Electroencephalography in Subjective Cognitive Decline and Mild Cognitive Impairment: Systematic Review of Biomarkers, Classification, and Prognostic Evidence.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Electroencephalography in Subjective Cognitive Decline and Mild Cognitive Impairment: Systematic Review of Biomarkers, Classification, and Prognostic Evidence.
Συγγραφείς:	Lu S; Key Laboratory of Tropical Translational Medicine of Ministry of Education, Department of Epidemiology, School of Public Health, Hainan Medical University, Haikou, Hainan, 571199, China, 86 13256406001., Hu S; Department of Neurology, The Second Affiliated Hospital of Hainan Medical University, Haikou, China., Huang J; Key Laboratory of Tropical Translational Medicine of Ministry of Education, Department of Epidemiology, School of Public Health, Hainan Medical University, Haikou, Hainan, 571199, China, 86 13256406001., Shi Y; Key Laboratory of Tropical Translational Medicine of Ministry of Education, Department of Epidemiology, School of Public Health, Hainan Medical University, Haikou, Hainan, 571199, China, 86 13256406001., Yu D; Key Laboratory of Tropical Translational Medicine of Ministry of Education, Department of Epidemiology, School of Public Health, Hainan Medical University, Haikou, Hainan, 571199, China, 86 13256406001., Cao W; Key Laboratory of Tropical Translational Medicine of Ministry of Education, Department of Epidemiology, School of Public Health, Hainan Medical University, Haikou, Hainan, 571199, China, 86 13256406001., Yin M; Library, Hainan Medical University, Haikou, Hainan, China., Petersen JD; Key Laboratory of Tropical Translational Medicine of Ministry of Education, Department of Epidemiology, School of Public Health, Hainan Medical University, Haikou, Hainan, 571199, China, 86 13256406001.; Department of Neurology, The Second Affiliated Hospital of Hainan Medical University, Haikou, China.; Department of Public Health, Research Unit for General Practice, University of Copenhagen, Copenhagen, Denmark., Huang B; Big Data Research Center, School of Intelligent Medicine and Technology, Hainan Medical University, No. 3 Xue Yuan Lu Road, Longhua District, Haikou, Hainan, 571199, China, 86 18976660867.; School of Software, Northwestern Polytechnical University, Xi' an, Shaanxi, China.
Πηγή:	Journal of medical Internet research [J Med Internet Res] 2026 Sep 30; Vol. 28, pp. e92709. Date of Electronic Publication: 2026 Sep 30.
Τύπος έκδοσης:	Journal Article; Systematic Review
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE
Imprint Name(s):	Publication: <2011- > : Toronto : JMIR Publications Original Publication: [Pittsburgh, PA? : s.n., 1999-
Ιατρικοί όροι (MeSH):	Cognitive Dysfunction/diagnosis , Cognitive Dysfunction/classification , Cognitive Dysfunction/physiopathology , Biomarkers , Electroencephalography*, Humans ; Disease Progression ; Prognosis
Περίληψη:	Background: Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are heterogeneous clinical states that may represent early or at-risk stages of Alzheimer disease (AD) and other dementias in some individuals. Improved characterization and risk stratification in these populations may facilitate timely evaluation and intervention. Electroencephalography (EEG), a noninvasive, cost-effective neurophysiological technique with high temporal resolution, holds significant potential for elucidating neural mechanisms and providing candidate neurophysiological markers associated with SCD and MCI. Objective: The study aims to systematically synthesize evidence on group-level EEG biomarkers, EEG-based classification models, the evaluation of EEG in screening or diagnostic pathways, and EEG-based prediction of progression in SCD and MCI. Methods: A search was conducted across PubMed, Web of Science, Cochrane Library, MEDLINE (via Ovid), Scopus, Wanfang Data, CQVIP, Yiigle, and CNKI databases to include reports published in English or Chinese, which reported group-level EEG differences, EEG-based classification, screening or diagnostic evaluations, or prognostic outcomes in SCD and MCI. A total of 2 independent reviewers screened the titles and abstracts. Prediction-model reports were assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool and Applicability Assessment for AI), prognostic-factor reports using QUIPS (Quality in Prognosis Studies), and other observational reports using design-specific Joanna Briggs Institute checklists. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) system. Results: A total of 20 reports were included, representing a maximum of 3853 confirmed-deduplicated participants after correction of a nested subsample and removal of confirmed duplicate participants across reports, of whom 2927 diagnosed with SCD/MCI/AD or other dementias. Of these, 10 reports were assessed as prediction or AI model reports, 9 as nondiagnostic observational reports, and 1 as a prognostic-factor study. Although potentially informative between-group EEG differences and preliminary model performance were reported, no conventional diagnostic test-accuracy study was identified. The certainty of evidence was very low across all 4 evidence bodies. Conclusions: The available evidence suggests that EEG is a promising noninvasive modality for characterizing neurophysiological alterations associated with SCD and MCI. EEG-based classification and prognostic models have also shown encouraging preliminary performance. However, the current evidence is more supportive of biomarker discovery and model development than of established clinical screening or diagnosis. Translation into routine practice will require prospective reports with standardized EEG procedures, representative clinical populations, prespecified thresholds, participant-level data separation, and independent external validation. These findings provide a basis for evaluating EEG as a potential adjunctive or triage tool in future clinical pathways. (© Sifei Lu, Sheng Hu, Junxuan Huang, Yehua Shi, Dee Yu, Wenting Cao, Mingzhang Yin, Jindong Ding Petersen, Binwen Huang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org).)
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Contributed Indexing:	Keywords: Alzheimer disease; electroencephalography; event-related potential; mild cognitive impairment; subjective cognitive decline
Substance Nomenclature:	0 (Biomarkers)
Entry Date(s):	Date Created: 20260930 Date Completed: 20260930 Latest Revision: 20260930
Update Code:	20261001
DOI:	10.2196/92709
PMID:	42814956
Βάση Δεδομένων:	MEDLINE

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ISSN:	1438-8871
DOI:	10.2196/92709