Resting-State EEG captures functional network correlates of plasma p-Tau-217 in Alzheimer’s disease

Background: Scalable biomarkers are needed for early Alzheimer's disease (AD) detection. Plasma p-tau217 reflects AD pathology, while resting-state EEG captures functional brain alterations. Their relationship remains unclear. Methods: We enrolled 128 patients with subjective cognitive decline (SCD), mild cognitive impairment due to AD (AD-MCI), or AD dementia (AD-DEM), who underwent 32-channel EEG and plasma biomarker assessment. EEG features included spectral, aperiodic, phase-amplitude coupling, and complexity metrics. Machine learning was used to classify p-tau217 positivity. Results: AD-MCI and AD-DEM patients showed increased p-tau217 and spectral slowing (higher theta, lower alpha). While no correlations survived correction for multiple comparisons, stage-specific analyses revealed positive associations between theta power and p-tau217 in AD-MCI and AD-DEM. A random forest classifier achieved an AUC of 0.75 in predicting p-tau217 positivity. Conclusions: EEG captures functional alterations reflecting AD pathology beyond molecular measures, supporting its value as a complementary, non-invasive biomarker for early stratification in clinical settings.