Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023
Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.
СтатьяПоддержка правительства СШАПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Deng Jinxian, Sun Boxin, Kavcic Voyko, Liu Mingyan, Giordani Bruno, Li Tongtong
Аннотация
BACKGROUND: Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency.
METHODS: Our research is based on resting-state electroencephalography (EEG) and the current dataset includes 137 consensus-diagnosed, community-dwelling Black Americans (ages 60-90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time-varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity-based score.
RESULTS: The leave-one-out cross-validation accuracy is 91.97% and 3-fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability-limited subset sample is 84.61%.
CONCLUSION: The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.
METHODS: Our research is based on resting-state electroencephalography (EEG) and the current dataset includes 137 consensus-diagnosed, community-dwelling Black Americans (ages 60-90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time-varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity-based score.
RESULTS: The leave-one-out cross-validation accuracy is 91.97% and 3-fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability-limited subset sample is 84.61%.
CONCLUSION: The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.