Screening for amyloid positivity in patients with mild cognitive impairment using an electroencephalography-driven functional network

Poster Session D - Monday, March 31, 2025, 8:00 – 10:00 am EDT, Back Bay Ballroom/Republic Ballroom

Hayom Kim^1,2 (happyhy3@gmail.com), Jung Bin Kim^1,2; ¹Korea University College of Medicine, ²Korea University Anam Hospital

Introduction While monoclonal antibodies targeting Amyloid-β (Aβ) offer disease-modifying potential, identifying suitable candidates at the mild cognitive impairment (MCI) stage in Alzheimer's disease (AD) remains critical. This study explored whether resting-state electroencephalography (EEG) network indices can discriminate Aβ-positive from Aβ- negative groups in MCI. Methods Participants with cognitive decline were classified into subjective cognitive decline (SCD), MCI, or dementia groups. Aβ-positivity was determined using 18F-flutemetamol PET/CT. Resting-state EEG data were analyzed for functional connectivity (FC) using weighted phase lag index, and global network properties were assessed via graph theoretical analysis. Machine learning algorithms evaluated the discriminative ability of these metrics for Aβ-positivity in MCI. Results Among 100 participants (19 SCD, 55 MCI, 26 dementia), 53 were Aβ-positive. In the MCI subgroup, Aβ-positive individuals (n = 28) exhibited lower strength, global efficiency, local efficiency, clustering coefficient, and transitivity in the delta band compared to Aβ-negative (all p < 0.05). Machine learning algorithms using these features achieved an AUC of up to 0.835. Conclusion Resting-state EEG network indices could be a non-invasive, cost-effective tool for screening Aβ-positivity in MCI. These findings suggest the potential of global network measurements as biomarkers for early diagnosis, disease monitoring, and therapeutic evaluation in the era of monoclonal antibody therapies for AD.

Topic Area: METHODS: Electrophysiology