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ABSTRACT: Background
Subjective cognitive decline (SCD) has been recognized as a preclinical stage of Alzheimer's disease. However, the identification of early functional brain changes remains challenging. This study investigated the functional brain changes in SCD using longitudinal EEG and evaluate the feasibility of EEG features as scalable biomarkers for identifying amyloid burden and cognitive decline using an interpretable machine learning framework.Methods
We analyzed 120 individuals with SCD enrolled in a multicenter prospective cohort (the CoSCo study) at baseline and after a 2-year follow-up. Participants were classified as amyloid-positive (A + SCD) or amyloid-negative (A - SCD). Spectral power and graph theory-based network analyses were conducted. Also, we trained machin
SUBMITTER: Ryoo N
PROVIDER: S-EPMC12751729 | biostudies-literature | 2025 Dec
REPOSITORIES: biostudies-literature