Electroencephalography in Subjective Cognitive Decline and Mild Cognitive Impairment: Systematic Review of Biomarkers, Classification, and Prognostic Evidence
Journal of Medical Internet Research ·
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.
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.