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. 2026 Jun 26;21(6):e0352593. doi: 10.1371/journal.pone.0352593

Electroretinography as a non-invasive biomarker for early detection of cognitive impairment in older adults: A scoping review protocol

Isaiah Osei Duah Junior 1,2,*, Katelyn JeNay Houston 2, Danielle S Rodriguez 2, Cassandra M Germain 2,*
Editor: Vahid Mansouri3
PMCID: PMC13309028  PMID: 42361148

Abstract

Cognitive decline and dementia represent a growing global health challenge with an urgent need for accessible biomarkers to support early detection, monitoring, and timely intervention, particularly among marginalized populations who face persistent barriers to equitable access to specialized diagnostic and neurological care. Electroretinography (ERG), a non-invasive measure of retinal electrophysiological activity, has emerged as a potential visual biomarker given the retina’s structural and functional homology with the brain. Preliminary studies suggest associations between ERG abnormalities and cognitive impairment; however, the existing evidence base is limited and lacks sufficient consistency and integration to draw clear, definitive conclusions. The primary aim of this scoping review is to systematically characterize and synthesize existing evidence on the utility and efficacy of ERG as a visual biomarker of cognitive change and cognitive status in adults 50 years and over and identify research gaps. Specifically, the review will: (1) characterize study designs, populations, ERG modalities, parameters, and cognitive outcomes; (2) identify ERG components consistently associated with cognitive status or decline; (3) describe strategies for managing ophthalmic and systemic confounders; (4) locate longitudinal studies assessing prognostic or predictive utility; and (5) highlight methodological limitations, reporting gaps, and priority areas for future research. Following the Population–Concept–Context (PCC) framework, the review will include studies of older adults with cognitive impairment, or dementia, examining ERG modalities such as full-field, pattern, and multifocal ERG. All relevant and completed published studies in English language will be identified from MEDLINE (via PubMed), Embase, Web of Science, Scopus, PsycINFO, Google Scholar, Cochrane Central Register of Controlled Trials (CENTRAL) without date restrictions. The retrieved records will be managed in EndNote (version 2025), and in accordance with Covidence framework for scoping reviews, three reviewers will independently screen titles, abstracts, and full texts. Data will be charted on study characteristics, ERG parameters, cognitive outcomes, and ERG-cognitive status associations. Collectively, this synthesis will provide clinicians and researchers with consolidated evidence on ERG–cognition relationships, clarify whether the evidence base supports further systematic review or meta-analysis, and generate methodological recommendations for future studies.

Introduction

Globally, unprecedented population ageing, combined with increased life expectancy [1,2], socioeconomic transitions, and increases in cardiometabolic diseases [3–5], have led to a surge in neurodegenerative and neuropsychiatric disorders, accompanied by progressive cognitive and memory decline [1–5]. These conditions, together with their associated risk factors, are becoming increasingly prevalent and contribute substantially to the rising burden of cognitive impairment worldwide [6]. Current estimates indicate that between 32 and 315 million individuals fall along the Alzheimer’s disease pathology continuum [7], while the global prevalence of dementia is projected to rise sharply from 57.4 million cases in 2019 to 152.8 million by 2050 [8]. At the individual level, these projections underscore the urgent need for early identification of populations at risk of cognitive decline, to enable effective risk stratification and timely clinical intervention [9]. At the societal level, they highlight the necessity of informed public health planning, strategic resource allocation, and investment in innovative therapeutic approaches aimed at slowing disease progression and mitigating future burden [8].

The rapid expansion of dementia research, coupled with advances in biomedical technologies, has facilitated the development of several brain-based biomarkers with significant diagnostic and prognostic utility [10]. These include neuroimaging modalities [11], cerebrospinal fluid assays [12,13], and blood-based markers [12,13]. Despite their clinical value, these approaches often require specialized infrastructure, may be invasive or costly, and remain inaccessible in many clinical and low-resource community settings [14–16]. These factors underscore the need for non-invasive, cost-effective, and consumer-friendly technologies to support the timely detection and effective management of cognitive decline [17].

The human retina offers a unique and highly accessible window into central nervous system (CNS) pathology [17–19]. As an embryological outgrowth of the brain, the retina is part of the CNS and shares key structural and functional properties with cortical tissue [18,20]. To this end, retinal function can be assessed rapidly and non-invasively, making it particularly suitable for large-scale community-based screening and longitudinal monitoring of cognitive dysfunction in adults, including geriatric populations and marginalized groups with limited access to neurological care. Of note, the electroretinogram provides an objective measure of retinal electrophysiological function by capturing signal transduction across photoreceptors, bipolar cells, ganglion cells, and inner retinal circuits [21]. Because these retinal elements mirror core features of central synaptic and neural network organization, the ERG serves as a functional readout of broader brain integrity [17,22–24].

Consistent with this notion, ERG abnormalities have been demonstrated across diverse neuropsychiatric and neurodegenerative conditions, supporting its role as a functional proxy for brain integrity and cognitive health [17,22,23,25–32]. By detecting subtle retinal dysfunction that may precede overt clinical symptoms, ERG represents a promising biomarker for memory deficits, cognitive decline, and dementia-related pathophysiology [16,17,33]. Taken together, ERG holds potential for early risk stratification, improved disease monitoring, and the identification of individuals who may benefit from timely preventive or therapeutic interventions.

The rationale for conducting the scoping review

The growing interest in the retina–brain connection has prompted increasing investigation into whether specific ERG parameters including a-wave and b-wave amplitudes, implicit times, and oscillatory potentials across full-field ERG (ffERG), pattern ERG (pERG), and multifocal ERG (mfERG) are altered in persons living with MCI, Alzheimer’s disease [27,30,32,34–42], and other neuropsychiatric disorders compared with cognitively intact adults [31,43–51]. Preclinical and clinical studies suggest meaningful associations between cognitive dysfunction and ERG abnormalities, including reduced amplitudes, delayed implicit times, and altered inner-retinal oscillatory activity [36,52,53]. Together, these findings raise the possibility that ERG could serve as a low-cost, widely deployable ocular biomarker to complement and/or enhance existing diagnostic testing paradigm for cognitive deficits [16]. However, the evidence base remains fragmented, varied in quality, and methodologically inconsistent, making it difficult to draw firm conclusions about ERG’s utility in cognitive-health assessment.

Further, studies differ substantially in the ERG modalities used, stimulus protocols, retinal ERG components analyzed (e.g., a-wave, b-wave, implicit times, oscillatory potentials), and the cognitive outcomes measured [16,17,42,35,54–57]. Additional variability arises from differences in diagnostic criteria used for neurocognitive disorder, sample characteristics, study designs, and the handling of key confounders such as age-related ocular disease, systemic vascular factors, medication effects, and technical differences in ERG acquisition [27,30,32,34–46,58,59]. Importantly, recent advances have introduced portable ERG device mainly RETeval that enable rapid, non-invasive assessment outside of specialized laboratory settings [60–62]. These innovations have the potential to expand ERG feasibility as a scalable, community-based screening platform, by reducing barriers related to cost, accessibility, and infrastructure [60,61]. The deployment of portable ERG technologies in primary care, community clinics, and population-level studies will help bridge the gap between experimental promise and practical implementation in cognitive-health assessment.

The fragmentation of existing evidence has resulted in a landscape in which findings are difficult to synthesize, and the conceptual boundaries of the field remain unclear. Some studies examine ERG as a diagnostic tool, others as a method for disease staging, and still others as a means of monitoring cognitive decline over time [16,37,54,63]. Many investigations are small-scale, exploratory, or pilot in nature, and methodological rigor varies widely [16,17,42,35,54–57,64–67]. Collectively, these limitations have hindered the development of coherent conclusions regarding the role of ERG in cognitive impairment and dementia among older adults. Despite growing interest, there remains a lack of comprehensive evidence mapping that systematically links specific ERG measures with trajectories of cognitive decline.

To address this knowledge gap, a scoping review represents the most appropriate methodological approach [68–70]. Unlike traditional systematic reviews or meta-analyses, which typically focus on narrowly defined questions and relatively homogeneous data, scoping reviews are designed to map broad and evolving fields of inquiry [68–70]. This approach allows for the inclusion of diverse study designs and enables systematic identification of the ERG parameters assessed, clarification of how cognitive impairment is operationalized, and evaluation of variability in study methodologies and reporting practices [68–70]. A scoping review can also illuminate critical evidence gaps relevant to our hypotheses, including the need for longitudinal studies, the lack of standardized ERG acquisition and analysis protocols, and limited consideration of comorbid ocular or systemic conditions that may influence retinal signals [68–70].

By systematically charting the extent, range, and nature of research on ERG and cognitive impairment, this review will provide a foundational understanding of ERG’s potential utility in applied psychology and, more specifically, in aging neuroscience [71]. It will support researchers, clinicians, and policymakers in identifying priorities for future investigations, inform the development of standardized research frameworks, and determine whether subsequent systematic reviews or meta-analyses are feasible. Collectively, this synthesis will clarify whether ERG can be positioned as a credible, scalable ocular biomarker for cognitive decline in gerontology, complementing existing diagnostic approaches and advancing both research and clinical practice.

The aim of the scoping review

The primary aim of this scoping review is to systematically map existing evidence on the utility and efficacy of ERG as an ocular biomarker for cognitive changes in older adults (≥ 50 years), with specific attention to its relationships with cognitive impairment and dementia. This review will describe and categorize ERG modalities including ffERG, pERG, mfERG as well as the recording protocols used across studies, such as ERG system type, recording protocols, stimulus properties. It will also summarize the retinal ERG measures most evaluated, including amplitudes, implicit times, and waveform characteristics (specific aim 1). The review will further identify the cognitive assessment approaches used in studies examining ERG in relations to cognition, including the cognitive domains assessed, the neuropsychological tests employed, and the diagnostic criteria used to classify cognitive status (specific aim 2). In addition, it will examine methodological characteristics and sources of heterogeneity across studies, including study design, sample composition, strategies for handling ocular comorbidities confounds, and differences in ERG acquisition and analysis (specific aim 3). The synthesis will examine how alterations in ERG parameters including amplitudes, latencies, oscillatory potentials and the photopic negative response are associated with performance on cognitive tasks and overall cognitive status. It will consider both cross sectional findings and longitudinal or predictive evidence reported in the literature (specific aim 4). Finally, the review will identify research gaps, methodological inconsistencies and limitations in the existing evidence base to guide future research, particularly regarding feasibility, standardization requirements and the potential clinical utility of ERG. It will also map how ERG has been integrated with other biomarkers such as neuroimaging, structural retinal imaging, and fluid-based markers in studies examining cognitive decline (specific aim 5).

Methods

The scoping review protocol was prepared in accordance with PRISMA-P (S1 Table) [72] and indexed in the Open Science Framework (10.17605/OSF.IO/PM8XC) to ensure methodological rigor and transparent reporting. This protocol outlines a structured framework designed to identify and synthesize evidence on the relationship between ERG and cognitive status, as illustrated in the PRISMA flow diagram (see Fig 1). The review will follow the PCC framework to guide study identification and selection [73] and systematically chart the emerging findings in alignment with established scoping review methodology proposed by Arksey and O’Malley [71]. Specifically, the review will adopt five-stage process which includes: (1) identifying the research question, (2) identifying relevant studies, (3) selecting studies, (4) charting the data, and (5) collating, summarizing, and reporting the results [71]. This structured approach provides a transparent and replicable framework for mapping evidence across diverse study designs of cognitive assessment in aging adults. The final review will be reported in accordance with the PRISMA-ScR to maintain comprehensive reporting standards and methodological rigor [74]. Collectively, this scoping review protocol outlines a framework for systematically mapping how ERG has been investigated in relation to cognitive status, clarifying the current evidence base, identifying methodological strengths and research gaps, and informing future research and clinical translation in geriatric care.

Fig 1. PRISMA flow diagram.

Fig 1

This chart delineates the step-by-step process of how data will be charted.

Inclusion criteria

Population.

This scoping review will include studies involving adult populations aged 50 years and older across the cognitive spectrum, including cognitively intact individuals, those at risk of cognitive decline, and individuals diagnosed with MCI and Alzheimer’s disease and -related dementia (ADRD). Eligible studies will consist of human research investigations using randomized controlled trial (RCT), quasi-experimental, and observational research designs. Studies will be included participants’ cognitive status has been assessed using validated neuropsychological instruments or standardized cognitive screening toolkits such as Mini-Mental State Examination (MMSE) [75,76], Montreal Cognitive Assessment (MoCA) [77,78], Cambridge Neuropsychological Test Automated Battery (CANTAB) [79], Benton Visual Retention Test (BVRT) [80], Wechsler Memory Scale (WMS) [81]. Studies will also be eligible if cognitive status is defined using established diagnostic criteria, including the Diagnostic and Statistical Manual of Mental Disorders (DSM) [82,83], the International Classification of Diseases (ICD) [84], or the National Institute on Aging–Alzheimer’s Association (NIA‑AA) framework [85].

Concept.

The core concept of this scoping review is the use of ERG as a biomarker of cognitive decline and cognitive status. Eligible studies may employ any ERG modality, including ffERG, pERG, and mfERG and must report quantitative or qualitative retinal functional parameters such as amplitudes, implicit (latency) time, and other waveform morphology. Studies will be included if ERG findings are examined in relation to cognitive performance, cognitive trajectories, or neurodegenerative processes associated with cognitive impairment or ADRD (see Fig 2).

Fig 2. ERG as a biomarker for cognitive decline and CNS pathology.

Fig 2

This schematic illustrates the potential role of electroretinography (ERG) in detecting cognitive impairment and neurodegenerative conditions such as Alzheimer’s disease and other dementias. ERG measures retinal function, capturing electrophysiological signals that reflect central nervous system (CNS) integrity. Alterations in ERG parameters may serve as early indicators of cognitive decline, enabling timely intervention, monitoring, and preventive or therapeutic strategies. The figure highlights the translational pathway from retinal electrophysiology to clinical application, emphasizing ERG as a scalable biomarker bridging ocular measures and CNS pathology. Created in BioRender. Osei Duah, I. J. (2026) https://BioRender.com/f4fqajq.

Context.

This scoping review will encompass evidence generated across a broad range of research and care settings, including hospital-based studies, outpatient and specialty clinics, community-based cohorts, and population-level or epidemiological investigations. No geographical restrictions will be applied, allowing inclusion of studies from both high- and low-resource settings. Studies published in any language will be considered, provided that an English abstract is available to support screening and eligibility assessment. Both RCT, quasi-experimental, and prospective research designs will be included to comprehensively map how ERG has been applied to evaluate cognitive status, detect early cognitive changes, and monitor cognitive decline over time.

Exclusion criteria

Studies focusing exclusively on pediatric, adolescent, or young adult populations will be excluded. Studies using ERG solely to assess primary ophthalmic disorders (e.g., glaucoma, age-related macular degeneration, rod–cone dystrophies) will be excluded unless they explicitly report cognitive outcomes or examine associations between ERG parameters and cognitive status. In vivo animal studies, in vitro studies, case reports, editorials, commentaries, conference abstracts, review articles, and non–peer-reviewed publications will be excluded to ensure inclusion of rigorously evaluated and methodologically sound evidence. Articles without accessible full text will also be excluded due to insufficient detail for critical appraisal. Additionally, studies lacking validated cognitive assessments or clear diagnostic criteria for cognitive status, or those with inadequately described ERG methods or cognitive measures that are not analytically linked, will be excluded to maintain methodological rigor and interpretability.

Data sources

A comprehensive and systematic search will be conducted across major bibliographic databases to capture peer-reviewed literature relevant to the use of ERG in cognitive-health research. Databases will include MEDLINE (via PubMed), Embase, Web of Science, Scopus, PsycINFO, Google Scholar, and the CENTRAL, to ensure broad coverage across biomedical, psychological, and interdisciplinary domains. Citation searching will be performed on included studies to identify additional relevant publications. The search will be restricted to peer-reviewed published studies to ensure methodological rigor, data reliability, and consistency in quality appraisal. The review aims to exclude unpublished and grey literature sources to enhance the credibility and reproducibility of findings. No date or language restrictions will be applied, and eligible non-English studies will be translated where necessary to ensure consistent reporting.

Search strategy

Comprehensive searches will be conducted across all selected databases, with search strategies developed in consultation with a medical librarian to ensure methodological rigor and reproducibility. The search framework will be organized around three core concepts (see Table 1 for detailed terms and strategy). Boolean operators will be used to structure the search, applying “OR” to combine synonyms within each concept and “AND” to link distinct concepts. Search syntax will be adapted to ensure compatibility across databases. Controlled vocabulary, including medical subject headings (MeSH) and Emtree terms, will be integrated with free-text keywords related to ERG and cognitive outcomes. Proximity operators and database-specific filters will be applied where appropriate to optimize sensitivity and precision. All search strings, database interfaces, and search dates will be systematically documented and reported in accordance with PRISMA-ScR guidelines [74].

Table 1. PCC-based search strategy.

Serial No. PCC Element Search Terms (OR-separated)
#1 Population cognitive decline OR cognitive impairment OR cognitive dysfunction OR cognitive deterioration OR cognitive deficit OR cognitive disorder OR neurocognitive decline OR neurocognitive impairment OR mild cognitive impairment OR MCI OR dementia OR Alzheimer disease OR neurodegenerative disease OR neuropsychiatric disorder
#2 Concept electroretinography OR electroretinogram OR ERG OR retinal electrophysiology OR retinal electrical response OR retinal function test
#3 Context clinical setting OR healthcare setting OR hospital OR outpatient OR community-based OR research setting
#4 Combined PCC Search (optional) (electroretinography OR electroretinogram OR ERG OR retinal electrophysiology OR retinal electrical response OR retinal function test) AND (cognitive decline OR cognitive impairment OR cognitive dysfunction OR cognitive deterioration OR cognitive deficit OR cognitive disorder OR neurocognitive decline OR neurocognitive impairment OR mild cognitive impairment OR MCI OR dementia OR Alzheimer disease OR neurodegenerative disease OR neuropsychiatric disorder) AND (clinical setting OR healthcare setting OR hospital OR outpatient OR community-based OR research setting)

Boolean operators: “OR” and “AND”.

Study selection

All retrieved records will be managed in EndNote (version 2025) and subsequently imported into Covidence for duplicate removal and screening. Study selection will follow a two-stage process in accordance with PRISMA-ScR guidelines. First, titles and abstracts will be screened for relevance. Full text of potentially eligible studies will then be reviewed against the prespecified inclusion criteria. Each record will be independently assessed by two reviewers to minimize selection bias. Discrepancies will be resolved through discussion and majority voting (2:1). If consensus cannot be achieved, an independent reviewer will adjudicate. The study selection process will be documented using a PRISMA flow diagram. When necessary, corresponding authors will be contacted to clarify unclear or missing information.

Data extraction

Three authors will independently extract data from each included study using a piloted data extraction form adapted from the Joanna Briggs Institute data collection form (see Table 2). The ERG parameters will be treated as the primary predictors and will include ERG waveform components such as amplitudes and latencies, oscillatory potentials, the photopic negative response (PhNR), ERG modality type, and derived indices of retinal function from other modalities. The primary outcomes will consist of cognitive status and performance, operationalized through clinical classifications, for example cognitively intact, MCI, or dementia, as well as global cognitive screening scores and domain-specific measures of memory, executive function, attention, and processing speed. Secondary outcomes will include disease stage, symptom severity, and longitudinal cognitive trajectories where reported. Data extraction will also capture potential modifiers of ERG–cognition associations, including participant characteristics such as age, sex, and education; visual health confounders including visual acuity, cataract, glaucoma, and retinal pathology; and ERG methodological features. The extent to which ERG findings are integrated with other biomarkers, such as neuroimaging, cerebrospinal fluid indices, or blood-based measures, will be documented. Methodological characteristics, study limitations, and reporting quality will be systematically charted to delineate sources of heterogeneity, synthesize key findings, and identify evidence gaps and priorities for future investigation (see Table 2).

Table 2. Data extraction framework for ERG–cognition studies.

Serial No. Variable Description of the Variable
1 Study identification Author(s), year of publication, country/region, source type, study objectives/aim, funding, conflicts of interest
2 Study design Study design, sample size, study setting, inclusion/exclusion criteria, follow-up duration, statistical methods
3 Participant characteristics Age, sex distribution, education, cognitive status, diagnostic criteria, medical comorbidities, ocular health/visual acuity
4 Ocular and systemic confounders Screening for ocular diseases and systemic conditions that affects the retina, exclusion or adjustment methods, refractive error, pupil dilation control
5 ERG characteristics ERG modality, recording protocol, stimulus characteristics, retinal parameters, retinal cell/pathway target
6 Cognitive assessment Cognitive domains assessed, tests/tools used, cut-off scores, global versus domain-specific measures
7 ERG–cognitive associations Association direction, ERG parameters linked, cognitive domains affected, type of evidence
8 Integration with other biomarkers Neuroimaging, retinal imaging, blood/fluid biomarkers, multimodal analysis
9 Methodological characteristics and limitations ERG protocol variability, cognitive classification differences, sample heterogeneity, confounding factors, study limitations
10 Key findings and evidence gaps Main findings, evidence gaps, future research recommendations, clinical/feasibility implications

ERG, electroretinogram.

Data synthesis

Data synthesis will follow established methodological guidance for scoping reviews and will be reported in accordance with the Synthesis Without Meta-analysis (SWiM) framework which is appropriate when statistical pooling is not feasible due to heterogeneity [86]. Because the included studies are expected to differ substantially in ERG modalities, cognitive assessments, study designs, and analytic approaches, no meta‑analytic effect estimates will be generated. Instead, the synthesis will rely on structured, transparent comparison methods suited for diverse evidence. Following data charting, studies will be organized according to ERG modality (ffERG, pERG, and mfERG), waveform morphology (a-wave and b-wave amplitudes, implicit times, oscillatory potential, PhNR etc.), population characteristics (cognitively intact, MCI, and ADRD), and study design (RCT, quasi-experimental, and observational designs). Reported ERG–cognitive associations will be categorized by direction of effect (positive, negative, null, or mixed), statistical significance, and effect size metrics where available (e.g., correlation or regression coefficients, odds ratios, and 95% confidence intervals). A structured narrative synthesis will compare patterns of association across studies, emphasizing convergence, inconsistency, and sources of variation. Quantitative findings will be tabulated and visually mapped to facilitate comparison across ERG parameters and cognitive domains, including global cognition, memory, executive function, attention, and processing speed. Longitudinal evidence will be synthesized separately to evaluate whether specific ERG parameters demonstrate predictive utility for cognitive decline or disease progression. Methodological characteristics likely to contribute to heterogeneity will be examined systematically, including ERG acquisition protocols (stimulus conditions, adaptation state, electrode configuration), cognitive assessment instruments, diagnostic criteria, sample size, adjustment for confounders (age, sex, education), and control of ophthalmic or systemic comorbidities. These factors will inform interpretation of the strength and consistency of reported associations. Although no pooled effect estimates will be produced, the review will assess whether any subgroup of studies demonstrates sufficient methodological and outcome homogeneity to justify a future meta‑analysis. The resulting SWiM‑informed synthesis will provide a transparent and reproducible summary of the extent, nature, and methodological robustness of the evidence base, while identifying priorities for harmonization and future investigation [86].

Ethical consideration

This scoping review protocol is based entirely on previously published studies and secondary data sources. No new data will be collected from human participants or animals, and no interventions or experimental procedures will be conducted; therefore, ethical approval is not required. The protocol will follow established standards of research conduct, including transparent reporting, accurate citation of original sources, and responsible synthesis of evidence. In accordance with the PLOS Data Policy, all data underlying the findings of the completed scoping review will be made fully available at the time of publication. This will include the complete set of extracted variables, data-charting tables, and any summary statistics derived from included studies. If any included studies impose limitations on sharing specific data elements because of third-party ownership or licensing constraints, these restrictions will be clearly documented, and only data that can be shared legally and ethically will be made available. All included studies will be appropriately referenced to respect intellectual property and maintain the integrity of the original research.

Discussion

This scoping review protocol outlines a systematic approach to map and synthesize existing evidence on the use of ERG as an ocular biomarker for cognitive changes or cognitive status in older adults. By cataloguing ERG indices and modalities, cognitive domains, assessment tools, and diagnostic classifications across studies, the review aims to provide a comprehensive overview of how ERG has been applied in research on cognitive impairment and dementia in the aging populations. The synthesis will examine reported associations between ERG measures and cognitive outcomes, capturing both cross-sectional relationships and longitudinal or predictive patterns. In doing so, the review will delineate the scope, depth, and characteristics of the current literature while highlighting areas where evidence is emerging or remains limited.

Several methodological considerations are anticipated. Substantial heterogeneity in study designs, participant populations, ERG acquisition protocols, and approaches to managing ocular comorbidities is expected, which will preclude quantitative pooling and necessitate a narrative synthesis. This heterogeneity will also limit direct comparability across studies. As a scoping review, formal assessment of risk of bias or strength of evidence will not be conducted, and conclusions regarding clinical effectiveness or diagnostic accuracy will remain cautious. Nonetheless, by systematically identifying methodological variability, evidence gaps, and inconsistencies, the review is expected to inform future primary studies and systematic reviews, support efforts toward methodological standardization. It will also guide the development of rigorous longitudinal and multimodal investigations of ERG as a biomarker of cognitive decline among aging population.

Supporting information

S1 Table. PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) 2015 checklist: Recommended items to address in a systematic review protocol.

(DOCX)

pone.0352593.s001.docx (27.6KB, docx)

Acknowledgments

The authors would like to thank Ms. Mary White of the F.D. Bluford Library, North Carolina Agricultural and Technical State University, Greensboro, NC, United States of America, for her assistance with developing the search terms and strategies. The authors also acknowledge Dr. Yaw Barima Buabeng of the Department of Psychology, University of Georgia, GA, United States of America for his support in procuring BioRender for the creation of figure in the manuscript.

Abbreviations

ADRD

Alzheimer’s Diseases and Related Dementia

BVRT

Benton Visual Retention Test

CANTAB

Cambridge Neuropsychological Test Automated Battery

CENTRAL

Cochrane Central Register of Controlled Trials

DSM

Diagnostic and Statistical Manual of Mental Disorders

ERG

Electroretinography

ffERG

Full-field ERG

JBI

Joanna Briggs Institute

MCI

Mild Cognitive Impairment

mfERG

Multifocal ERG

MMSE

Mini-Mental State Examination

MoCA

Montreal Cognitive Assessment

PCC

Population–Concept–Context

pERG

Pattern ERG

PRISMA

Preferred Reporting Items for Systematic Review and Meta-analysis

PRISMA-ScR

Preferred Reporting Items for Systematic Review and Meta-analysis Extension for Scoping Reviews

WMS

Wechsler Memory Scale

Data Availability

Data Availability No new data will be collected for this study. All analyses will rely exclusively on previously published sources, with full methodological details provided in the manuscript and supplementary materials to ensure transparency, reproducibility, and independent verification. Any data extracted or generated as part of this review will be made available through the peer-reviewed publication to facilitate access and further use by the research community.

Funding Statement

The author(s) received no specific funding for this work.

References

  • 1.Hou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL, et al. Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol. 2019;15(10):565–81. doi: 10.1038/s41582-019-0244-7 [DOI] [PubMed] [Google Scholar]
  • 2.Bishop NA, Lu T, Yankner BA. Neural mechanisms of ageing and cognitive decline. Nature. 2010;464(7288):529–35. doi: 10.1038/nature08983 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Gireesh A, Sacker A, McMunn A, Bhatt R, Cadar D. Socioeconomic inequalities linked to the transitioning to neurocognitive disorders and mortality. Sci Rep. 2024;14(1):24690. doi: 10.1038/s41598-024-74125-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Sakaniwa R, Shirai K, Cador D, Saito T, Kondo K, Kawachi I, et al. Socioeconomic status transition throughout life and risk of dementia. JAMA Netw Open. 2024;7(5):e2412303. doi: 10.1001/jamanetworkopen.2024.12303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Muntaner C, Eaton WW, Miech R, O’Campo P. Socioeconomic position and major mental disorders. Epidemiol Rev. 2004;26:53–62. doi: 10.1093/epirev/mxh001 [DOI] [PubMed] [Google Scholar]
  • 6.Livingston G, Huntley J, Liu KY, Costafreda SG, Selbæk G, Alladi S, et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet. 2024;404(10452):572–628. doi: 10.1016/S0140-6736(24)01296-0 [DOI] [PubMed] [Google Scholar]
  • 7.Gustavsson A, Norton N, Fast T, Frölich L, Georges J, Holzapfel D, et al. Global estimates on the number of persons across the Alzheimer’s disease continuum. Alzheimers Dement. 2023;19(2):658–70. doi: 10.1002/alz.12694 [DOI] [PubMed] [Google Scholar]
  • 8.GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7(2):e105–25. doi: 10.1016/S2468-2667(21)00249-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hilsabeck RC, Lacritz LH, Colvin MK, Espe-Pfeifer P, Sperling SA, Arnett PA, et al. Cognition is a critical vital sign for older adults: The need for early detection of cognitive impairment is now. Policy Insights from the Behavioral and Brain Sciences. 2022;9(2):188–95. doi: 10.1177/23727322221110261 [DOI] [Google Scholar]
  • 10.Dhauria M, Mondal R, Deb S, Shome G, Chowdhury D, Sarkar S, et al. Blood-based biomarkers in Alzheimer’s disease: Advancing non-invasive diagnostics and prognostics. Int J Mol Sci. 2024;25(20):10911. doi: 10.3390/ijms252010911 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Chouliaras L, O’Brien JT. The use of neuroimaging techniques in the early and differential diagnosis of dementia. Mol Psychiatry. 2023;28(10):4084–97. doi: 10.1038/s41380-023-02215-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Lewczuk P, Hornegger J, Zimmermann R, Otto M, Wiltfang J, Kornhuber J. Neurochemical dementia diagnostics: Assays in CSF and blood. Eur Arch Psychiatry Clin Neurosci. 2008;258 Suppl 5:44–9. doi: 10.1007/s00406-008-5005-3 [DOI] [PubMed] [Google Scholar]
  • 13.Olsson B, Lautner R, Andreasson U, Öhrfelt A, Portelius E, Bjerke M, et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: A systematic review and meta-analysis. Lancet Neurol. 2016;15(7):673–84. doi: 10.1016/S1474-4422(16)00070-3 [DOI] [PubMed] [Google Scholar]
  • 14.Canestaro WJ, Bateman RJ, Holtzman DM, Monane M, Braunstein JB. Use of a blood biomarker test improves economic utility in the evaluation of older patients presenting with cognitive impairment. Popul Health Manag. 2024;27(3):174–84. doi: 10.1089/pop.2023.0309 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Schindler SE, Li Y, Li M, Despotis A, Park E, Vittert L, et al. Using Alzheimer’s disease blood tests to accelerate clinical trial enrollment. Alzheimers Dement. 2023;19(4):1175–83. doi: 10.1002/alz.12754 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Peredo R, Hébert M, Mérette C. Developing a clinical decision tool based on electroretinogram to monitor the risk of severe mental illness. BMC Psychiatry. 2022;22(1):718. doi: 10.1186/s12888-022-04375-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Youssef P, Nath S, Chaimowitz GA, Prat SS. Electroretinography in psychiatry: A systematic literature review. Eur Psychiatry. 2019;62:97–106. doi: 10.1016/j.eurpsy.2019.09.006 [DOI] [PubMed] [Google Scholar]
  • 18.London A, Benhar I, Schwartz M. The retina as a window to the brain-from eye research to CNS disorders. Nat Rev Neurol. 2013;9(1):44–53. doi: 10.1038/nrneurol.2012.227 [DOI] [PubMed] [Google Scholar]
  • 19.Banna HU, Slayo M, Armitage JA, Del Rosal B, Vocale L, Spencer SJ. Imaging the eye as a window to brain health: Frontier approaches and future directions. J Neuroinflammation. 2024;21(1):309. doi: 10.1186/s12974-024-03304-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Ptito M, Bleau M, Bouskila J. The retina: A window into the brain. Cells. 2021;10(12):3269. doi: 10.3390/cells10123269 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Perlman I: The electroretinogram: ERG. Webvision: The organization of the retina and visual system. 2007.
  • 22.Nowacka B, Lubiński W, Honczarenko K, Potemkowski A, Safranow K. Bioelectrical function and structural assessment of the retina in patients with early stages of Parkinson’s disease (PD). Doc Ophthalmol. 2015;131(2):95–104. doi: 10.1007/s10633-015-9503-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Hanson JVM, Hediger M, Manogaran P, Landau K, Hagenbuch N, Schippling S, et al. Outer retinal dysfunction in the absence of structural abnormalities in multiple sclerosis. Invest Ophthalmol Vis Sci. 2018;59(1):549–60. doi: 10.1167/iovs.17-22821 [DOI] [PubMed] [Google Scholar]
  • 24.Prager TC, Schweitzer FC, Peacock LW, Garcia CA. The effect of optical defocus on the pattern electroretinogram in normal subjects and patients with Alzheimer’s disease. Am J Ophthalmol. 1993;116(3):363–9. doi: 10.1016/s0002-9394(14)71355-8 [DOI] [PubMed] [Google Scholar]
  • 25.Silverstein SM, Fradkin SI, Demmin DL. Schizophrenia and the retina: Towards a 2020 perspective. Schizophr Res. 2020;219:84–94. doi: 10.1016/j.schres.2019.09.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Choi J, Lee H, Silverstein S, Kelly T, Byju A. Retinal neurosensory function assessed by electroretinography predicts cognition in older adults with diabetic retinopathy or advanced heart failure. The American Journal of Geriatric Psychiatry. 2023;31(10):e25–7. doi: 10.1016/j.jagp.2023.02.033 [DOI] [Google Scholar]
  • 27.Sartucci F, Borghetti D, Bocci T, Murri L, Orsini P, Porciatti V, et al. Dysfunction of the magnocellular stream in Alzheimer’s disease evaluated by pattern electroretinograms and visual evoked potentials. Brain Res Bull. 2010;82(3–4):169–76. doi: 10.1016/j.brainresbull.2010.04.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Friedel EBN, Schäfer M, Endres D, Maier S, Runge K, Bach M, et al. Electroretinography in adults with high-functioning autism spectrum disorder. Autism Res. 2022;15(11):2026–37. doi: 10.1002/aur.2823 [DOI] [PubMed] [Google Scholar]
  • 29.Roy M, Strippoli M-P, Hébert M, Preisig M, Marquet P. T7. Electroretinography for major psychiatric disorders in multicentric sites. Schizophrenia Bulletin. 2019;45(Supplement_2):S205–6. doi: 10.1093/schbul/sbz019.287 [DOI] [Google Scholar]
  • 30.Sen S, Saxena R, Vibha D, Tripathi M, Sharma P, Phuljhele S, et al. Detection of structural and electrical disturbances in macula and optic nerve in Alzheimer’s patients and their correlation with disease severity. Seminars in ophthalmology. Taylor & Francis. 2020. 116–25. [DOI] [PubMed] [Google Scholar]
  • 31.Friedel EBN, Hahn H-T, Maier S, Küchlin S, Reich M, Runge K, et al. Structural and functional retinal alterations in patients with paranoid schizophrenia. Transl Psychiatry. 2022;12(1):402. doi: 10.1038/s41398-022-02167-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Asanad S, Felix CM, Fantini M, Harrington MG, Sadun AA, Karanjia R. Retinal ganglion cell dysfunction in preclinical Alzheimer’s disease: an electrophysiologic biomarker signature. Scientific Reports. 2021; 11(1):6344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Molho W, Stiltner B, Raymond N, Kiely C, Trotti R, Harris C, et al. Retinal electrophysiological alterations are associated with cognition in early course psychosis. Schizophr Res. 2024;274:137–41. doi: 10.1016/j.schres.2024.09.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Moschos MM, Tagaris G, Markopoulos I, Margetis I, Tsapakis S, Kanakis M, et al. Morphologic changes and functional retinal impairment in patients with Parkinson disease without visual loss. Eur J Ophthalmol. 2011;21(1):24–9. doi: 10.5301/ejo.2010.1318 [DOI] [PubMed] [Google Scholar]
  • 35.Cabrera DeBuc D, Somfai GM, Arthur E, Kostic M, Oropesa S, Mendoza Santiesteban C. Investigating multimodal diagnostic eye biomarkers of cognitive impairment by measuring vascular and neurogenic changes in the retina. Front Physiol. 2018;9:1721. doi: 10.3389/fphys.2018.01721 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Ngoo QZ, Wan Hitam WH, Ab Razak A. Evaluation of retinal nerve fiber layer thickness, electroretinogram and visual evoked potential in patients with Alzheimer’s disease. J Ophthalmol. 2019;2019:6248185. doi: 10.1155/2019/6248185 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Krasodomska K, Lubiński W, Potemkowski A, Honczarenko K. Pattern electroretinogram (PERG) and pattern visual evoked potential (PVEP) in the early stages of Alzheimer’s disease. Doc Ophthalmol. 2010;121(2):111–21. doi: 10.1007/s10633-010-9238-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Parisi V, Restuccia R, Fattapposta F, Mina C, Bucci MG, Pierelli F. Morphological and functional retinal impairment in Alzheimer’s disease patients. Clin Neurophysiol. 2001;112(10):1860–7. doi: 10.1016/s1388-2457(01)00620-4 [DOI] [PubMed] [Google Scholar]
  • 39.Katz B, Rimmer S, Iragui V, Katzman R. Abnormal pattern electroretinogram in Alzheimer’s disease: Evidence for retinal ganglion cell degeneration?. Ann Neurol. 1989;26(2):221–5. doi: 10.1002/ana.410260207 [DOI] [PubMed] [Google Scholar]
  • 40.Trick GL, Barris MC, Bickler-Bluth M. Abnormal pattern electroretinograms in patients with senile dementia of the Alzheimer type. Ann Neurol. 1989;26(2):226–31. doi: 10.1002/ana.410260208 [DOI] [PubMed] [Google Scholar]
  • 41.Nesher R, Trick GL. The pattern electroretinogram in retinal and optic nerve disease. A quantitative comparison of the pattern of visual dysfunction. Doc Ophthalmol. 1991;77(3):225–35. doi: 10.1007/BF00161370 [DOI] [PubMed] [Google Scholar]
  • 42.Mavilio A, Sisto D, Prete F, Guadalupi V, Dammacco R, Alessio G. RE-PERG in early-onset Alzheimer’s disease: A double-blind, electrophysiological pilot study. PLoS One. 2020;15(8):e0236568. doi: 10.1371/journal.pone.0236568 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Demmin DL, Netser R, Roché MW, Thompson JL, Silverstein SM. People with current major depression resemble healthy controls on flash Electroretinogram indices associated with impairment in people with stabilized schizophrenia. Schizophr Res. 2020;219:69–76. doi: 10.1016/j.schres.2019.07.024 [DOI] [PubMed] [Google Scholar]
  • 44.Hébert M, Mérette C, Paccalet T, Émond C, Gagné AM, Sasseville A, et al. Light evoked potentials measured by electroretinogram may tap into the neurodevelopmental roots of schizophrenia. 2015. [DOI] [PubMed]
  • 45.Hébert M, Mérette C, Paccalet T, Gagné A-M, Maziade M. Electroretinographic anomalies in medicated and drug free patients with major depression: Tagging the developmental roots of major psychiatric disorders. Prog Neuropsychopharmacol Biol Psychiatry. 2017;75:10–5. doi: 10.1016/j.pnpbp.2016.12.002 [DOI] [PubMed] [Google Scholar]
  • 46.Hébert M, Mérette C, Gagné A-M, Paccalet T, Moreau I, Lavoie J, et al. The electroretinogram may differentiate schizophrenia from bipolar disorder. Biol Psychiatry. 2020;87(3):263–70. doi: 10.1016/j.biopsych.2019.06.014 [DOI] [PubMed] [Google Scholar]
  • 47.Nightingale S, Mitchell KW, Howe JW. Visual evoked cortical potentials and pattern electroretinograms in Parkinson’s disease and control subjects. J Neurol Neurosurg Psychiatry. 1986;49(11):1280–7. doi: 10.1136/jnnp.49.11.1280 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Friedel EBN, Tebartz van Elst L, Schmelz C, Ebert D, Maier S, Endres D, et al. Replication of reduced pattern electroretinogram amplitudes in depression with improved recording parameters. Front Med (Lausanne). 2021;8:732222. doi: 10.3389/fmed.2021.732222 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Boudriot E, Gabriel V, Popovic D, Pingen P, Yakimov V, Papiol S, et al. Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk. Biol Psychiatry. 2024;96(10):792–803. doi: 10.1016/j.biopsych.2024.04.014 [DOI] [PubMed] [Google Scholar]
  • 50.Liu F, Wang H, Zou F, Wang G, Li S, Ou P, et al. Electrophysiological evidence of visual dysfunction in SCA3: PERG/PVEP as novel biomarkers for ataxia severity and cognitive decline. Movement Disorders. 2026. [DOI] [PubMed] [Google Scholar]
  • 51.Pearl JR, Heath LM, Bergey DE, Kelly JP, Smith C, Laurino MY, et al. Enhanced retinal responses in Huntington’s disease patients. J Huntingtons Dis. 2017;6(3):237–47. doi: 10.3233/JHD-170255 [DOI] [PubMed] [Google Scholar]
  • 52.Constable PA, Lim JKH, Thompson DA. Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies. Front Neurosci. 2023;17:1215097. doi: 10.3389/fnins.2023.1215097 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Lavoie J, Illiano P, Sotnikova TD, Gainetdinov RR, Beaulieu J-M, Hébert M. The electroretinogram as a biomarker of central dopamine and serotonin: Potential relevance to psychiatric disorders. Biol Psychiatry. 2014;75(6):479–86. doi: 10.1016/j.biopsych.2012.11.024 [DOI] [PubMed] [Google Scholar]
  • 54.Constable PA, Gaigg SB, Bowler DM, Jägle H, Thompson DA. Full-field electroretinogram in autism spectrum disorder. Doc Ophthalmol. 2016;132(2):83–99. doi: 10.1007/s10633-016-9529-y [DOI] [PubMed] [Google Scholar]
  • 55.Peredo R, Gagné A-M, Gilbert E, Hébert M, Maziade M, Mérette C. Electroretinography may reveal cognitive impairment among a cohort of subjects at risk of a major psychiatric disorder. Psychiatry Res. 2020;291:113227. doi: 10.1016/j.psychres.2020.113227 [DOI] [PubMed] [Google Scholar]
  • 56.Ariello LE, Faria D de P, Andrade T de S, Oyamada MK, Cunha LP, Westenhofen GK, et al. Inner and outer retinal abnormalities detected in Alzheimer’s disease subjects diagnosed by amyloid PET not revealed when classified based on clinical criteria. Alzheimers Dement (Amst). 2025;17(4):e70218. doi: 10.1002/dad2.70218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Liao C, Xu J, Chen Y, Ip NY. Retinal dysfunction in Alzheimer’s disease and implications for biomarkers. Biomolecules. 2021;11(8):1215. doi: 10.3390/biom11081215 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Justino L, Kergoat M, Bergman H, Chertkow H, Robillard A, Kergoat H. Neuroretinal function is normal in early dementia of the Alzheimer type. Neurobiol Aging. 2001;22(4):691–5. doi: 10.1016/s0197-4580(01)00234-2 [DOI] [PubMed] [Google Scholar]
  • 59.Kergoat H, Kergoat M-J, Justino L, Chertkow H, Robillard A, Bergman H. Visual retinocortical function in dementia of the Alzheimer type. Gerontology. 2002;48(4):197–203. doi: 10.1159/000058350 [DOI] [PubMed] [Google Scholar]
  • 60.Saad A, Turgut F, Sommer C, Becker M, DeBuc D, Barboni M, et al. The use of the RETeval portable electroretinography device for low-cost screening: A mini-review. Klin Monbl Augenheilkd. 2024;241(4):533–7. doi: 10.1055/a-2237-3814 [DOI] [PubMed] [Google Scholar]
  • 61.“Cordoba N, Daza S, Constable PA, Posada-Quintero HF. Design of a Smartphone-Based Clinical Electroretinogram Recording System. 2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2024. 1–2. 10.1109/memea60663.2024.10596799 [DOI]
  • 62.Carter P, Gordon-Reid A, Shawkat F, Self JE. Comparison of the handheld RETeval ERG system with a routine ERG system in healthy adults and in paediatric patients. Eye (Lond). 2021;35(8):2180–9. doi: 10.1038/s41433-020-01221-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Komatsu H, Onoguchi G, Silverstein SM, Jerotic S, Sakuma A, Kanahara N, et al. Retina as a potential biomarker in schizophrenia spectrum disorders: A systematic review and meta-analysis of optical coherence tomography and electroretinography. Mol Psychiatry. 2024;29(2):464–82. doi: 10.1038/s41380-023-02340-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Lee IO, Skuse DH, Constable PA, Marmolejo-Ramos F, Olsen LR, Thompson DA. The electroretinogram b-wave amplitude: A differential physiological measure for Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder. J Neurodev Disord. 2022;14(1):30. doi: 10.1186/s11689-022-09440-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Felix C, Asanad S, Karanjia R, Harrington M, Sadun AA. In vivo assessment of retinal ganglion cells in human preclinical Alzheimer’s disease using electroretinography. Investigative Ophthalmology & Visual Science. 2019;60(9):6443. [Google Scholar]
  • 66.Manjur SM, Diaz LRM, Lee IO, Skuse DH, Thompson DA, Marmolejos-Ramos F, et al. Detecting autism spectrum disorder and attention deficit hyperactivity disorder using multimodal time-frequency analysis with machine learning using the electroretinogram from two flash strengths. J Autism Dev Disord. 2025;55(4):1365–78. doi: 10.1007/s10803-024-06290-w [DOI] [PubMed] [Google Scholar]
  • 67.Netser R, Demmin DL, Dobkin R, Goldstein A, Roché M, Netser Zernik A, et al. Flash electroretinography parameters and Parkinson’s disease. J Parkinsons Dis. 2021;11(1):251–9. doi: 10.3233/JPD-191830 [DOI] [PubMed] [Google Scholar]
  • 68.Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143. doi: 10.1186/s12874-018-0611-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Smith SA, Duncan AA. Systematic and scoping reviews: A comparison and overview. Seminars in Vascular Surgery. Elsevier. 2022. 464–9. [DOI] [PubMed] [Google Scholar]
  • 70.Peters MDJ, Marnie C, Colquhoun H, Garritty CM, Hempel S, Horsley T, et al. Scoping reviews: Reinforcing and advancing the methodology and application. Syst Rev. 2021;10(1):263. doi: 10.1186/s13643-021-01821-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Arksey H, O’Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology. 2005;8(1):19–32. doi: 10.1080/1364557032000119616 [DOI] [Google Scholar]
  • 72.Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: Elaboration and explanation. BMJ. 2015;349(jan02 1):g7647–g7647. doi: 10.1136/bmj.g7647 [DOI] [PubMed] [Google Scholar]
  • 73.Peters MDJ, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. 2015;13(3):141–6. doi: 10.1097/XEB.0000000000000050 [DOI] [PubMed] [Google Scholar]
  • 74.Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med. 2018;169(7):467–73. doi: 10.7326/M18-0850 [DOI] [PubMed] [Google Scholar]
  • 75.Arevalo‐Rodriguez I, Smailagic N, Figuls MR, Ciapponi A, Sanchez‐Perez E, Giannakou A, et al. Mini‐Mental State Examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database of Systematic Reviews. 2015;(3). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Schultz-Larsen K, Lomholt RK, Kreiner S. Mini-mental status examination: A short form of MMSE was as accurate as the original MMSE in predicting dementia. J Clin Epidemiol. 2007;60(3):260–7. doi: 10.1016/j.jclinepi.2006.06.008 [DOI] [PubMed] [Google Scholar]
  • 77.Hobson J. The montreal cognitive assessment (MoCA). Occup Med (Lond). 2015;65(9):764–5. doi: 10.1093/occmed/kqv078 [DOI] [PubMed] [Google Scholar]
  • 78.Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9. doi: 10.1111/j.1532-5415.2005.53221.x [DOI] [PubMed] [Google Scholar]
  • 79.J. Fray P, W. Robbins T, J. Sahakian B. Neuorpsychiatyric applications of CANTAB. Int J Geriat Psychiatry. 1996;11(4):329–36. doi: 10.1002/(sici)1099-1166(199604)11:4<329::aid-gps453>3.0.co;2-6 [DOI] [Google Scholar]
  • 80.Benton SA. Benton visual retention test. 1992. [Google Scholar]
  • 81.Wechsler D. Wechsler memory scale. 1945. [Google Scholar]
  • 82.Simpson JR. DSM-5 and neurocognitive disorders. J Am Acad Psychiatry Law. 2014;42(2):159–64. [PubMed] [Google Scholar]
  • 83.Looi JC, Velakoulis D. Major and minor neurocognitive disorders in DSM-5: The difference between the map and the terrain. Aust N Z J Psychiatry. 2014;48(3):284–6. doi: 10.1177/0004867413514493 [DOI] [PubMed] [Google Scholar]
  • 84.Uysal S. Diagnostic coding of neurocognitive disorders: A roadmap to ICD-10-CM. Clin Neuropsychol. 2025;39(1):1–16. doi: 10.1080/13854046.2024.2345925 [DOI] [PubMed] [Google Scholar]
  • 85.Jack CR, Albert MS, Knopman DS, McKhann GM, Sperling RA, Carrillo MC, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):257–62. doi: 10.1016/j.jalz.2011.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan SE, Ellis S, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline. BMJ. 2020;368:l6890. doi: 10.1136/bmj.l6890 [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

S1 Table. PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) 2015 checklist: Recommended items to address in a systematic review protocol.

(DOCX)

pone.0352593.s001.docx (27.6KB, docx)

Data Availability Statement

Data Availability No new data will be collected for this study. All analyses will rely exclusively on previously published sources, with full methodological details provided in the manuscript and supplementary materials to ensure transparency, reproducibility, and independent verification. Any data extracted or generated as part of this review will be made available through the peer-reviewed publication to facilitate access and further use by the research community.


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