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. 2023 Nov 21;34(1):bhad387. doi: 10.1093/cercor/bhad387

Impaired visual-motor functional connectivity in first-episode medication-naïve patients with major depressive disorder

Yongbo Hu 1,2,3,4, Shiyi Li 5, Jin Li 6, Youjin Zhao 7,8,9, Meiling Li 10, Weigang Cui 11, Xiaolong Peng 12, Zaiquan Dong 13, Lianqing Zhang 14,15,16, Haizhen Xu 17,18,19, Li Gao 20, Xiaoqi Huang 21,22,23, Weihong Kuang 24,✉, Qiyong Gong 25,26,✉, Hesheng Liu 27,28,✉
PMCID: PMC10793073  PMID: 37991260

Abstract

The perceptual dysfunctions have been fundamental causes of cognitive and emotional problems in patients with major depressive disorder. However, visual system impairment in depression has been underexplored. Here, we explored functional connectivity in a large cohort of first-episode medication-naïve patients with major depressive disorder (n = 190) and compared it with age- and sex-matched healthy controls (n = 190). A recently developed individual-oriented approach was applied to parcellate the cerebral cortex into 92 regions of interest using resting-state functional magnetic resonance imaging data. Significant reductions in functional connectivities were observed between the right lateral occipitotemporal junction within the visual network and 2 regions of interest within the sensorimotor network in patients. The volume of right lateral occipitotemporal junction was also significantly reduced in major depressive disorder patients, indicating that this visual region is anatomically and functionally impaired. Behavioral correlation analysis showed that the reduced functional connectivities were significantly associated with inhibition control in visual-motor processing in patients. Taken together, our data suggest that functional connectivity between visual network and sensorimotor network already shows a significant reduction in the first episode of major depressive disorder, which may interfere with the inhibition control in visual-motor processing. The lateral occipitotemporal junction may be a hub of disconnection and may play a role in the pathophysiology of major depressive disorder.

Keywords: major depressive disorder, fMRI, visual cortex, visual-motor processing, individualized functional connectome

Introduction

Major depressive disorder (MDD) is a complex psychiatric illness and has been ranked as one of the leading causes of disability worldwide (Friedrich 2017; James et al. 2018). Apart from its characteristic pathological mood states, cognitive impairments in multiple domains, including attention, memory, processing speed, and executive function, are commonly observed in MDD (Gotlib and Joormann 2010; Lee et al. 2012; Snyder et al. 2013; Semkovska et al. 2019; Hu et al. 2023). Importantly, impairments are not limited to higher-order cognitive functions but also exist in visual perceptual functions, such as deficits in color perception (Bubl et al. 2010) or visual motion perception (Wallisch and Kumbhani 2009).

Brain imaging studies of MDD have identified changes in various cortical and subcortical regions associated with emotional and cognitive functions (Schmaal et al. 2016, 2017; Friedman and Robbins 2022). However, the role of the visual cortex in MDD is poorly understood. Only a few studies have reported the alterations of the visual cortex in gray matter volumes (Ancelin et al. 2019), surface area (Schmaal et al. 2017), cortical activation, and functional connectivity (FC) (Le et al. 2017) in patients with MDD. At the same time, several large meta-analyses of MDD failed to find consistent abnormal results in visual regions (Muller et al. 2017; Wang et al. 2017; Gray et al. 2020; Li et al. 2020). One important confounder in these studies is the high heterogeneity of patients that often included both treatment-naïve patients and repeatedly retreated, relapsed patients (Wang et al. 2017). Moreover, the small sample sizes and substantial variability in functional organization further hamper the discovery of meaningful brain-symptom associations (Gordon et al. 2017; Marek et al. 2022).

Visual motion perception function can be localized to motion-selective brain regions, most prominently in the visual cortex (Born et al. 2000; Born and Bradley 2005), where a high concentration of gamma-aminobutyric acid (GABA) enables inhibitory processing within the visual system to support the sensitivity to small moving stimuli segregated from the background (Tadin et al. 2019). Previous research has consistently reported the reduced inhibition of the visual system in MDD patients (Song et al. 2021; Liu et al. 2022), measured by the high-contrast context of the suppression test, regardless of the episodes (acute, remitted) (Golomb et al. 2009; Norton et al. 2016) and subtypes (unipolar, bipolar) (Salmela et al. 2021) of patients. Studies using magnetic resonance spectroscopy further linked the behaviorally decreased suppression to the established GABA deficit in the occipital area in depression (Bhagwagar et al. 2007; Truong et al. 2021), suggesting that an altered visual system might be a brain-based biomarker of MDD (Liu et al. 2022). Despite the solid physiological evidence of visual impairment in MDD, a critical question arises regarding the location of the hub of dysfunction, which has yet to be established using functional imaging.

The present study aimed to identify the impaired visual regions in a large sample of first-episode medication-naïve (FEMN) MDD patients. To minimize the potential confounds of significant neuroanatomic variance in patients (Wang et al. 2020; Zhao et al. 2023), we used an individual-oriented approach previously developed by our team (Wang et al. 2015), which allowed us to parcellate the cerebral cortex into 92 regions of interest (ROIs) based on resting-state functional magnetic resonance imaging (rs-fMRI) data. An “individualized functional connectome” was constructed for each participant using the connectivity among these individuals, representing a highly simplified description of the participant’s functional brain organization. The individualized connectomes were then compared between the patients and healthy controls (HCs).

Materials and methods

Participants

This study is part of the neurobiological research on MDD conducted at West China Hospital. The details of patient recruitment have been published previously (Peng et al. 2019; Zhao et al. 2023). In brief, FEMN patients with MDD were recruited consecutively from the Department of Psychiatry in West China Hospital. The MDD diagnosis was based on the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) and confirmed using the structured clinical interview for DSM-IV axis I disorders. The inclusion criteria were: (i) 18 to 65 years, (ii) first episode, (iii) had not received psychiatric medication treatment before, (iv) 17-item Hamilton depression rating scale (HAMD-17) scores > 7, and (v) did not display psychiatric comorbidities or severe somatic disease. This study was approved by the Clinical Trials and Biomedical Ethics Committee of West China Hospital, and all participants provided written informed consent.

Cognitive and clinical evaluation

Trail Making Test Part B (TMT-B), which assessed visual scanning, graphomotor speed, visual-motor processing speed, working memory, and inhibition control in visual-motor processing (Arbuthnott and Frank 2000; Llinàs-Reglà et al. 2017; Hu et al. 2023), was administered to all patients (n = 190) and 84 HC subjects. Participants were required to quickly connect the circles by alternating the Chinese serial number and Arabic serial number in the sequence. Completion time in seconds was recorded as the outcome score. Furthermore, the clinician version of HAMD-17 Rating Scale was employed to evaluate the severity of depression in patients (Zimmerman et al. 2013). Trained and authorized psychological assessors administered the HAMD scale to MDD patients on the same day as the magnetic resonance imaging (MRI) scan.

MRI data acquisition and preprocessing

Imaging data were collected using a 3.0 T MRI scanner (Siemens Trio, Erlangen, Germany) with an 8-channel phased-array head coil. High-resolution T1-weighted anatomical images were acquired using a gradient echo sequence: repetition time (TR) = 1,900 ms, echo time (TE) = 2.26 ms, flip angle (FA) = 9°, slice thickness = 1 mm, 176 axial slices, field of view (FOV) = 256 × 256 mm2, matrix = 256 × 256, voxel size = 1 × 1 × 1 mm3. rs-fMRI data were acquired using a gradient-echo echo-planar imaging sequence (TR = 2,000 ms, TE = 30 ms, FA = 90°, slice thickness = 5 mm, 30 slices, FOV = 240 × 240 mm2, matrix = 64 × 64, 3.75 × 3.75 × 5 mm3). Each scan lasted 350 s. The participants were instructed to lie still during the scans, keep their eyes closed, and stay awake. T1-weighted structural images were preprocessed using FreeSurfer (v.5.3) (http://surfer.nmr.mgh.harvard.edu/). Rs-fMRI data were preprocessed using a pipeline previously described (Buckner et al. 2011; Yeo et al. 2011), which included the following steps: (1) discarding the first 4 volumes, (2) slice timing correction, (3) motion correction, (4) bandpass filtering (0.01 to 0.1 Hz), and (5) nuisance regression including artifacts of head motion, ventricular and white matter, and whole-brain signal (see Supplementary Methods).

Parcellating cortical ROIs in individual

We parcellated each subject’s cortex into 92 regions using an iterative approach, which was developed from our previous studies (Wang et al. 2015, 2020; Zhao et al. 2023). First, 92 group-level cortical ROIs were obtained using k-means clustering and rs-fMRI data from the Genomic Superstruct Project (N = 1,000) (Holmes et al. 2015) (see Supplementary Methods). Second, group-level ROIs were projected onto the cerebral cortex of each individual. Third, the boundaries of the ROIs were gradually refined using an iterative approach. Critically, the influence of the group-level atlas on the individual brain parcellation is not identical for every subject or every brain region and is flexibly adjusted based on the known distribution of individual variability and the signal-to-noise distribution in the particular subject. Specifically, a weighting strategy will be applied where the group-level atlas will have less impact than the individual subject’s data on brain regions known to have high intersubject variability or brain regions showing good signal-to-noise in a particular subject. For subsequent analyses, each individualized ROI was also assigned to 1 of the 7 network atlases developed by Yeo et al. (2011), including the visual network (VN), sensorimotor network (SMN), etc. (see Fig. S1). Finally, the surface ROIs were projected to the volumetric MRI data, and each ROI’s volume was calculated (Yeo et al. 2011; Wang et al. 2015).

Intergroup comparison of FCs and ROI volumes

For each of the 92 individualized cortical ROIs, we extracted the average time series of resting-state blood oxygen level-dependent (BOLD) signals. FCs among these ROIs were then computed, and an “individualized functional connectome” was constructed for each subject. Next, “functional connectomes” were compared between the MDD and HC groups by 2-sample t-tests with false discovery rate (FDR) correction for multiple testing after controlling for age, sex, and head motion using linear regression models. Furthermore, the volumes of the ROIs involved in the significantly different FCs were also compared between the 2 groups using a Mann–Whitney U test. In addition, partial correlation analyses were performed between the significantly different FCs and the volumes of their ROIs while controlling for age, sex, head motion, whole-brain volume, and group factor (MDD vs. HC). Note that a natural log transformation was applied to the volume of these ROIs and the whole brain to achieve normality. Finally, impaired ROIs were defined as those showed significantly changed volume and FCs in patients as compared with HC.

Correlation analyses between the significantly reduced FCs and TMT-B performance

The correlation between the strength of the significantly reduced FCs and the scores of TMT-B was determined by partial correlation analyses while controlling for age, sex, and educational attainment in partial participants. Note that a log10 transformation was applied to the scores of TMT-B to achieve normality.

Statistical analysis

The enumeration data were analyzed using χ2 tests and presented as a number (percentage), and the measurement data were presented as the mean ± SD. ROI volumes were compared between the 2 groups using a Mann–Whitney U test as they were skewed. All statistical analyses were performed using MATLAB 2018a (The MathWorks, Natick, MA, United States) or SPSS version 25.0 (IBM, Armonk, NY). Statistical significance was set at a P-value < 0.05.

Results

Demographic and clinical information of the participants

Between 2010 November and 2019 September, 195 FEMN patients with MDD and 276 HCs were enrolled. After quality control of the MRI data, 190 FEMN patients with MDD were retained, and 190 HCs were strictly matched with the MDD patients one-to-one according to sex and age. Table 1 shows the demographic and clinical characteristics of the participants. For MDD and HC participants, the mean age (31.77 years) and sex ratio (61.1% female) were identical. Statistically, age and sex were not significantly different between the MDD and HC groups (for age, Mann–Whitney U Test, z [n = 380] = −0.022, P = 0.982; for age group composition, χ2 [3 degrees of freedom, n = 380] = 0.154, P = 0.985; for sex, χ2 [1 degree of freedom, n = 380] = 0.000, P = 1.000). HAMD-17 scores of patients with MDD were 24.15 ± 6.18 (mean ± SD; range between 8 and 43). The majority of the FEMN patients with MDD were identified as having moderate to severe depression, according to the severity of depression symptoms: mild depression (8.4%), moderate depression (38.9%), and severe depression (52.6%).

Table 1.

Demographic and clinical characteristics of patients with MDD and HCs.

Characteristic MDD group (n = 190) HC group (n = 190) χ2/zb P-value
Mean ± SD or n (%) Mean ± SD or n (%)
Age (years) 31.77 ± 11.15 31.77 ± 11.23 −0.022 0.982
18 to 29 97 (51.1) 99 (52.1) 0.154 0.985
30 to 39 45 (23.7) 42 (22.1)
40 to 49 32 (16.8) 32 (16.8)
50 to 65 16 (8.4) 17 (8.9)
Sex 0.000 1
Male 74 (38.9) 74 (38.9)
Female 116 (61.1) 116 (61.1)
Depression severitya 24.15 ± 6.18 / / /
Mild 16 (8.4) / / /
Moderate 74 (38.9) / / /
Severe 100 (52.6) / / /
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aPatients with MDD were divided into 3 groups by HAMD-17 scores: mild depression (8 to 16), moderate depression (17 to 23), and severe depression (≥24).

bMann–Whitney U test was applied to compare the age, and the Pearson chi-square test was used to compare the age group composition and sex ratio between the 2 groups.

TMT-B scores were examined for missing values and extreme outliers. Extreme outliers were defined as more than 4 SDs deviating from the mean value in a group, according to the previous literature (Shilyansky et al. 2016). Participants with missing values or extreme outliers were excluded from the statistical datasheet. After quality control, 184 patients with MDD and 71 matched HCs were included in subsequent correlation analyses. Detailed information about the demographics and depression severity of the subset for cognition-connectivity correlation analysis are summarized in Table S1.

Identifying impaired functional regions

Two connections from the “functional connectome” (4,186 FCs in total) were significantly reduced in patients with MDD compared with HCs (Fig. 1a, connection 1: t[378] = −4.46, FDR-adjusted P = 0.023; connection 2: t[378] = −4.66, FDR-adjusted P = 0.019). Interestingly, both connections involved ROI 52 (connection 1 was FC between ROI 52 and ROI 7, and connection 2 was FC between ROI 52 and ROI 53, Fig. 1b and c), a region located at the right lateral occipitotemporal junction (r-LOTJ). The 2 connections link ROI 52 to 2 symmetrical ROIs within the SMN (ROI 7 and ROI 53, located at the bilateral superior medial frontoparietal junctions, smFPJ, Fig. 1b and c). The strength of the 2 connections was highly correlated (r = 0.855). In the above 3 ROIs (7, 52, and 53), the volume of ROI 52 was significantly reduced (Mann–Whitney U test, z = −3.22, P = 0.001, Fig. 1d) in patients with MDD compared with HCs. Moreover, partial correlation analysis revealed that the volume of ROI 52 was positively correlated with the strength of connection 1 (r = 0.195, P = 1.48 × 10−4) and connection 2 (r = 0.200, P = 9.70 × 10−5, Fig. 1e), indicating that the functional changes may be partly driven by anatomical alternations of ROI 52. Meanwhile, the volumes of ROI 7 and ROI 53 were not significantly (P > 0.05) different between the 2 groups (Fig. S2a) and uncorrelated (P > 0.05) with the strength of the above 2 FCs (Fig. S2b). To exclude the possible confounders brought by head motion differences between MDD patients and HC groups, we selected 126 MDD patients and 126 HC participants from the original data sets with matched head motion (see Supplementary Methods). The comparison of connections 1 and 2 also demonstrated significant differences between the 2 head motion-matched groups (uncorrected P = 4.71 × 10−3 and 7.74 × 10−4, respectively, see Fig. S3). These data suggest that the anatomy and function of a visual region, ROI 52, might be impaired in patients with MDD.

Fig. 1.

Fig. 1

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Significantly reduced FC between visual and sensorimotor cortices in patients with MDD compared with HCs. a) Scatter plots of 2 FCs in patients with MDD and HCs. Among 4,186 connections examined, connection 1 (between ROI 52 and 7) and connection 2 (between ROI 52 and 53) showed significantly reduced FC in patients (FDR corrected for multiple comparisons). The t and P-values were obtained by 2-sample t-tests with FDR correction after controlling for age, sex, and head motion. b) Connections 1 and 2 are illustrated in the brain. c) Positions of ROIs involved in the 2 connections are illustrated on the cortical surface map. d) The volume of ROI 52 is compared between patients and HCs. Significant between-group difference was found by the Mann–Whitney U test. e) Correlations between the volume of ROI 52 and FCs. The r and P-values were obtained using the partial correlation analysis. The scatter plots and the regression lines were depicted after controlling for the effect of age, sex, head motion, the volume of the whole brain, and group factor (MDD vs. HC). A natural log-transform was applied to the volume of region 52 and the whole brain to achieve normal distributions. FC z-scores: Fisher’s r-to-z transformation of FC. *Indicates a statistically significant difference (FDR-adjusted P < 0.05 or P < 0.05).

Impaired FC in MDD

To further explore the connectivity changes in MDD, we selected ROI 52 as the seed to perform a series of seed-based FC analyses (see Supplementary Methods). We first calculated the mean value of FCs between ROI 52 with the whole cerebral cortex (Fig. S4a) and compared the FC maps between patients and HCs. The comparison revealed a significant group difference between ROI 52 and the left and right smFPJ (Fig. S4b).

We also explored the FC changes between ROI 52 and another 28 ROIs within the VN and SMN. Almost half of the connections examined (13 of 28 connections) showed a significant reduction in patients compared with HCs (Fig. 2, FDR-adjusted P < 0.05). At the network level, the connectivity between ROI 52 and the whole VN or SMN also showed significant reductions in patients compared with HCs (Fig. S5).

Fig. 2.

Fig. 2

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Fingerprints of FCs between ROI 52 and visual ROIs, as well as FCs between ROI 52 and sensorimotor ROIs. The polar plots show the average z-scores of FCs between ROI 52 and each individualized ROI within a) VN and b) SMN, respectively. The concentric circles represent FC z-scores from 0 (center) to 1 (outer boundary) in 0.25 increments in a) and from −0.1 (center) to 0.3 (outer boundary) in 0.1 increments in b). Anatomical positions of ROIs within the c) VN and d) SMN are displayed on the cerebral parcellation map. *Statistically significant difference (FDR-adjusted P < 0.05, multiple comparisons).

Expanded analyses of the peripheral and mirror-symmetric regions of ROI 52 were also conducted to validate the dysfunction of visual areas in MDD (see Supplementary Methods). First, some ROIs around ROI 52 seemed to be jointly impaired, as the FCs between some ROIs around ROI 52 (such as ROIs 73, 76, and 51) and the SMN ROI 53 were reduced to a greater extent in patients with MDD (Fig. S6). Second, we found that the symmetric connections (FCs between ROI 6 and ROI 7 and 53) to the 2 aberrant connections mentioned above (connections 1 and 2) were also impaired (Fig. S7a to c), as well as the mirror-symmetric region (ROI 6) of ROI 52 had a trend toward impaired volume in patients with MDD (Fig. S7d). Finally, FCs between ROI 6 and other ROIs within VN and SMN (Figs S8 and S9) were also impaired in patients. Taken together, these data suggest that the peripheral and mirror-symmetric regions of ROI 52 were potentially impaired in patients with MDD, affecting their connectivity with SMN and VN.

Impaired FC was related to inhibition control in visual-motor processing in MDD

Significant correlations (P < 0.05) were observed between the 2 aberrant connections (connections 1 and 2) and the TMT-B test scores in MDD patients, whereas no significant correlation (P > 0.05) was found in HC participants (Fig. 3). In addition, no significant correlations were observed between the aberrant connections 1 and 2 and other visual-motor processing test scores (TMT-A and DSST) (Fig. S10).

Fig. 3.

Fig. 3

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Correlations between TMT-B scores and connections 1 and 2 in MDD patients and HC participants. The r and P-values were obtained using the partial correlation analysis between TMT-B test scores and a) connection 1 (MDD group: r = 0.152, P = 0.041; HC group: r = 0.059, P = 0.633), b) connection 2 (MDD group: r = 0.153, P = 0.040; HC group: r = −0.037, P = 0.765). The scatter plots and the regression lines were depicted after controlling for the effect of age, sex, and educational attainment. A log 10 transformation was applied to the TMT-B test scores to normalize the data.

Discussion

In this study, we investigated functional and anatomical impairments related to MDD in a large sample of FEMN patients, avoiding potential confounds related to antidepressants. Using a novel individual-oriented method, we found significant reductions in FCs between the VN and SMN and anatomical and functional alterations in LOTJ. The significantly reduced FCs were related to inhibition control in visual-motor processing. These findings suggest that the FC between VN and SMN already shows significant impairment at the first episode of MDD, which may disrupt visual-motor inhibitory control in these patients.

Impaired FC of the visual area in MDD

An important observation of the present study is that the connections significantly affected in FEMN patients with MDD were between the visual and sensorimotor cortex. Previous fMRI studies had reported aberrant functioning of the visual regions in patients with MDD. While meta-analyses identified the voxel-based pathophysiology and abnormal resting-state hypoactivity in the visual cortex in MDD, such as the middle occipital/inferior temporal gyri (Gray et al. 2020) and left middle occipital gyrus (Ma et al. 2019), the exact locations of these impaired visual regions in individual subjects were yet to be identified. Our data suggested that r-LOTJ (ROI 52) is anatomically and functionally impaired. Moreover, regions near ROI 52 and mirror-symmetric regions of ROI 52 also showed similar FC reduction, indicating that LOTJ and its surrounding visual areas might be the key to perceptual dysfunction in MDD and deserve careful investigation in future research.

The disconnection of the visual cortex with other regions in MDD has been recognized in previous studies (Le et al. 2017; Yan et al. 2019). However, researchers tended to attribute visual impairment as the cumulative effect of depression in recurrent MDD patients (Yan et al. 2019). An important finding of the present study is that connections between VN and SMN are the most significantly impaired connections, even in FEMN patients. This hypoconnectivity was consistently observed in both seed-based and network-level analyses, suggesting that disconnection between VN and SMN may occur in the early stage of MDD rather than as a result of prolonged disease progression or medication. These results call for more attention to the role of perceptual-level alteration in the etiology of MDD and how inhibition control deficits in the visual region are related to higher-order cognitive functions in different disorder phases.

A large body of previous research has suggested that the decreased GABA concentration in a wide range of brain regions might be the molecular mechanism underpinning affective and cognitive disorders in depression (Luscher et al. 2011; Prévot and Sibille 2021). Some studies have reported decreased concentration of GABA in the occipital cortex in MDD (Sanacora et al. 2004; Bhagwagar et al. 2007). Moreover, decreased GABA concentration in the higher-order occipital-middle temporal (MT) area was found to be associated with reduced visual suppression in patients with MDD (Song et al. 2021). Interestingly, in the present study, we identified an anatomically and functionally impaired region, ROI 52, which is localized in the right MT (Fig. S11) (Kolster et al. 2010). We speculate that the functional changes of ROI 52 might be attributed to the reduced GABA concentration, which may lead to the deficits in inhibition control.

Potential implications for future clinical work

As reported in the present study, the weakened visual-motor FC might be candidates for novel brain-based biomarkers in detecting the early stage of depression. Future treatments may also target the visual area, especially the LOTJ, to restore its functional organization and alleviate symptoms related to visual-motor inhibition processing. This area has been proven accessible and responsive to noninvasive neuromodulation methods such as transcranial magnetic stimulation, which could offer a practical and well-tolerated treatment for MDD (Zhang et al. 2021).

Limitations

There are several limitations of the study that are worth mentioning. First, we only collected 350 s of resting state fMRI data from each participant, which is not ideal for personalized functional network parcellation. Previous studies have indicated that reliability is a function of scan length; thus, future research should acquire longer fMRI data to improve reliability. Nevertheless, this limitation can be partially mitigated by the large sample size of our data set. Second, we did not analyze the BOLD signal in the brain white matter, which could carry important information related to neuropsychiatric disorders (Ji et al. 2017, 2019). Future explorations of individualized functional parcellation may take into account functional signals in the white matter. Third, this study did not include subcortical structures like the amygdala, hippocampus, and ventral striatum, resulting in a potential oversight of any volume and functional impairments within these structures in relation to depression. Finally, we cannot completely rule out the possibility that some first-episode drug-naïve patients with MDD are misdiagnosed. Given that psychiatric diagnosis still depends on a subjective interpretation of behavior, the lack of medication history could increase the chance of error. However, we focus on visual-motor inhibition processing in our patient cohort, which is domain-specific. Thus, our findings might not rely on the diagnosis of MDD.

Supplementary Material

Supplementary_material_final_bhad387
Click here for additional data file. (3.6MB, docx)

Acknowledgments

This study used data from the Department of Psychiatry, West China Hospital of Sichuan University. We thank all staff and participants of this study for their support. We thank Haoran Li, Shan Rao, and Yunyun Li for their assistance with data collection.

Contributor Information

Yongbo Hu, Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu 610041, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China; Department of Neurology, The Third People’s Hospital of Chengdu, Chengdu 610031, China.

Shiyi Li, Changping Laboratory, Science Park Road, Changping District, Beijing 100001, China.

Jin Li, Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China.

Youjin Zhao, Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu 610041, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China.

Meiling Li, Changping Laboratory, Science Park Road, Changping District, Beijing 100001, China.

Weigang Cui, School of Engineering Medicine, Beihang University, Bejing 100083, China.

Xiaolong Peng, Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, United States.

Zaiquan Dong, Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China.

Lianqing Zhang, Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu 610041, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China.

Haizhen Xu, Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu 610041, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China.

Li Gao, Department of Neurology, The Third People’s Hospital of Chengdu, Chengdu 610031, China.

Xiaoqi Huang, Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu 610041, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China.

Weihong Kuang, Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China.

Qiyong Gong, Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu 610041, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen 361000, China.

Hesheng Liu, Changping Laboratory, Science Park Road, Changping District, Beijing 100001, China; Biomedical Pioneering Innovation Center, Peking University, Beijing 100871, China.

Author contributions

Yongbo Hu (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing—original draft), Shiyi Li (Data curation, Writing—review & editing), Jin Li (Investigation, Resources, Supervision), Youjin Zhao (Data curation, Investigation, Software), Meiling Li (Data curation, Methodology, Software), Weigang Cui (Data curation, Methodology, Software), Xiaolong Peng (Data curation, Methodology, Software), Zaiquan Dong (Investigation, Resources), Lianqing Zhang (Data curation, Investigation, Methodology), Haizhen Xu (Investigation, Resources), Li Gao (Investigation, Resources), Xiaoqi Huang (Investigation, Methodology), Weihong Kuang (Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources), Qiyong Gong (Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing—review & editing), and Hesheng Liu (Conceptualization, Funding acquisition, Methodology, Project administration, Software, Supervision, Writing—review & editing).

Funding

The Changping Laboratory and the Ministry of Science and Technology of China (2021B-01-01); the National Key R&D Program of China (2022YFCs2009900); the National Natural Science Foundation of China (81621003, 81820108018, 82027808, 81790652, 81790650); National Institutes of Health (NIH) grants (P50MH106435, 1R01DC017991, R61MH121640); West China Hospital of Sichuan University research grant (ZYJC21029); the Chengdu Science and Technology Bureau Project (2019-YF09-00086-SN).

Conflict of interest statement: None declared.

Data and code availability

The code for personalized parcellation is available at https://www.nmr.mgh.harvard.edu/bid/DownLoad.html. Access to the data used in this study is restricted to requests made directly to the corresponding authors. Any attempt to re-analyze the data must first receive approval from the Academic Committees of West China Hospital, Sichuan University.

References

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Associated Data

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

Supplementary Materials

Supplementary_material_final_bhad387
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Data Availability Statement

The code for personalized parcellation is available at https://www.nmr.mgh.harvard.edu/bid/DownLoad.html. Access to the data used in this study is restricted to requests made directly to the corresponding authors. Any attempt to re-analyze the data must first receive approval from the Academic Committees of West China Hospital, Sichuan University.

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