doi: 10.1016/j.nicl.2020.102238 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r167] 167.Wang XH, Jiao Y, Li L. Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity. Neurosci Lett. 2018;685:30-34. doi: 10.1016/j.neulet.2018.07.029 [DOI] [PubMed] [Google Scholar]

[zoi230081r168] 168.Peng X, Lin P, Zhang T, Wang J. Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS One. 2013;8(11):e79476. doi: 10.1371/journal.pone.0079476 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r169] 169.Yasumura A, Omori M, Fukuda A, et al. Applied machine learning method to predict children with ADHD using prefrontal cortex activity: a multicenter study in Japan. J Atten Disord. 2020;24(14):2012-2020. doi: 10.1177/1087054717740632 [DOI] [PubMed] [Google Scholar]

[zoi230081r170] 170.Qureshi MNI, Oh J, Min B, Jo HJ, Lee B. Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain MRI. Front Hum Neurosci. 2017;11:157. [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r171] 171.Biederman J, Hammerness P, Sadeh B, et al. Diagnostic utility of brain activity flow patterns analysis in attention deficit hyperactivity disorder. Psychol Med. 2017;47(7):1259-1270. doi: 10.1017/S0033291716003329 [DOI] [PubMed] [Google Scholar]

[zoi230081r172] 172.Gehricke JG, Kruggel F, Thampipop T, et al. The brain anatomy of attention-deficit/hyperactivity disorder in young adults: a magnetic resonance imaging study. PLoS One. 2017;12(4):e0175433. doi: 10.1371/journal.pone.0175433 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r173] 173.Sato JR, Hoexter MQ, Fujita A, Rohde LA. Evaluation of pattern recognition and feature extraction methods in ADHD prediction. Front Syst Neurosci. 2012;6:68. doi: 10.3389/fnsys.2012.00068 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r174] 174.Iannaccone R, Hauser TU, Ball J, Brandeis D, Walitza S, Brem S. Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging. Eur Child Adolesc Psychiatry. 2015;24(10):1279-1289. doi: 10.1007/s00787-015-0678-4 [DOI] [PubMed] [Google Scholar]

[zoi230081r175] 175.Gu Y, Miao S, Han J, et al. Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy. J Neural Eng. 2018;15(3):035005. doi: 10.1088/1741-2552/aa9ee9 [DOI] [PubMed] [Google Scholar]

[zoi230081r176] 176.Du J, Wang L, Jie B, Zhang D. Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA. Comput Med Imaging Graph. 2016;52:82-88. doi: 10.1016/j.compmedimag.2016.04.004 [DOI] [PubMed] [Google Scholar]

[zoi230081r177] 177.Wang XH, Jiao Y, Li L. Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity. Sci Rep. 2018;8(1):11789. doi: 10.1038/s41598-018-30308-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r178] 178.Hart H, Chantiluke K, Cubillo AI, et al. Pattern classification of response inhibition in ADHD: toward the development of neurobiological markers for ADHD. Hum Brain Mapp. 2014;35(7):3083-3094. doi: 10.1002/hbm.22386 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r179] 179.Dai D, Wang J, Hua J, He H. Classification of ADHD children through multimodal magnetic resonance imaging. Front Syst Neurosci. 2012;6:63. doi: 10.3389/fnsys.2012.00063 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r180] 180.Liu S, Zhao L, Wang X, et al. Deep spatio-temporal representation and ensemble classification for attention deficit/hyperactivity disorder. IEEE Trans Neural Syst Rehabil Eng. 2021;29:1-10. doi: 10.1109/TNSRE.2020.3019063 [DOI] [PubMed] [Google Scholar]

[zoi230081r181] 181.Wang X, Jiao Y, Tang T, Wang H, Lu Z. Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder. Eur J Radiol. 2013;82(9):1552-1557. doi: 10.1016/j.ejrad.2013.04.009 [DOI] [PubMed] [Google Scholar]

[zoi230081r182] 182.Sidhu GS, Asgarian N, Greiner R, Brown MR. Kernel principal component analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Front Syst Neurosci. 2012;6:74. doi: 10.3389/fnsys.2012.00074 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r183] 183.Liechti MD, Valko L, Müller UC, et al. Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan. Brain Topogr. 2013;26(1):135-151. doi: 10.1007/s10548-012-0258-6 [DOI] [PubMed] [Google Scholar]

[zoi230081r184] 184.Zhu CZ, Zang YF, Cao QJ, et al. Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. Neuroimage. 2008;40(1):110-120. doi: 10.1016/j.neuroimage.2007.11.029 [DOI] [PubMed] [Google Scholar]

[zoi230081r185] 185.Olivetti E, Greiner S, Avesani P. ADHD diagnosis from multiple data sources with batch effects. Front Syst Neurosci. 2012;6:70. doi: 10.3389/fnsys.2012.00070 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r186] 186.Johnston BA, Mwangi B, Matthews K, Coghill D, Konrad K, Steele JD. Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification. Hum Brain Mapp. 2014;35(10):5179-5189. doi: 10.1002/hbm.22542 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r187] 187.Aradhya AMS, Subbaraju V, Sundaram S, Sundararajan N. Regularized Spatial Filtering Method (R-SFM) for detection of attention deficit hyperactivity disorder (ADHD) from resting-state functional magnetic resonance imaging (rs-fMRI). Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:5541-5544. doi: 10.1109/EMBC.2018.8513522 [DOI] [PubMed] [Google Scholar]

[zoi230081r188] 188.Chang CW, Ho CC, Chen JH. ADHD classification by a texture analysis of anatomical brain MRI data. Front Syst Neurosci. 2012;6:66. doi: 10.3389/fnsys.2012.00066 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r189] 189.Smith JL, Johnstone SJ, Barry RJ. Aiding diagnosis of attention-deficit/hyperactivity disorder and its subtypes: discriminant function analysis of event-related potential data. J Child Psychol Psychiatry. 2003;44(7):1067-1075. doi: 10.1111/1469-7610.00191 [DOI] [PubMed] [Google Scholar]

[zoi230081r190] 190.dos Santos Siqueira A, Biazoli Junior CE, Comfort WE, Rohde LA, Sato JR. Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data. Biomed Res Int. 2014;2014:380531. doi: 10.1155/2014/380531 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r191] 191.Sun H, Chen Y, Huang Q, et al. Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: a radiomics analysis. Radiology. 2018;287(2):620-630. doi: 10.1148/radiol.2017170226 [DOI] [PubMed] [Google Scholar]

[zoi230081r192] 192.Mueller A, Candrian G, Kropotov JD, Ponomarev VA, Baschera GM. Classification of ADHD patients on the basis of independent ERP components using a machine learning system. Nonlinear Biomed Phys. 2010;4(Suppl 1)(suppl 1):S1. doi: 10.1186/1753-4631-4-S1-S1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r193] 193.Cheng W, Ji X, Zhang J, Feng J. Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques. Front Syst Neurosci. 2012;6:58. doi: 10.3389/fnsys.2012.00058 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r194] 194.Ahmadlou M, Adeli H. Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD. Clin EEG Neurosci. 2010;41(1):1-10. doi: 10.1177/155005941004100103 [DOI] [PubMed] [Google Scholar]

[zoi230081r195] 195.Abibullaev B, An J. Decision support algorithm for diagnosis of ADHD using electroencephalograms. J Med Syst. 2012;36(4):2675-2688. doi: 10.1007/s10916-011-9742-x [DOI] [PubMed] [Google Scholar]

[zoi230081r196] 196.Colby JB, Rudie JD, Brown JA, Douglas PK, Cohen MS, Shehzad Z. Insights into multimodal imaging classification of ADHD. Front Syst Neurosci. 2012;6:59. doi: 10.3389/fnsys.2012.00059 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r197] 197.Mueller A, Candrian G, Grane VA, Kropotov JD, Ponomarev VA, Baschera GM. Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study. Nonlinear Biomed Phys. 2011;5:5. doi: 10.1186/1753-4631-5-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r198] 198.Yu D. Additional brain functional network in adults with attention-deficit/hyperactivity disorder: a phase synchrony analysis. PLoS One. 2013;8(1):e54516. doi: 10.1371/journal.pone.0054516 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r199] 199.Poil SS, Bollmann S, Ghisleni C, et al. Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD). Clin Neurophysiol. 2014;125(8):1626-1638. doi: 10.1016/j.clinph.2013.12.118 [DOI] [PubMed] [Google Scholar]

[zoi230081r200] 200.Qureshi MN, Min B, Jo HJ, Lee B. Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: structural MRI study. PLoS One. 2016;11(8):e0160697. doi: 10.1371/journal.pone.0160697 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r201] 201.Hart H, Marquand AF, Smith A, et al. Predictive neurofunctional markers of attention-deficit/hyperactivity disorder based on pattern classification of temporal processing. J Am Acad Child Adolesc Psychiatry. 2014;53(5):569-78.e1. doi: 10.1016/j.jaac.2013.12.024 [DOI] [PubMed] [Google Scholar]

[zoi230081r202] 202.Qureshi MNI, Lee B. Classification of ADHD subgroup with recursive feature elimination for structural brain MRI. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:5929-5932. doi: 10.1109/EMBC.2016.7592078 [DOI] [PubMed] [Google Scholar]

[zoi230081r203] 203.Deshpande G, Wang P, Rangaprakash D, Wilamowski B. Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Trans Cybern. 2015;45(12):2668-2679. doi: 10.1109/TCYB.2014.2379621 [DOI] [PubMed] [Google Scholar]

[zoi230081r204] 204.Hammer R, Cooke GE, Stein MA, Booth JR. Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder. Neuroimage Clin. 2015;9:244-252. doi: 10.1016/j.nicl.2015.08.015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r205] 205.Moghaddari M, Lighvan MZ, Danishvar S. Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. Comput Methods Programs Biomed. 2020;197:105738. doi: 10.1016/j.cmpb.2020.105738 [DOI] [PubMed] [Google Scholar]

[zoi230081r206] 206.Pereda E, García-Torres M, Melián-Batista B, Mañas S, Méndez L, González JJ. The blessing of dimensionality: feature selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation. PLoS One. 2018;13(8):e0201660. doi: 10.1371/journal.pone.0201660 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r207] 207.Lim L, Marquand A, Cubillo AA, et al. Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging. PLoS One. 2013;8(5):e63660. doi: 10.1371/journal.pone.0063660 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r208] 208.Öztoprak H, Toycan M, Alp YK, Arıkan O, Doğutepe E, Karakaş S. Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity. Clin Neurophysiol. 2017;128(12):2400-2410. doi: 10.1016/j.clinph.2017.09.105 [DOI] [PubMed] [Google Scholar]

[zoi230081r209] 209.Tor HT, Ooi CP, Lim-Ashworth NS, et al. Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals. Comput Methods Programs Biomed. 2021;200:105941. doi: 10.1016/j.cmpb.2021.105941 [DOI] [PubMed] [Google Scholar]

[zoi230081r210] 210.Tosun M. Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning. Phys Eng Sci Med. 2021;44(3):693-702. doi: 10.1007/s13246-021-01018-x [DOI] [PubMed] [Google Scholar]

[zoi230081r211] 211.Johnstone SJ, Parrish L, Jiang H, Zhang DW, Williams V, Li S. Aiding diagnosis of childhood attention-deficit/hyperactivity disorder of the inattentive presentation: discriminant function analysis of multi-domain measures including EEG. Biol Psychol. 2021;161:108080. doi: 10.1016/j.biopsycho.2021.108080 [DOI] [PubMed] [Google Scholar]

[zoi230081r212] 212.Dey S, Rao AR, Shah M. Exploiting the brain’s network structure in identifying ADHD subjects. Front Syst Neurosci. 2012;6:75. doi: 10.3389/fnsys.2012.00075 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r213] 213.Yoo JH, Kim JI, Kim BN, Jeong B. Exploring characteristic features of attention-deficit/hyperactivity disorder: findings from multi-modal MRI and candidate genetic data. Brain Imaging Behav. 2020;14(6):2132-2147. doi: 10.1007/s11682-019-00164-x [DOI] [PubMed] [Google Scholar]

[zoi230081r214] 214.Tenev A, Markovska-Simoska S, Kocarev L, Pop-Jordanov J, Müller A, Candrian G. Machine learning approach for classification of ADHD adults. Int J Psychophysiol. 2014;93(1):162-166. doi: 10.1016/j.ijpsycho.2013.01.008 [DOI] [PubMed] [Google Scholar]

[zoi230081r215] 215.Rezaeezadeh M, Shamekhi S, Shamsi M. Attention deficit hyperactivity disorder diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study. Phys Eng Sci Med. 2020;43(2):577-592. doi: 10.1007/s13246-020-00858-3 [DOI] [PubMed] [Google Scholar]

[zoi230081r216] 216.Crippa A, Salvatore C, Molteni E, et al. The utility of a computerized algorithm based on a multi-domain profile of measures for the diagnosis of attention deficit/hyperactivity disorder. Front Psychiatry. 2017;8:189. doi: 10.3389/fpsyt.2017.00189 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r217] 217.Ishii-Takahashi A, Takizawa R, Nishimura Y, et al. Prefrontal activation during inhibitory control measured by near-infrared spectroscopy for differentiating between autism spectrum disorders and attention deficit hyperactivity disorder in adults. Neuroimage Clin. 2013;4:53-63. doi: 10.1016/j.nicl.2013.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r218] 218.Zhang T, Li C, Li P, et al. Separated channel attention convolutional neural network (SC-CNN-Attention) to identify ADHD in multi-site Rs-fMRI dataset. Entropy (Basel). 2020;22(8):893. doi: 10.3390/e22080893 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r219] 219.Abramov DM, Lazarev VV, Gomes Junior SC, et al. Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples. PeerJ. 2019;7:e7074. doi: 10.7717/peerj.7074 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r220] 220.Dey S, Rao AR, Shah M. Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects. Front Neural Circuits. 2014;8:64. doi: 10.3389/fncir.2014.00064 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r221] 221.Helgadóttir H, Gudmundsson ÓÓ, Baldursson G, et al. Electroencephalography as a clinical tool for diagnosing and monitoring attention deficit hyperactivity disorder: a cross-sectional study. BMJ Open. 2015;5(1):e005500. doi: 10.1136/bmjopen-2014-005500 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r222] 222.Chow JC, Ouyang CS, Tsai CL, et al. Entropy-based quantitative electroencephalogram analysis for diagnosing attention-deficit hyperactivity disorder in girls. Clin EEG Neurosci. 2019;50(3):172-179. doi: 10.1177/1550059418814983 [DOI] [PubMed] [Google Scholar]

[zoi230081r223] 223.Chen H, Song Y, Li X. Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD. J Neural Eng. 2019;16(6):066046. doi: 10.1088/1741-2552/ab3a0a [DOI] [PubMed] [Google Scholar]

[zoi230081r224] 224.Jahanshahloo HR, Shamsi M, Ghasemi E, Kouhi A. Automated and ERP-based diagnosis of attention-deficit hyperactivity disorder in children. J Med Signals Sens. 2017;7(1):26-32. doi: 10.4103/2228-7477.199152 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r225] 225.Wolfers T, van Rooij D, Oosterlaan J, et al. Quantifying patterns of brain activity: distinguishing unaffected siblings from participants with ADHD and healthy individuals. Neuroimage Clin. 2016;12:227-233. doi: 10.1016/j.nicl.2016.06.020 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r226] 226.Shao L, You Y, Du H, Fu D. Classification of ADHD with fMRI data and multi-objective optimization. Comput Methods Programs Biomed. 2020;196:105676. doi: 10.1016/j.cmpb.2020.105676 [DOI] [PubMed] [Google Scholar]

[zoi230081r227] 227.Itani S, Rossignol M, Lecron F, Fortemps P. Towards interpretable machine learning models for diagnosis aid: a case study on attention deficit/hyperactivity disorder. PLoS One. 2019;14(4):e0215720. doi: 10.1371/journal.pone.0215720 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r228] 228.Khoshnoud S, Nazari MA, Shamsi M. Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals. J Integr Neurosci. 2018;17(1):11-17. doi: 10.3233/JIN-170033 [DOI] [PubMed] [Google Scholar]

[zoi230081r229] 229.Chikara RK, Ko LW. Neural activities classification of human inhibitory control using hierarchical model. Sensors (Basel). 2019;19(17):3791. doi: 10.3390/s19173791 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r230] 230.Zhou X, Lin Q, Gui Y, Wang Z, Liu M, Lu H. Multimodal MR images-based diagnosis of early adolescent attention-deficit/hyperactivity disorder using multiple kernel learning. Front Neurosci. 2021;15:710133. doi: 10.3389/fnins.2021.710133 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r231] 231.Zhou D, Liao Z, Chen R. Deep learning enabled diagnosis of children’s ADHD based on the big data of video screen long-range EEG. J Healthc Eng. 2022;2022:5222136. doi: 10.1155/2022/5222136 [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]

[zoi230081r232] 232.Tang Y, Sun J, Wang C, et al. ADHD classification using auto-encoding neural network and binary hypothesis testing. Artif Intell Med. 2022;123:102209. doi: 10.1016/j.artmed.2021.102209 [DOI] [PubMed] [Google Scholar]

[zoi230081r233] 233.Catherine Joy R, Thomas George S, Albert Rajan A, Subathra MSP. Detection of ADHD from EEG signals using different entropy measures and ANN. Clin EEG Neurosci. 2022;53(1):12-23. doi: 10.1177/15500594211036788 [DOI] [PubMed] [Google Scholar]

[zoi230081r234] 234.Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y. A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage. 2022;246:118774. doi: 10.1016/j.neuroimage.2021.118774 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r235] 235.Wang Z, Zhu Y, Shi H, Zhang Y, Yan C. A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images. Math Biosci Eng. 2021;18(5):6978-6994. doi: 10.3934/mbe.2021347 [DOI] [PubMed] [Google Scholar]

[zoi230081r236] 236.Dinkel PJ, Willmes K, Krinzinger H, Konrad K, Koten JW Jr. Diagnosing developmental dyscalculia on the basis of reliable single case FMRI methods: promises and limitations. PLoS One. 2013;8(12):e83722. doi: 10.1371/journal.pone.0083722 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r237] 237.Zahia S, Garcia-Zapirain B, Saralegui I, Fernandez-Ruanova B. Dyslexia detection using 3D convolutional neural networks and functional magnetic resonance imaging. Comput Methods Programs Biomed. 2020;197:105726. doi: 10.1016/j.cmpb.2020.105726 [DOI] [PubMed] [Google Scholar]

[zoi230081r238] 238.Płoński P, Gradkowski W, Altarelli I, et al. Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia. Hum Brain Mapp. 2017;38(2):900-908. doi: 10.1002/hbm.23426 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r239] 239.Martinez-Murcia FJ, Ortiz A, Gorriz JM, et al. EEG connectivity analysis using denoising autoencoders for the detection of dyslexia. Int J Neural Syst. 2020;30(7):2050037. doi: 10.1142/S0129065720500379 [DOI] [PubMed] [Google Scholar]

[zoi230081r240] 240.Zainuddin AZA, Mansor W, Lee KY, Mahmoodin Z. Comparison of extreme learning machine and K-nearest neighbour performance in classifying EEG signal of normal, poor and capable dyslexic children. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:4513-4516. doi: 10.1109/EMBC.2019.8857569 [DOI] [PubMed] [Google Scholar]

[zoi230081r241] 241.Serrallach B, Groß C, Bernhofs V, et al. Neural biomarkers for dyslexia, ADHD, and ADD in the auditory cortex of children. Front Neurosci. 2016;10:324. doi: 10.3389/fnins.2016.00324 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r242] 242.Cui Z, Xia Z, Su M, Shu H, Gong G. Disrupted white matter connectivity underlying developmental dyslexia: a machine learning approach. Hum Brain Mapp. 2016;37(4):1443-1458. doi: 10.1002/hbm.23112 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r243] 243.García Chimeno Y, García Zapirain B, Saralegui Prieto I, Fernandez-Ruanova B. Automatic classification of dyslexic children by applying machine learning to fMRI images. Biomed Mater Eng. 2014;24(6):2995-3002. doi: 10.3233/BME-141120 [DOI] [PubMed] [Google Scholar]

[zoi230081r244] 244.Bailey S, Hoeft F, Aboud K, Cutting L. Anomalous gray matter patterns in specific reading comprehension deficit are independent of dyslexia. Ann Dyslexia. 2016;66(3):256-274. doi: 10.1007/s11881-015-0114-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r245] 245.Usman OL, Muniyandi RC, Omar K, Mohamad M. Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism. PLoS One. 2021;16(2):e0245579. doi: 10.1371/journal.pone.0245579 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r246] 246.Mascheretti S, Peruzzo D, Andreola C, et al. Selecting the most relevant brain regions to classify children with developmental dyslexia and typical readers by using complex magnocellular stimuli and multiple kernel learning. Brain Sci. 2021;11(6):722. doi: 10.3390/brainsci11060722 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r247] 247.Li A, Zalesky A, Yue W, et al. A neuroimaging biomarker for striatal dysfunction in schizophrenia. Nat Med. 2020;26(4):558-565. doi: 10.1038/s41591-020-0793-8 [DOI] [PubMed] [Google Scholar]

[zoi230081r248] 248.Jo YT, Joo SW, Shon SH, Kim H, Kim Y, Lee J. Diagnosing schizophrenia with network analysis and a machine learning method. Int J Methods Psychiatr Res. 2020;29(1):e1818. doi: 10.1002/mpr.1818 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r249] 249.Yassin W, Nakatani H, Zhu Y, et al. Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis. Transl Psychiatry. 2020;10(1):278. doi: 10.1038/s41398-020-00965-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r250] 250.Chen Z, Yan T, Wang E, et al. Detecting abnormal brain regions in schizophrenia using structural MRI via machine learning. Comput Intell Neurosci. 2020;2020:6405930. doi: 10.1155/2020/6405930 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r251] 251.Jahmunah V, Lih Oh S, Rajinikanth V, et al. Automated detection of schizophrenia using nonlinear signal processing methods. Artif Intell Med. 2019;100:101698. doi: 10.1016/j.artmed.2019.07.006 [DOI] [PubMed] [Google Scholar]

[zoi230081r252] 252.Wang L, Li X, Zhu Y, et al. Discriminative analysis of symptom severity and ultra-high risk of schizophrenia using intrinsic functional connectivity. Int J Neural Syst. 2020;30(9):2050047. doi: 10.1142/S0129065720500471 [DOI] [PubMed] [Google Scholar]

[zoi230081r253] 253.Mikolas P, Hlinka J, Skoch A, et al. Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy. BMC Psychiatry. 2018;18(1):97. doi: 10.1186/s12888-018-1678-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r254] 254.Schwarz E, Doan NT, Pergola G, et al. ; IMAGEMEND Consortium, Karolinska Schizophrenia Project (KaSP) Consortium . Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Transl Psychiatry. 2019;9(1):12. doi: 10.1038/s41398-018-0225-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r255] 255.Baradits M, Bitter I, Czobor P. Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls. Psychiatry Res. 2020;288:112938. doi: 10.1016/j.psychres.2020.112938 [DOI] [PubMed] [Google Scholar]

[zoi230081r256] 256.Kim J, Kim MY, Kwon H, et al. Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography. J Neurosci Methods. 2020;338:108688. doi: 10.1016/j.jneumeth.2020.108688 [DOI] [PubMed] [Google Scholar]

[zoi230081r257] 257.de Moura AM, Pinaya WHL, Gadelha A, et al. Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach. Psychiatry Res Neuroimaging. 2018;275:14-20. doi: 10.1016/j.pscychresns.2018.03.003 [DOI] [PubMed] [Google Scholar]

[zoi230081r258] 258.Yamamoto M, Bagarinao E, Kushima I, et al. Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites. PLoS One. 2020;15(11):e0239615. doi: 10.1371/journal.pone.0239615 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r259] 259.Zou H, Yang J. Dynamic thresholding networks for schizophrenia diagnosis. Artif Intell Med. 2019;96:25-32. doi: 10.1016/j.artmed.2019.03.007 [DOI] [PubMed] [Google Scholar]

[zoi230081r260] 260.Rozycki M, Satterthwaite TD, Koutsouleris N, et al. Multisite machine learning analysis provides a robust structural imaging signature of schizophrenia detectable across diverse patient populations and within individuals. Schizophr Bull. 2018;44(5):1035-1044. doi: 10.1093/schbul/sbx137 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r261] 261.Liang S, Deng W, Li X, et al. Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: a machine learning study. Schizophr Res. 2020;220:187-193. doi: 10.1016/j.schres.2020.03.022 [DOI] [PubMed] [Google Scholar]

[zoi230081r262] 262.Alamian G, Pascarella A, Lajnef T, et al. Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia. Neuroimage Clin. 2020;28:102485. doi: 10.1016/j.nicl.2020.102485 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r263] 263.de Pierrefeu A, Löfstedt T, Laidi C, et al. Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity. Acta Psychiatr Scand. 2018;138(6):571-580. doi: 10.1111/acps.12964 [DOI] [PubMed] [Google Scholar]

[zoi230081r264] 264.Di Carlo P, Pergola G, Antonucci LA, et al. Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals. Psychol Med. 2020;50(9):1501-1509. doi: 10.1017/S0033291719001430 [DOI] [PubMed] [Google Scholar]

[zoi230081r265] 265.Bae Y, Kumarasamy K, Ali IM, Korfiatis P, Akkus Z, Erickson BJ. Differences between schizophrenic and normal subjects using network properties from fMRI. J Digit Imaging. 2018;31(2):252-261. doi: 10.1007/s10278-017-0020-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r266] 266.Yu Y, Shen H, Zeng LL, Ma Q, Hu D. Convergent and divergent functional connectivity patterns in schizophrenia and depression. PLoS One. 2013;8(7):e68250. doi: 10.1371/journal.pone.0068250 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r267] 267.Dluhoš P, Schwarz D, Cahn W, et al. Multi-center machine learning in imaging psychiatry: a meta-model approach. Neuroimage. 2017;155:10-24. doi: 10.1016/j.neuroimage.2017.03.027 [DOI] [PubMed] [Google Scholar]

[zoi230081r268] 268.Lee J, Chon MW, Kim H, et al. Diagnostic value of structural and diffusion imaging measures in schizophrenia. Neuroimage Clin. 2018;18:467-474. doi: 10.1016/j.nicl.2018.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r269] 269.Antonucci LA, Penzel N, Pergola G, et al. Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity. Neuropsychopharmacology. 2020;45(4):613-621. doi: 10.1038/s41386-019-0532-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r270] 270.Lu X, Yang Y, Wu F, et al. Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images. Medicine (Baltimore). 2016;95(30):e3973. doi: 10.1097/MD.0000000000003973 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r271] 271.Chyzhyk D, Graña M, Öngür D, Shinn AK. Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI. Int J Neural Syst. 2015;25(3):1550007. doi: 10.1142/S0129065715500070 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r272] 272.Jing R, Li P, Ding Z, et al. Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients. Hum Brain Mapp. 2019;40(13):3930-3939. doi: 10.1002/hbm.24678 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r273] 273.Hua M, Peng Y, Zhou Y, Qin W, Yu C, Liang M. Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2020;99:109837. doi: 10.1016/j.pnpbp.2019.109837 [DOI] [PubMed] [Google Scholar]

[zoi230081r274] 274.Gould IC, Shepherd AM, Laurens KR, Cairns MJ, Carr VJ, Green MJ. Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: a support vector machine learning approach. Neuroimage Clin. 2014;6:229-236. doi: 10.1016/j.nicl.2014.09.009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r275] 275.Song H, Chen L, Gao R, et al. Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM. BMC Med Inform Decis Mak. 2017;17(suppl 3):166. doi: 10.1186/s12911-017-0559-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r276] 276.Zhu Q, Huang J, Xu X. Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI. Biomed Eng Online. 2018;17(1):32. doi: 10.1186/s12938-018-0464-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r277] 277.Zhao W, Guo S, Linli Z, Yang AC, Lin CP, Tsai SJ. Functional, anatomical, and morphological networks highlight the role of basal ganglia-thalamus-cortex circuits in schizophrenia. Schizophr Bull. 2020;46(2):422-431. [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r278] 278.Chin R, You AX, Meng F, Zhou J, Sim K. Recognition of schizophrenia with regularized support vector machine and sequential region of interest selection using structural magnetic resonance imaging. Sci Rep. 2018;8(1):13858. doi: 10.1038/s41598-018-32290-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r279] 279.Iwabuchi SJ, Palaniyappan L. Abnormalities in the effective connectivity of visuothalamic circuitry in schizophrenia. Psychol Med. 2017;47(7):1300-1310. doi: 10.1017/S0033291716003469 [DOI] [PubMed] [Google Scholar]

[zoi230081r280] 280.Winterburn JL, Voineskos AN, Devenyi GA, et al. Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? a multi-method and multi-dataset study. Schizophr Res. 2019;214:3-10. doi: 10.1016/j.schres.2017.11.038 [DOI] [PubMed] [Google Scholar]

[zoi230081r281] 281.Chen H, Uddin LQ, Duan X, et al. Shared atypical default mode and salience network functional connectivity between autism and schizophrenia. Autism Res. 2017;10(11):1776-1786. doi: 10.1002/aur.1834 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r282] 282.Xiao Y, Yan Z, Zhao Y, et al. Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI. Schizophr Res. 2019;214:11-17. doi: 10.1016/j.schres.2017.11.037 [DOI] [PubMed] [Google Scholar]

[zoi230081r283] 283.Zeng LL, Wang H, Hu P, et al. Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine. 2018;30:74-85. doi: 10.1016/j.ebiom.2018.03.017 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r284] 284.Supekar K, Cai W, Krishnadas R, Palaniyappan L, Menon V. Dysregulated brain dynamics in a triple-network saliency model of schizophrenia and its relation to psychosis. Biol Psychiatry. 2019;85(1):60-69. doi: 10.1016/j.biopsych.2018.07.020 [DOI] [PubMed] [Google Scholar]

[zoi230081r285] 285.Mastrovito D, Hanson C, Hanson SJ. Differences in atypical resting-state effective connectivity distinguish autism from schizophrenia. Neuroimage Clin. 2018;18:367-376. doi: 10.1016/j.nicl.2018.01.014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r286] 286.Liu Y, Zhang Y, Lv L, Wu R, Zhao J, Guo W. Abnormal neural activity as a potential biomarker for drug-naive first-episode adolescent-onset schizophrenia with coherence regional homogeneity and support vector machine analyses. Schizophr Res. 2018;192:408-415. doi: 10.1016/j.schres.2017.04.028 [DOI] [PubMed] [Google Scholar]

[zoi230081r287] 287.Cui LB, Liu L, Wang HN, et al. Disease definition for schizophrenia by functional connectivity using radiomics strategy. Schizophr Bull. 2018;44(5):1053-1059. doi: 10.1093/schbul/sby007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r288] 288.Pinaya WHL, Mechelli A, Sato JR. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: a large-scale multi-sample study. Hum Brain Mapp. 2019;40(3):944-954. doi: 10.1002/hbm.24423 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r289] 289.Chen X, Liu C, He H, et al. Transdiagnostic differences in the resting-state functional connectivity of the prefrontal cortex in depression and schizophrenia. J Affect Disord. 2017;217:118-124. doi: 10.1016/j.jad.2017.04.001 [DOI] [PubMed] [Google Scholar]

[zoi230081r290] 290.Schnack HG, Nieuwenhuis M, van Haren NE, et al. Can structural MRI aid in clinical classification? a machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. Neuroimage. 2014;84:299-306. doi: 10.1016/j.neuroimage.2013.08.053 [DOI] [PubMed] [Google Scholar]

[zoi230081r291] 291.Cheng H, Newman S, Goñi J, et al. Nodal centrality of functional network in the differentiation of schizophrenia. Schizophr Res. 2015;168(1-2):345-352. doi: 10.1016/j.schres.2015.08.011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r292] 292.Mikolas P, Melicher T, Skoch A, et al. Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study. Psychol Med. 2016;46(13):2695-2704. doi: 10.1017/S0033291716000878 [DOI] [PubMed] [Google Scholar]

[zoi230081r293] 293.Venkataraman A, Whitford TJ, Westin CF, Golland P, Kubicki M. Whole brain resting state functional connectivity abnormalities in schizophrenia. Schizophr Res. 2012;139(1-3):7-12. doi: 10.1016/j.schres.2012.04.021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r294] 294.Wang S, Zhang Y, Lv L, et al. Abnormal regional homogeneity as a potential imaging biomarker for adolescent-onset schizophrenia: a resting-state fMRI study and support vector machine analysis. Schizophr Res. 2018;192:179-184. doi: 10.1016/j.schres.2017.05.038 [DOI] [PubMed] [Google Scholar]

[zoi230081r295] 295.Cabral C, Kambeitz-Ilankovic L, Kambeitz J, et al. Classifying schizophrenia using multimodal multivariate pattern recognition analysis: evaluating the impact of individual clinical profiles on the neurodiagnostic performance. Schizophr Bull. 2016;42(Suppl 1)(suppl 1):S110-S117. doi: 10.1093/schbul/sbw053 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r296] 296.Ebdrup BH, Axelsen MC, Bak N, et al. Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naive schizophrenia patients. Psychol Med. 2019;49(16):2754-2763. doi: 10.1017/S0033291718003781 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r297] 297.Pinaya WH, Gadelha A, Doyle OM, et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci Rep. 2016;6:38897. doi: 10.1038/srep38897 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r298] 298.Kim J, Calhoun VD, Shim E, Lee JH. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage. 2016;124(Pt A):127-146. doi: 10.1016/j.neuroimage.2015.05.018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r299] 299.Viviano JD, Buchanan RW, Calarco N, et al. ; Social Processes Initiative in Neurobiology of the Schizophrenia(s) Group . Resting-state connectivity biomarkers of cognitive performance and social function in individuals with schizophrenia spectrum disorder and healthy control subjects. Biol Psychiatry. 2018;84(9):665-674. doi: 10.1016/j.biopsych.2018.03.013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r300] 300.Arbabshirani MR, Castro E, Calhoun VD. Accurate classification of schizophrenia patients based on novel resting-state fMRI features. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:6691-6694. doi: 10.1109/EMBC.2014.6945163 [DOI] [PubMed] [Google Scholar]

[zoi230081r301] 301.Yan W, Calhoun V, Song M, et al. Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data. EBioMedicine. 2019;47:543-552. doi: 10.1016/j.ebiom.2019.08.023 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r302] 302.Orban P, Dansereau C, Desbois L, et al. Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity. Schizophr Res. 2018;192:167-171. doi: 10.1016/j.schres.2017.05.027 [DOI] [PubMed] [Google Scholar]

[zoi230081r303] 303.Yu Y, Shen H, Zhang H, Zeng LL, Xue Z, Hu D. Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern analysis of resting-state fMRI from schizophrenic patients and their healthy siblings. Biomed Eng Online. 2013;12:10. doi: 10.1186/1475-925X-12-10 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r304] 304.Dillon K, Calhoun V, Wang YP. A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI. J Neurosci Methods. 2017;276:46-55. doi: 10.1016/j.jneumeth.2016.11.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r305] 305.Pergola G, Trizio S, Di Carlo P, et al. Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia. Schizophr Res. 2017;180:13-20. doi: 10.1016/j.schres.2016.07.005 [DOI] [PubMed] [Google Scholar]

[zoi230081r306] 306.Shen H, Wang L, Liu Y, Hu D. Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage. 2010;49(4):3110-3121. doi: 10.1016/j.neuroimage.2009.11.011 [DOI] [PubMed] [Google Scholar]

[zoi230081r307] 307.Chu WL, Huang MW, Jian BL, Hsu CY, Cheng KS. A correlative classification study of schizophrenic patients with results of clinical evaluation and structural magnetic resonance images. Behav Neurol. 2016;2016:7849526. doi: 10.1155/2016/7849526 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r308] 308.Masychev K, Ciprian C, Ravan M, Reilly JP, MacCrimmon D. Advanced signal processing methods for characterization of schizophrenia. IEEE Trans Biomed Eng. 2021;68(4):1123-1130. doi: 10.1109/TBME.2020.3011842 [DOI] [PubMed] [Google Scholar]

[zoi230081r309] 309.Korda AI, Ruef A, Neufang S, et al. Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions. Psychiatry Res Neuroimaging. 2021;313:111303. doi: 10.1016/j.pscychresns.2021.111303 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r310] 310.Pina-Camacho L, Garcia-Prieto J, Parellada M, et al. Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model. Eur Child Adolesc Psychiatry. 2015;24(4):427-440. doi: 10.1007/s00787-014-0593-0 [DOI] [PubMed] [Google Scholar]

[zoi230081r311] 311.Castro E, Martínez-Ramón M, Pearlson G, Sui J, Calhoun VD. Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia. Neuroimage. 2011;58(2):526-536. doi: 10.1016/j.neuroimage.2011.06.044 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r312] 312.Koch SP, Hägele C, Haynes JD, Heinz A, Schlagenhauf F, Sterzer P. Diagnostic classification of schizophrenia patients on the basis of regional reward-related FMRI signal patterns. PLoS One. 2015;10(3):e0119089. doi: 10.1371/journal.pone.0119089 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r313] 313.Castro E, Gupta CN, Martínez-Ramón M, Calhoun VD, Arbabshirani MR, Turner J. Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:1513-1516. doi: 10.1109/EMBC.2014.6943889 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r314] 314.Arribas JI, Calhoun VD, Adali T. Automatic bayesian classification of healthy controls, bipolar disorder, and schizophrenia using intrinsic connectivity maps from fMRI data. IEEE Trans Biomed Eng. 2010;57(12):2850-2860. doi: 10.1109/TBME.2010.2080679 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r315] 315.Yoon JH, Nguyen DV, McVay LM, et al. Automated classification of fMRI during cognitive control identifies more severely disorganized subjects with schizophrenia. Schizophr Res. 2012;135(1-3):28-33. doi: 10.1016/j.schres.2012.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r316] 316.Chyzhyk D, Savio A, Graña M. Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Netw. 2015;68:23-33. doi: 10.1016/j.neunet.2015.04.002 [DOI] [PubMed] [Google Scholar]

[zoi230081r317] 317.Wang P, Verma R. On classifying disease-induced patterns in the brain using diffusion tensor images. In: Metaxas D, Axel L, Fichtinger G, Székely G., eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science. Vol 5241. Springer; 2008:908-916. [DOI] [PubMed] [Google Scholar]

[zoi230081r318] 318.Ince NF, Goksu F, Pellizzer G, Tewfik A, Stephane M. Selection of spectro-temporal patterns in multichannel MEG with support vector machines for schizophrenia classification. Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:3554-3557. doi: 10.1109/IEMBS.2008.4649973 [DOI] [PubMed] [Google Scholar]

[zoi230081r319] 319.Sveinsson JR, Benediktsson JA, Stefansson SB, Davidsson K. Parallel principal component neural networks for classification of event-related potential waveforms. Med Eng Phys. 1997;19(1):15-20. doi: 10.1016/S1350-4533(96)00035-5 [DOI] [PubMed] [Google Scholar]

[zoi230081r320] 320.Calhas D, Romero E, Henriques R. On the use of pairwise distance learning for brain signal classification with limited observations. Artif Intell Med. 2020;105:101852. doi: 10.1016/j.artmed.2020.101852 [DOI] [PubMed] [Google Scholar]

[zoi230081r321] 321.Calhoun VD, Maciejewski PK, Pearlson GD, Kiehl KA. Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder. Hum Brain Mapp. 2008;29(11):1265-1275. doi: 10.1002/hbm.20463 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r322] 322.Neuhaus AH, Popescu FC, Bates JA, Goldberg TE, Malhotra AK. Single-subject classification of schizophrenia using event-related potentials obtained during auditory and visual oddball paradigms. Eur Arch Psychiatry Clin Neurosci. 2013;263(3):241-247. doi: 10.1007/s00406-012-0326-7 [DOI] [PubMed] [Google Scholar]

[zoi230081r323] 323.Xu T, Stephane M, Parhi KK. Abnormal neural oscillations in schizophrenia assessed by spectral power ratio of MEG during word processing. IEEE Trans Neural Syst Rehabil Eng. 2016;24(11):1148-1158. doi: 10.1109/TNSRE.2016.2551700 [DOI] [PubMed] [Google Scholar]

[zoi230081r324] 324.Ravan M, MacCrimmon D, Hasey G, Reilly JP, Khodayari-Rostamabad A. A machine learning approach using P300 responses to investigate effect of clozapine therapy. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5911-5914. doi: 10.1109/EMBC.2012.6347339 [DOI] [PubMed] [Google Scholar]

[zoi230081r325] 325.Castellani U, Rossato E, Murino V, et al. Classification of schizophrenia using feature-based morphometry. J Neural Transm (Vienna). 2012;119(3):395-404. doi: 10.1007/s00702-011-0693-7 [DOI] [PubMed] [Google Scholar]

[zoi230081r326] 326.Khare S, Bajaj V, Siuly S, Sinha PG. Classification of schizophrenia patients through empirical wavelet transformation using electroencephalogram signals. In: Bajaj V, Sinha GR, eds. Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1. IOP Publishing Ltd; 2020:1-26. [Google Scholar]

[zoi230081r327] 327.Yan W, Zhao M, Fu Z, Pearlson GD, Sui J, Calhoun VD. Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: a deep classification and clustering framework using fMRI time series. Schizophr Res. 2022;245:141-150. doi: 10.1016/j.schres.2021.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r328] 328.Du Y, Hao H, Wang S, Pearlson GD, Calhoun VD. Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis. Neuroimage Clin. 2020;27:102284. doi: 10.1016/j.nicl.2020.102284 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r329] 329.Oh K, Kim W, Shen G, et al. Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization. Schizophr Res. 2019;212:186-195. doi: 10.1016/j.schres.2019.07.034 [DOI] [PubMed] [Google Scholar]

[zoi230081r330] 330.Guo Y, Qiu J, Lu W. Support vector machine-based schizophrenia classification using morphological information from amygdaloid and hippocampal subregions. Brain Sci. 2020;10(8):562. doi: 10.3390/brainsci10080562 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r331] 331.Qureshi MNI, Oh J, Cho D, Jo HJ, Lee B. Multimodal discrimination of schizophrenia using hybrid weighted feature concatenation of brain functional connectivity and anatomical features with an extreme learning machine. Front Neuroinform. 2017;11:59. doi: 10.3389/fninf.2017.00059 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r332] 332.Gallos IK, Gkiatis K, Matsopoulos GK, Siettos C. ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with schizophrenia. AIMS Neurosci. 2021;8(2):295-321. doi: 10.3934/Neuroscience.2021016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r333] 333.Wang T, Bezerianos A, Cichocki A, Li J. Multikernel capsule network for schizophrenia identification. IEEE Trans Cybern. 2022;52(6):4741-4750. doi: 10.1109/TCYB.2020.3035282 [DOI] [PubMed] [Google Scholar]

[zoi230081r334] 334.Zang J, Huang Y, Kong L, et al. Effects of brain atlases and machine learning methods on the discrimination of schizophrenia patients: a multimodal MRI study. Front Neurosci. 2021;15:697168. doi: 10.3389/fnins.2021.697168 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r335] 335.Hu M, Qian X, Liu S, et al. Structural and diffusion MRI based schizophrenia classification using 2D pretrained and 3D naive convolutional neural networks. Schizophr Res. 2022;243:330-341. doi: 10.1016/j.schres.2021.06.011 [DOI] [PubMed] [Google Scholar]

[zoi230081r336] 336.Kim K, Duc NT, Choi M, Lee B. EEG microstate features for schizophrenia classification. PLoS One. 2021;16(5):e0251842. doi: 10.1371/journal.pone.0251842 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r337] 337.Salvador R, Canales-Rodríguez E, Guerrero-Pedraza A, et al. Multimodal integration of brain images for MRI-based diagnosis in schizophrenia. Front Neurosci. 2019;13:1203. doi: 10.3389/fnins.2019.01203 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r338] 338.Chou PH, Yao YH, Zheng RX, et al. Deep neural network to differentiate brain activity between patients with first-episode schizophrenia and healthy individuals: a multi-channel near infrared spectroscopy study. Front Psychiatry. 2021;12:655292. doi: 10.3389/fpsyt.2021.655292 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r339] 339.Li Z, Li W, Wei Y, et al. Deep learning based automatic diagnosis of first-episode psychosis, bipolar disorder and healthy controls. Comput Med Imaging Graph. 2021;89:101882. doi: 10.1016/j.compmedimag.2021.101882 [DOI] [PubMed] [Google Scholar]

[zoi230081r340] 340.Yang H, Liu J, Sui J, Pearlson G, Calhoun VD. A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia. Front Hum Neurosci. 2010;4:192. doi: 10.3389/fnhum.2010.00192 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r341] 341.Kalmady SV, Greiner R, Agrawal R, et al. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. NPJ Schizophr. 2019;5(1):2. doi: 10.1038/s41537-018-0070-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r342] 342.Liu W, Zhang X, Qiao Y, et al. Functional connectivity combined with a machine learning algorithm can classify high-risk first-degree relatives of patients with schizophrenia and identify correlates of cognitive impairments. Front Neurosci. 2020;14:577568. doi: 10.3389/fnins.2020.577568 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r343] 343.Bansal R, Staib LH, Laine AF, et al. Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses. PLoS One. 2012;7(12):e50698. doi: 10.1371/journal.pone.0050698 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r344] 344.Li F, Wang J, Liao Y, et al. Differentiation of schizophrenia by combining the spatial EEG brain network patterns of rest and task P300. IEEE Trans Neural Syst Rehabil Eng. 2019;27(4):594-602. doi: 10.1109/TNSRE.2019.2900725 [DOI] [PubMed] [Google Scholar]

[zoi230081r345] 345.Zhao Z, Li J, Niu Y, et al. Classification of schizophrenia by combination of brain effective and functional connectivity. Front Neurosci. 2021;15:651439. doi: 10.3389/fnins.2021.651439 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r346] 346.Park SM, Jeong B, Oh DY, et al. Identification of major psychiatric disorders from resting-state electroencephalography using a machine learning approach. Front Psychiatry. 2021;12:707581. doi: 10.3389/fpsyt.2021.707581 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r347] 347.Lei D, Pinaya WHL, Young J, et al. Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual. Hum Brain Mapp. 2020;41(5):1119-1135. doi: 10.1002/hbm.24863 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r348] 348.Kim JY, Lee HS, Lee SH. EEG source network for the diagnosis of schizophrenia and the identification of subtypes based on symptom severity-a machine learning approach. J Clin Med. 2020;9(12):3934. doi: 10.3390/jcm9123934 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r349] 349.Shi D, Li Y, Zhang H, et al. Machine learning of schizophrenia detection with structural and functional neuroimaging. Dis Markers. 2021;2021:9963824. doi: 10.1155/2021/9963824 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r350] 350.Shalbaf A, Bagherzadeh S, Maghsoudi A. Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med. 2020;43(4):1229-1239. doi: 10.1007/s13246-020-00925-9 [DOI] [PubMed] [Google Scholar]

[zoi230081r351] 351.Janousova E, Montana G, Kasparek T, Schwarz D. Supervised, multivariate, whole-brain reduction did not help to achieve high classification performance in schizophrenia research. Front Neurosci. 2016;10:392. doi: 10.3389/fnins.2016.00392 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r352] 352.Lieslehto J, Jääskeläinen E, Kiviniemi V, et al. The progression of disorder-specific brain pattern expression in schizophrenia over 9 years. NPJ Schizophr. 2021;7(1):32. doi: 10.1038/s41537-021-00157-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r353] 353.Ke PF, Xiong DS, Li JH, et al. An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data. Sci Rep. 2021;11(1):14636. doi: 10.1038/s41598-021-94007-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r354] 354.Gheiratmand M, Rish I, Cecchi GA, et al. Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms. NPJ Schizophr. 2017;3:22. doi: 10.1038/s41537-017-0022-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r355] 355.Chen J, Li X, Calhoun VD, et al. Sparse deep neural networks on imaging genetics for schizophrenia case-control classification. Hum Brain Mapp. 2021;42(8):2556-2568. doi: 10.1002/hbm.25387 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r356] 356.Oh J, Oh BL, Lee KU, Chae JH, Yun K. Identifying schizophrenia using structural MRI with a deep learning algorithm. Front Psychiatry. 2020;11:16. doi: 10.3389/fpsyt.2020.00016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r357] 357.Singh K, Singh S, Malhotra J. Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proc Inst Mech Eng H. 2021;235(2):167-184. doi: 10.1177/0954411920966937 [DOI] [PubMed] [Google Scholar]

[zoi230081r358] 358.Sun J, Cao R, Zhou M, et al. A hybrid deep neural network for classification of schizophrenia using EEG data. Sci Rep. 2021;11(1):4706. doi: 10.1038/s41598-021-83350-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r359] 359.Li YJ, Fan FY. Classification of schizophrenia and depression by EEG with ANNs*. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:2679-2682. [DOI] [PubMed] [Google Scholar]

[zoi230081r360] 360.Johannesen JK, Bi J, Jiang R, Kenney JG, Chen CA. Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatr Electrophysiol. 2016;2:3. doi: 10.1186/s40810-016-0017-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r361] 361.Jin K, Xu D, Shen Z, et al. Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis. Acta Neuropsychiatr. 2021;33(4):182-190. doi: 10.1017/neu.2021.9 [DOI] [PubMed] [Google Scholar]

[zoi230081r362] 362.Siuly S, Khare SK, Bajaj V, Wang H, Zhang Y. A computerized method for automatic detection of schizophrenia using EEG signals. IEEE Trans Neural Syst Rehabil Eng. 2020;28(11):2390-2400. doi: 10.1109/TNSRE.2020.3022715 [DOI] [PubMed] [Google Scholar]

[zoi230081r363] 363.Jang KI, Kim S, Kim SY, Lee C, Chae JH. Machine learning-based electroencephalographic phenotypes of schizophrenia and major depressive disorder. Front Psychiatry. 2021;12:745458. doi: 10.3389/fpsyt.2021.745458 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r364] 364.Zheng J, Wei X, Wang J, Lin H, Pan H, Shi Y. Diagnosis of schizophrenia based on deep learning using fMRI. Comput Math Methods Med. 2021;2021:8437260. doi: 10.1155/2021/8437260 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r365] 365.Geenjaar E, Lewis N, Fu Z, Venkatdas R, Plis S, Calhoun V. Fusing multimodal neuroimaging data with a variational autoencoder. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:3630-3633. doi: 10.1109/EMBC46164.2021.9630806 [DOI] [PubMed] [Google Scholar]

[zoi230081r366] 366.Najafzadeh H, Esmaeili M, Farhang S, Sarbaz Y, Rasta SH. Automatic classification of schizophrenia patients using resting-state EEG signals. Phys Eng Sci Med. 2021;44(3):855-870. doi: 10.1007/s13246-021-01038-7 [DOI] [PubMed] [Google Scholar]

[zoi230081r367] 367.Shoeibi A, Sadeghi D, Moridian P, et al. Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models. Front Neuroinform. 2021;15:777977. doi: 10.3389/fninf.2021.777977 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r368] 368.Khare SK, Bajaj V. A self-learned decomposition and classification model for schizophrenia diagnosis. Comput Methods Programs Biomed. 2021;211:106450. doi: 10.1016/j.cmpb.2021.106450 [DOI] [PubMed] [Google Scholar]

[zoi230081r369] 369.Lee LH, Chen CH, Chang WC, et al. Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants. Eur Psychiatry. 2021;65(1):e1. doi: 10.1192/j.eurpsy.2021.2248 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r370] 370.Wu J, Lyu G, Wang K, Tang X. A hybrid learning pipeline for automated diagnosis of first-episode schizophrenia utilizing T1-weighted images. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:2761-2764. doi: 10.1109/EMBC46164.2021.9630313 [DOI] [PubMed] [Google Scholar]

[zoi230081r371] 371.Hashimoto Y, Ogata Y, Honda M, Yamashita Y. Deep feature extraction for resting-state functional MRI by self-supervised learning and application to schizophrenia diagnosis. Front Neurosci. 2021;15:696853. doi: 10.3389/fnins.2021.696853 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r372] 372.Sharma M, Acharya UR. Automated detection of schizophrenia using optimal wavelet-based l1 norm features extracted from single-channel EEG. Cogn Neurodyn. 2021;15(4):661-674. doi: 10.1007/s11571-020-09655-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r373] 373.Al-Hiyali MI, Yahya N, Faye I, Hussein AF. Identification of autism subtypes based on wavelet coherence of BOLD fMRI signals using convolutional neural network. Sensors (Basel). 2021;21(16):5256. doi: 10.3390/s21165256 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r374] 374.Elad D, Cetin-Karayumak S, Zhang F, et al. Improving the predictive potential of diffusion MRI in schizophrenia using normative models-towards subject-level classification. Hum Brain Mapp. 2021;42(14):4658-4670. doi: 10.1002/hbm.25574 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r375] 375.Wu Y, Ren P, Chen R, et al. Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression. Brain Imaging Behav. 2022;16(1):281-290. doi: 10.1007/s11682-021-00501-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r376] 376.Cruz-Martinez C, Reyes-Garcia CA, Vanello N. A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis. Comput Methods Programs Biomed. 2022;213:106509. doi: 10.1016/j.cmpb.2021.106509 [DOI] [PubMed] [Google Scholar]

[zoi230081r377] 377.Algumaei AH, Algunaid RF, Rushdi MA, Yassine IA. Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data. PLoS One. 2022;17(5):e0265300. doi: 10.1371/journal.pone.0265300 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r378] 378.Tian Q, Yang NB, Fan Y, et al. Detection of schizophrenia cases from healthy controls with combination of neurocognitive and electrophysiological features. Front Psychiatry. 2022;13:810362. doi: 10.3389/fpsyt.2022.810362 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r379] 379.Prabhakar SK, Rajaguru H, Kim C, Won DO. A fusion-based technique with hybrid swarm algorithm and deep learning for biosignal classification. Front Hum Neurosci. 2022;16:895761. doi: 10.3389/fnhum.2022.895761 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r380] 380.Khare SK, Bajaj V. A hybrid decision support system for automatic detection of schizophrenia using EEG signals. Comput Biol Med. 2022;141:105028. doi: 10.1016/j.compbiomed.2021.105028 [DOI] [PubMed] [Google Scholar]

[zoi230081r381] 381.Zhou Z, Wang K, Tang J, et al. Cortical thickness distinguishes between major depression and schizophrenia in adolescents. BMC Psychiatry. 2021;21(1):361. doi: 10.1186/s12888-021-03373-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r382] 382.Li H, Cui L, Cao L, et al. Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques. BMC Psychiatry. 2020;20(1):488. doi: 10.1186/s12888-020-02886-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r383] 383.Linke JO, Adleman NE, Sarlls J, et al. White matter microstructure in pediatric bipolar disorder and disruptive mood dysregulation disorder. J Am Acad Child Adolesc Psychiatry. 2020;59(10):1135-1145. doi: 10.1016/j.jaac.2019.05.035 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r384] 384.Squarcina L, Dagnew TM, Rivolta MW, Bellani M, Sassi R, Brambilla P. Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method. J Affect Disord. 2019;256:416-423. doi: 10.1016/j.jad.2019.06.019 [DOI] [PubMed] [Google Scholar]

[zoi230081r385] 385.Matsuo K, Harada K, Fujita Y, et al. Distinctive neuroanatomical substrates for depression in bipolar disorder versus major depressive disorder. Cereb Cortex. 2019;29(1):202-214. doi: 10.1093/cercor/bhx319 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r386] 386.Frangou S, Dima D, Jogia J. Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder. Neuroimage. 2017;145(Pt B):230-237. doi: 10.1016/j.neuroimage.2016.08.066 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r387] 387.Doan NT, Kaufmann T, Bettella F, et al. Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders. Neuroimage Clin. 2017;15:719-731. doi: 10.1016/j.nicl.2017.06.014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r388] 388.Reavis EA, Lee J, Wynn JK, et al. Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data. Neuroimage Clin. 2017;16:491-497. doi: 10.1016/j.nicl.2017.08.023 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r389] 389.Mwangi B, Wu MJ, Bauer IE, et al. Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines. Psychiatry Res. 2015;234(2):265-271. doi: 10.1016/j.pscychresns.2015.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r390] 390.Mwangi B, Spiker D, Zunta-Soares GB, Soares JC. Prediction of pediatric bipolar disorder using neuroanatomical signatures of the amygdala. Bipolar Disord. 2014;16(7):713-721. doi: 10.1111/bdi.12222 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r391] 391.Costafreda SG, Fu CH, Picchioni M, et al. Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder. BMC Psychiatry. 2011;11:18. doi: 10.1186/1471-244X-11-18 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r392] 392.Rashid B, Arbabshirani MR, Damaraju E, et al. Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. Neuroimage. 2016;134:645-657. doi: 10.1016/j.neuroimage.2016.04.051 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r393] 393.Kaufmann T, Alnæs D, Brandt CL, et al. Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets. Neuroimage. 2017;147:243-252. doi: 10.1016/j.neuroimage.2016.11.073 [DOI] [PubMed] [Google Scholar]

[zoi230081r394] 394.Mourão-Miranda J, Almeida JR, Hassel S, et al. Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression. Bipolar Disord. 2012;14(4):451-460. doi: 10.1111/j.1399-5618.2012.01019.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r395] 395.Wu MJ, Mwangi B, Bauer IE, et al. Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning. Neuroimage. 2017;145(Pt B):254-264. doi: 10.1016/j.neuroimage.2016.02.016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r396] 396.Bürger C, Redlich R, Grotegerd D, et al. Differential abnormal pattern of anterior cingulate gyrus activation in unipolar and bipolar depression: an fMRI and pattern classification approach. Neuropsychopharmacology. 2017;42(7):1399-1408. doi: 10.1038/npp.2017.36 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r397] 397.Anticevic A, Cole MW, Repovs G, et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb Cortex. 2014;24(12):3116-3130. doi: 10.1093/cercor/bht165 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r398] 398.Besga A, Termenon M, Graña M, Echeveste J, Pérez JM, Gonzalez-Pinto A. Discovering Alzheimer’s disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neurosci Lett. 2012;520(1):71-76. doi: 10.1016/j.neulet.2012.05.033 [DOI] [PubMed] [Google Scholar]

[zoi230081r399] 399.Chen Y, Storrs J, Tan L, Mazlack LJ, Lee JH, Lu LJ. Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach. J Neurosci Methods. 2014;221:22-31. doi: 10.1016/j.jneumeth.2013.09.001 [DOI] [PubMed] [Google Scholar]

[zoi230081r400] 400.Xi C, Lai J, Du Y, et al. Abnormal functional connectivity within the reward network: a potential neuroimaging endophenotype of bipolar disorder. J Affect Disord. 2021;280(Pt B):49-56. doi: 10.1016/j.jad.2020.11.072 [DOI] [PubMed] [Google Scholar]

[zoi230081r401] 401.Teng S, Lu CF, Wang PS, et al. Classification of bipolar disorder using basal-ganglia-related functional connectivity in the resting state. Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:1057-1060. [DOI] [PubMed] [Google Scholar]

[zoi230081r402] 402.Nazhvani AD, Boostani R, Afrasiabi S, Sadatnezhad K. Classification of ADHD and BMD patients using visual evoked potential. Clin Neurol Neurosurg. 2013;115(11):2329-2335. doi: 10.1016/j.clineuro.2013.08.009 [DOI] [PubMed] [Google Scholar]

[zoi230081r403] 403.Chen YL, Tu PC, Huang TH, et al. Using minimal-redundant and maximal-relevant whole-brain functional connectivity to classify bipolar disorder. Front Neurosci. 2020;14:563368. doi: 10.3389/fnins.2020.563368 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r404] 404.Mwangi B, Wu MJ, Cao B, et al. Individualized prediction and clinical staging of bipolar disorders using neuroanatomical biomarkers. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(2):186-194. doi: 10.1016/j.bpsc.2016.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r405] 405.Qiu Y, Yang M, Li S, et al. Altered fractional amplitude of low-frequency fluctuation in major depressive disorder and bipolar disorder. Front Psychiatry. 2021;12:739210. doi: 10.3389/fpsyt.2021.739210 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r406] 406.Sánchez-Morla EM, Fuentes JL, Miguel-Jiménez JM, et al. Automatic diagnosis of bipolar disorder using optical coherence tomography data and artificial intelligence. J Pers Med. 2021;11(8):803. doi: 10.3390/jpm11080803 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r407] 407.Yamashita A, Sakai Y, Yamada T, et al. Generalizable brain network markers of major depressive disorder across multiple imaging sites. PLoS Biol. 2020;18(12):e3000966. doi: 10.1371/journal.pbio.3000966 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r408] 408.Maglanoc LA, Kaufmann T, Jonassen R, et al. Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. Hum Brain Mapp. 2020;41(1):241-255. doi: 10.1002/hbm.24802 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r409] 409.Shim M, Jin MJ, Im CH, Lee SH. Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features. Neuroimage Clin. 2019;24:102001. doi: 10.1016/j.nicl.2019.102001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r410] 410.Chun JY, Sendi MSE, Sui J, Zhi D, Calhoun VD. Visualizing functional network connectivity difference between healthy control and major depressive disorder using an explainable machine-learning method. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:1424-1427. doi: 10.1109/EMBC44109.2020.9175685 [DOI] [PubMed] [Google Scholar]

[zoi230081r411] 411.Uyulan C, Ergüzel TT, Unubol H, et al. Major depressive disorder classification based on different convolutional neural network models: deep learning approach. Clin EEG Neurosci. 2021;52(1):38-51. doi: 10.1177/1550059420916634 [DOI] [PubMed] [Google Scholar]

[zoi230081r412] 412.Shi Y, Zhang L, Wang Z, et al. Multivariate machine learning analyses in identification of major depressive disorder using resting-state functional connectivity: a multicentral study. ACS Chem Neurosci. 2021;12(15):2878-2886. doi: 10.1021/acschemneuro.1c00256 [DOI] [PubMed] [Google Scholar]

[zoi230081r413] 413.Mahato S, Goyal N, Ram D, Paul S. Detection of depression and scaling of severity using six channel EEG data. J Med Syst. 2020;44(7):118. doi: 10.1007/s10916-020-01573-y [DOI] [PubMed] [Google Scholar]

[zoi230081r414] 414.Guo H, Cao X, Liu Z, Li H, Chen J, Zhang K. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder. Neuroreport. 2012;23(17):1006-1011. doi: 10.1097/WNR.0b013e32835a650c [DOI] [PubMed] [Google Scholar]

[zoi230081r415] 415.Guo H, Li Y, Mensah GK, et al. Resting-state functional network scale effects and statistical significance-based feature selection in machine learning classification. Comput Math Methods Med. 2019;2019:9108108. doi: 10.1155/2019/9108108 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r416] 416.Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds CF III, Aizenstein HJ. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry. 2015;30(10):1056-1067. doi: 10.1002/gps.4262 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r417] 417.Yang J, Zhang M, Ahn H, et al. Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp. 2018;39(11):4420-4439. doi: 10.1002/hbm.24282 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r418] 418.Ahmadlou M, Adeli H, Adeli A. Fractality analysis of frontal brain in major depressive disorder. Int J Psychophysiol. 2012;85(2):206-211. doi: 10.1016/j.ijpsycho.2012.05.001 [DOI] [PubMed] [Google Scholar]

[zoi230081r419] 419.Sacchet MD, Livermore EE, Iglesias JE, Glover GH, Gotlib IH. Subcortical volumes differentiate major depressive disorder, bipolar disorder, and remitted major depressive disorder. J Psychiatr Res. 2015;68:91-98. doi: 10.1016/j.jpsychires.2015.06.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r420] 420.Ramasubbu R, Brown EC, Marcil LD, Talai AS, Forkert ND. Automatic classification of major depression disorder using arterial spin labeling MRI perfusion measurements. Psychiatry Clin Neurosci. 2019;73(8):486-493. doi: 10.1111/pcn.12862 [DOI] [PubMed] [Google Scholar]

[zoi230081r421] 421.Li H, Song S, Wang D, et al. Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features. BMC Psychiatry. 2021;21(1):415. doi: 10.1186/s12888-021-03414-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r422] 422.Zhong X, Shi H, Ming Q, et al. Whole-brain resting-state functional connectivity identified major depressive disorder: a multivariate pattern analysis in two independent samples. J Affect Disord. 2017;218:346-352. doi: 10.1016/j.jad.2017.04.040 [DOI] [PubMed] [Google Scholar]

[zoi230081r423] 423.Liu W, Zhang C, Wang X, et al. Functional connectivity of major depression disorder using ongoing EEG during music perception. Clin Neurophysiol. 2020;131(10):2413-2422. doi: 10.1016/j.clinph.2020.06.031 [DOI] [PubMed] [Google Scholar]

[zoi230081r424] 424.Liao SC, Wu CT, Huang HC, Cheng WT, Liu YH. Major depression detection from EEG signals using kernel Eigen-filter-bank common spatial patterns. Sensors (Basel). 2017;17(6):1385. doi: 10.3390/s17061385 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r425] 425.Rosa MJ, Portugal L, Hahn T, et al. Sparse network-based models for patient classification using fMRI. Neuroimage. 2015;105:493-506. doi: 10.1016/j.neuroimage.2014.11.021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r426] 426.Schnyer DM, Clasen PC, Gonzalez C, Beevers CG. Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Res Neuroimaging. 2017;264:1-9. doi: 10.1016/j.pscychresns.2017.03.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r427] 427.Johnston BA, Steele JD, Tolomeo S, Christmas D, Matthews K. Structural MRI-based predictions in patients with treatment-refractory depression (TRD). PLoS One. 2015;10(7):e0132958. doi: 10.1371/journal.pone.0132958 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r428] 428.Liu F, Guo W, Yu D, et al. Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans. PLoS One. 2012;7(7):e40968. doi: 10.1371/journal.pone.0040968 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r429] 429.Wei M, Qin J, Yan R, Li H, Yao Z, Lu Q. Identifying major depressive disorder using Hurst exponent of resting-state brain networks. Psychiatry Res. 2013;214(3):306-312. doi: 10.1016/j.pscychresns.2013.09.008 [DOI] [PubMed] [Google Scholar]

[zoi230081r430] 430.Chu SH, Lenglet C, Schreiner MW, Klimes-Dougan B, Cullen K, Parhi KK. Anatomical biomarkers for adolescent major depressive disorder from diffusion weighted imaging using SVM classifier. Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:2740-2743. doi: 10.1109/EMBC.2018.8512852 [DOI] [PubMed] [Google Scholar]

[zoi230081r431] 431.Guo H, Qin M, Chen J, Xu Y, Xiang J. Machine-learning classifier for patients with major depressive disorder: multifeature approach based on a high-order minimum spanning tree functional brain network. Comput Math Methods Med. 2017;2017:4820935. doi: 10.1155/2017/4820935 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r432] 432.Zeng LL, Shen H, Liu L, Hu D. Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp. 2014;35(4):1630-1641. doi: 10.1002/hbm.22278 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r433] 433.Fang P, Zeng LL, Shen H, et al. Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging. PLoS One. 2012;7(9):e45972. doi: 10.1371/journal.pone.0045972 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r434] 434.Tan W, Liu Z, Xi C, et al. Decreased integration of the frontoparietal network during a working memory task in major depressive disorder. Aust N Z J Psychiatry. 2021;55(6):577-587. doi: 10.1177/0004867420978284 [DOI] [PubMed] [Google Scholar]

[zoi230081r435] 435.Cao L, Guo S, Xue Z, et al. Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci. 2014;68(2):110-119. doi: 10.1111/pcn.12106 [DOI] [PubMed] [Google Scholar]

[zoi230081r436] 436.Zeng LL, Shen H, Liu L, et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain. 2012;135(Pt 5):1498-1507. doi: 10.1093/brain/aws059 [DOI] [PubMed] [Google Scholar]

[zoi230081r437] 437.Ramasubbu R, Brown MR, Cortese F, et al. Accuracy of automated classification of major depressive disorder as a function of symptom severity. Neuroimage Clin. 2016;12:320-331. doi: 10.1016/j.nicl.2016.07.012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r438] 438.Qiu L, Huang X, Zhang J, et al. Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images. J Psychiatry Neurosci. 2014;39(2):78-86. [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r439] 439.Mwangi B, Ebmeier KP, Matthews K, Steele JD. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain. 2012;135(Pt 5):1508-1521. doi: 10.1093/brain/aws084 [DOI] [PubMed] [Google Scholar]

[zoi230081r440] 440.Zhao J, Huang J, Zhi D, et al. Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders. J Neurosci Methods. 2020;341:108756. doi: 10.1016/j.jneumeth.2020.108756 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r441] 441.Bi K, Hua L, Wei M, Qin J, Lu Q, Yao Z. Dynamic functional-structural coupling within acute functional state change phases: evidence from a depression recognition study. J Affect Disord. 2016;191:145-155. doi: 10.1016/j.jad.2015.11.041 [DOI] [PubMed] [Google Scholar]

[zoi230081r442] 442.Zhu X, Yuan F, Zhou G, et al. Cross-network interaction for diagnosis of major depressive disorder based on resting state functional connectivity. Brain Imaging Behav. 2021;15(3):1279-1289. doi: 10.1007/s11682-020-00326-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r443] 443.Sundermann B, Feder S, Wersching H, et al. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm (Vienna). 2017;124(5):589-605. doi: 10.1007/s00702-016-1673-8 [DOI] [PubMed] [Google Scholar]

[zoi230081r444] 444.Qin J, Wei M, Liu H, et al. Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification. Magn Reson Imaging. 2014;32(10):1314-1320. doi: 10.1016/j.mri.2014.08.037 [DOI] [PubMed] [Google Scholar]

[zoi230081r445] 445.Guo M, Wang T, Zhang Z, et al. Diagnosis of major depressive disorder using whole-brain effective connectivity networks derived from resting-state functional MRI. J Neural Eng. 2020;17(5):056038. doi: 10.1088/1741-2552/abbc28 [DOI] [PubMed] [Google Scholar]

[zoi230081r446] 446.Khan D, Yahya N, Kamel N, Faye I. Automated diagnosis of major depressive disorder using brain effective connectivity and 3D convolutional neural network. IEEE Access. 2021;(9):8835-8846. doi: 10.1109/ACCESS.2021.3049427 [DOI] [Google Scholar]

[zoi230081r447] 447.Zhang B, Yan G, Yang Z, Su Y, Wang J, Lei T. Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification. IEEE Trans Neural Syst Rehabil Eng. 2021;29:215-229. doi: 10.1109/TNSRE.2020.3043426 [DOI] [PubMed] [Google Scholar]

[zoi230081r448] 448.Zhu Y, Jayagopal JK, Mehta RK, et al. Classifying major depressive disorder using fNIRS during motor rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2020;28(4):961-969. doi: 10.1109/TNSRE.2020.2972270 [DOI] [PubMed] [Google Scholar]

[zoi230081r449] 449.Geng X, Xu J, Liu B, Shi Y. Multivariate classification of major depressive disorder using the effective connectivity and functional connectivity. Front Neurosci. 2018;12:38. doi: 10.3389/fnins.2018.00038 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r450] 450.Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH. Elucidating brain connectivity networks in major depressive disorder using classification-based scoring. Proc IEEE Int Symp Biomed Imaging. 2014;2014:246-249. doi: 10.1109/ISBI.2014.6867855 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r451] 451.Yan B, Xu X, Liu M, et al. Quantitative identification of major depression based on resting-state dynamic functional connectivity: a machine learning approach. Front Neurosci. 2020;14:191. doi: 10.3389/fnins.2020.00191 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r452] 452.Nakano T, Takamura M, Ichikawa N, et al. Enhancing multi-center generalization of machine learning-based depression diagnosis from resting-state fMRI. Front Psychiatry. 2020;11:400. doi: 10.3389/fpsyt.2020.00400 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r453] 453.Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH. Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front Psychiatry. 2015;6:21. doi: 10.3389/fpsyt.2015.00021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r454] 454.Shi Y, Zhang L, He C, et al. Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: a multicenter study based on machine learning. J Affect Disord. 2021;295:148-155. doi: 10.1016/j.jad.2021.08.027 [DOI] [PubMed] [Google Scholar]

[zoi230081r455] 455.Duan L, Duan H, Qiao Y, et al. Machine learning approaches for MDD detection and emotion decoding using EEG signals. Front Hum Neurosci. 2020;14:284. doi: 10.3389/fnhum.2020.00284 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r456] 456.Saeedi A, Saeedi M, Maghsoudi A, Shalbaf A. Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn Neurodyn. 2021;15(2):239-252. doi: 10.1007/s11571-020-09619-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r457] 457.Bi K, Chattun MR, Liu X, et al. Abnormal early dynamic individual patterns of functional networks in low gamma band for depression recognition. J Affect Disord. 2018;238:366-374. doi: 10.1016/j.jad.2018.05.078 [DOI] [PubMed] [Google Scholar]

[zoi230081r458] 458.Qin J, Wei M, Liu H, et al. Altered anatomical patterns of depression in relation to antidepressant treatment: evidence from a pattern recognition analysis on the topological organization of brain networks. J Affect Disord. 2015;180:129-137. doi: 10.1016/j.jad.2015.03.059 [DOI] [PubMed] [Google Scholar]

[zoi230081r459] 459.Knott V, Mahoney C, Kennedy S, Evans K. EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res. 2001;106(2):123-140. doi: 10.1016/S0925-4927(00)00080-9 [DOI] [PubMed] [Google Scholar]

[zoi230081r460] 460.Marquand AF, Mourão-Miranda J, Brammer MJ, Cleare AJ, Fu CH. Neuroanatomy of verbal working memory as a diagnostic biomarker for depression. Neuroreport. 2008;19(15):1507-1511. doi: 10.1097/WNR.0b013e328310425e [DOI] [PubMed] [Google Scholar]

[zoi230081r461] 461.Lu Q, Bi K, Liu C, Luo G, Tang H, Yao Z. Predicting depression based on dynamic regional connectivity: a windowed Granger causality analysis of MEG recordings. Brain Res. 2013;1535:52-60. doi: 10.1016/j.brainres.2013.08.033 [DOI] [PubMed] [Google Scholar]

[zoi230081r462] 462.Craddock RC, Holtzheimer PE III, Hu XP, Mayberg HS. Disease state prediction from resting state functional connectivity. Magn Reson Med. 2009;62(6):1619-1628. doi: 10.1002/mrm.22159 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r463] 463.Kang M, Kwon H, Park JH, Kang S, Lee Y. Deep-asymmetry: asymmetry matrix image for deep learning method in pre-screening depression. Sensors (Basel). 2020;20(22):6526. doi: 10.3390/s20226526 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r464] 464.Lois G, Wessa M. Differential association of default mode network connectivity and rumination in healthy individuals and remitted MDD patients. Soc Cogn Affect Neurosci. 2016;11(11):1792-1801. doi: 10.1093/scan/nsw085 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r465] 465.Hasanzadeh F, Mohebbi M, Rostami R. Graph theory analysis of directed functional brain networks in major depressive disorder based on EEG signal. J Neural Eng. 2020;17(2):026010. doi: 10.1088/1741-2552/ab7613 [DOI] [PubMed] [Google Scholar]

[zoi230081r466] 466.Mumtaz W, Ali SSA, Yasin MAM, Malik AS. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput. 2018;56(2):233-246. doi: 10.1007/s11517-017-1685-z [DOI] [PubMed] [Google Scholar]

[zoi230081r467] 467.Sen B, Cullen KR, Parhi KK. Classification of adolescent major depressive disorder via static and dynamic connectivity. IEEE J Biomed Health Inform. 2021;25(7):2604-2614. doi: 10.1109/JBHI.2020.3043427 [DOI] [PubMed] [Google Scholar]

[zoi230081r468] 468.Movahed RA, Jahromi GP, Shahyad S, Meftahi GH. A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis. J Neurosci Methods. 2021;358:109209. doi: 10.1016/j.jneumeth.2021.109209 [DOI] [PubMed] [Google Scholar]

[zoi230081r469] 469.Li Y, Dai X, Wu H, Wang L. Establishment of effective biomarkers for depression diagnosis with fusion of multiple resting-state connectivity measures. Front Neurosci. 2021;15:729958. doi: 10.3389/fnins.2021.729958 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r470] 470.Wu CT, Huang HC, Huang S, et al. Resting-state EEG signal for major depressive disorder detection: a systematic validation on a large and diverse dataset. Biosensors (Basel). 2021;11(12):499. doi: 10.3390/bios11120499 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r471] 471.Gao Y, Wang X, Xiong Z, et al. Abnormal fractional amplitude of low-frequency fluctuation as a potential imaging biomarker for first-episode major depressive disorder: a resting-state fMRI study and support vector machine analysis. Front Neurol. 2021;12:751400. doi: 10.3389/fneur.2021.751400 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r472] 472.Lin H, Jian C, Cao Y, et al. MDD-TSVM: a novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals. Comput Biol Med. 2021;140:105039. doi: 10.1016/j.compbiomed.2021.105039 [DOI] [PubMed] [Google Scholar]

[zoi230081r473] 473.Liu B, Chang H, Peng K, Wang X. An end-to-end depression recognition method based on EEGNet. Front Psychiatry. 2022;13:864393. doi: 10.3389/fpsyt.2022.864393 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r474] 474.Frick A, Gingnell M, Marquand AF, et al. Classifying social anxiety disorder using multivoxel pattern analyses of brain function and structure. Behav Brain Res. 2014;259:330-335. doi: 10.1016/j.bbr.2013.11.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r475] 475.Zhang W, Yang X, Lui S, et al. Diagnostic prediction for social anxiety disorder via multivariate pattern analysis of the regional homogeneity. Biomed Res Int. 2015;2015:763965. doi: 10.1155/2015/763965 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r476] 476.Liu F, Guo W, Fouche JP, et al. Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Struct Funct. 2015;220(1):101-115. doi: 10.1007/s00429-013-0641-4 [DOI] [PubMed] [Google Scholar]

[zoi230081r477] 477.Xing M, Fitzgerald JM, Klumpp H. Classification of social anxiety disorder with support vector machine analysis using neural correlates of social signals of threat. Front Psychiatry. 2020;11:144. doi: 10.3389/fpsyt.2020.00144 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r478] 478.Gavrilescu M, Vizireanu N. Predicting depression, anxiety, and stress levels from videos using the facial action coding system. Sensors (Basel). 2019;19(17):3693. doi: 10.3390/s19173693 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r479] 479.Xie Y, Yang B, Lu X, et al. Anxiety and depression diagnosis method based on brain networks and convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:1503-1506. doi: 10.1109/EMBC44109.2020.9176471 [DOI] [PubMed] [Google Scholar]

[zoi230081r480] 480.Qiao J, Li A, Cao C, Wang Z, Sun J, Xu G. Aberrant functional network connectivity as a biomarker of generalized anxiety disorder. Front Hum Neurosci. 2017;11:626. doi: 10.3389/fnhum.2017.00626 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r481] 481.Xing X, Jin L, Li Q, et al. Modeling essential connections in obsessive-compulsive disorder patients using functional MRI. Brain Behav. 2020;10(2):e01499. doi: 10.1002/brb3.1499 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r482] 482.Yang X, Hu X, Tang W, et al. Multivariate classification of drug-naive obsessive-compulsive disorder patients and healthy controls by applying an SVM to resting-state functional MRI data. BMC Psychiatry. 2019;19(1):210. doi: 10.1186/s12888-019-2184-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r483] 483.Bruin WB, Taylor L, Thomas RM, et al. ; ENIGMA-OCD Working Group . Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters. Transl Psychiatry. 2020;10(1):342. doi: 10.1038/s41398-020-01013-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r484] 484.Takagi Y, Sakai Y, Lisi G, et al. A neural marker of obsessive-compulsive disorder from whole-brain functional connectivity. Sci Rep. 2017;7(1):7538. doi: 10.1038/s41598-017-07792-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r485] 485.Zhou C, Cheng Y, Ping L, et al. Support vector machine classification of obsessive-compulsive disorder based on whole-brain volumetry and diffusion tensor imaging. Front Psychiatry. 2018;9:524. doi: 10.3389/fpsyt.2018.00524 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r486] 486.Hu X, Liu Q, Li B, et al. Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy. Eur Neuropsychopharmacol. 2016;26(2):246-254. doi: 10.1016/j.euroneuro.2015.12.014 [DOI] [PubMed] [Google Scholar]

[zoi230081r487] 487.Bu X, Hu X, Zhang L, et al. Investigating the predictive value of different resting-state functional MRI parameters in obsessive-compulsive disorder. Transl Psychiatry. 2019;9(1):17. doi: 10.1038/s41398-018-0362-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r488] 488.Sen B, Bernstein GA, Tingting X, et al. Classification of obsessive-compulsive disorder from resting-state fMRI. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:3606-3609. doi: 10.1109/EMBC.2016.7591508 [DOI] [PubMed] [Google Scholar]

[zoi230081r489] 489.Trambaiolli LR, Biazoli CE Jr, Balardin JB, Hoexter MQ, Sato JR. The relevance of feature selection methods to the classification of obsessive-compulsive disorder based on volumetric measures. J Affect Disord. 2017;222:49-56. doi: 10.1016/j.jad.2017.06.061 [DOI] [PubMed] [Google Scholar]

[zoi230081r490] 490.Li F, Huang X, Tang W, et al. Multivariate pattern analysis of DTI reveals differential white matter in individuals with obsessive-compulsive disorder. Hum Brain Mapp. 2014;35(6):2643-2651. doi: 10.1002/hbm.22357 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r491] 491.Shenas SK, Halici U, Çiçek M. A comparative analysis of functional connectivity data in resting and task-related conditions of the brain for disease signature of OCD. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:978-981. doi: 10.1109/EMBC.2014.6943756 [DOI] [PubMed] [Google Scholar]

[zoi230081r492] 492.Liu J, Bu X, Hu X, et al. Temporal variability of regional intrinsic neural activity in drug-naïve patients with obsessive-compulsive disorder. Hum Brain Mapp. 2021;42(12):3792-3803. doi: 10.1002/hbm.25465 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r493] 493.Liu W, Hua M, Qin J, et al. Disrupted pathways from frontal-parietal cortex to basal ganglia and cerebellum in patients with unmedicated obsessive compulsive disorder as observed by whole-brain resting-state effective connectivity analysis: a small sample pilot study. Brain Imaging Behav. 2021;15(3):1344-1354. doi: 10.1007/s11682-020-00333-3 [DOI] [PubMed] [Google Scholar]

[zoi230081r494] 494.Chen Y, Ou Y, Lv D, et al. Decreased nucleus accumbens connectivity at rest in medication-free patients with obsessive-compulsive disorder. Neural Plast. 2021;2021:9966378. doi: 10.1155/2021/9966378 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r495] 495.Kalatzis I, Piliouras N, Glotsos D, et al. Identifying differences in the P600 component of ERP-signals between OCD patients and controls employing a PNN-based majority vote classification scheme. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:3994-3997. doi: 10.1109/IEMBS.2005.1615337 [DOI] [PubMed] [Google Scholar]

[zoi230081r496] 496.Aydin S, Arica N, Ergul E, Tan O. Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements. Int J Neural Syst. 2015;25(3):1550010. doi: 10.1142/S0129065715500100 [DOI] [PubMed] [Google Scholar]

[zoi230081r497] 497.Zilcha-Mano S, Zhu X, Suarez-Jimenez B, et al. Diagnostic and predictive neuroimaging biomarkers for posttraumatic stress disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5(7):688-696. doi: 10.1016/j.bpsc.2020.03.010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r498] 498.Nicholson AA, Densmore M, McKinnon MC, et al. Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach. Psychol Med. 2019;49(12):2049-2059. doi: 10.1017/S0033291718002866 [DOI] [PubMed] [Google Scholar]

[zoi230081r499] 499.Zhu H, Yuan M, Qiu C, et al. Multivariate classification of earthquake survivors with post-traumatic stress disorder based on large-scale brain networks. Acta Psychiatr Scand. 2020;141(3):285-298. doi: 10.1111/acps.13150 [DOI] [PubMed] [Google Scholar]

[zoi230081r500] 500.Nicholson AA, Harricharan S, Densmore M, et al. Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning. Neuroimage Clin. 2020;27:102262. doi: 10.1016/j.nicl.2020.102262 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230081r501] 501.Harricharan S, Nicholson AA, Thome J, et al. PTSD and its dissociative subtype through the lens of the insula: anterior and posterior insula resting-state functional connectivity and its predictive validity using machine learning. Psychophysiology. 2020;57(1):e13472. doi: 10.1111/psyp.13472 [DOI] [PubMed] [Google Scholar]

PERMALINK

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

Zhiyi Chen, PhD

Xuerong Liu, MS

Qingwu Yang, MD

Yan-Jiang Wang, MD

Kuan Miao, MS

Zheng Gong, MS

Yang Yu, MPH

Artemiy Leonov, MPH

Chunlei Liu, PhD

Zhengzhi Feng, PhD, MD

Hu Chuan-Peng, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Evidence Review

Findings

Conclusions and Relevance

Introduction

Methods

Figure 1. Evidence Review Pipeline Based on PRISMA 2020 Guideline.

Retrieval Strategy

Data Extraction and Analysis

Statistical Analysis

Results

Figure 2. Geospatial Model for Sample Population.

Overall ROB for AI Models

Table 1. ROB Assessment for All Included AI Models Using the PROBAST (N = 555).

Clinical Applicability Concerns in AI Models

Reporting Quality of AI Models

Table 2. Reporting Quality for All Included AI Models Using the Modified CLEAR (N = 555).

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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