Electroencephalography functional network for screening amyloid positivity in mild cognitive impairment: a cross-sectional study.

OBJECTIVE: Monoclonal antibodies targeting amyloid-β (Aβ) show disease-modifying potential in Alzheimer's disease (AD), making early identification of Aβ-positive individuals at the mild cognitive impairment (MCI) stage essential. Functional network metrics derived from electroencephalography (EEG) may reflect Aβ-related network disruption and serve as viable screening tools. METHODS: This study included patients with cognitive decline who underwent 18F-flutemetamol PET/CT, EEG, and neuropsychological testing at Korea University Anam Hospital (2020-2024). Participants were categorized into subjective cognitive decline (SCD), MCI, or dementia. Resting-state EEG was analyzed using the weighted phase lag index to compute functional connectivity, followed by graph theoretical analysis to assess global network properties. Machine learning models were used to classify Aβ status in the MCI group based on EEG-derived features. RESULTS: Among 100 participants (19 SCD, 55 MCI, 26 dementia), 53 were Aβ-positive. In MCI, Aβ-positive individuals (n = 28) showed significantly reduced delta-band network strength, global/local efficiency, clustering coefficient, and transitivity (all p < 0.05). Classification models reached an AUC of up to 0.850. CONCLUSIONS: Resting-state EEG network analysis provides a non-invasive, cost-effective approach for screening Aβ positivity in MCI. SIGNIFICANCE: EEG-based global network measures may aid in early AD diagnosis and patient selection for anti-Aβ therapies.