EEG Biomarkers for ATN Classification in Early Alzheimer’s Disease
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INTRODUCTION
Early detection of Alzheimer’s disease (AD) remains challenging. EEG offers a scalable, non-invasive tool for patient stratification, but its relationship to ATN-defined staging is poorly understood.
METHODS
EEG was recorded in 60 participants (SCD, MCI–, MCI+, N=20 per group) using a battery of tasks (N-back, auditory oddball, 40 Hz ASSR, resting-state). Event-related, spectral, connectivity, and complexity features were extracted, compared across groups, correlated with CSF and plasma biomarkers, and evaluated for classification performance.
RESULTS
Multiple EEG features showed discriminatory power among groups and correlated with amyloid and tau biomarkers. MCI– showed a cortical hyperexcitability profile. EEG added no value for ATN-based discrimination where plasma pTau217 performed near ceiling (AUC=0.96–0.99), but uniquely separated SCD from MCI (EEG AUC≈0.72–0.75) where plasma biomarkers failed (AUC≈0.32–0.33).
DISCUSSION
EEG biomarkers capture ATN-stage-dependent neurophysiological signatures, support a non-monotonic model of AD progression, and show promise as a first screening tool where plasma biomarkers are uninformative.
