Electroencephalography biomarkers for prodromal Alzheimer’s disease: applications in early diagnosis, treatment, and monitoring

Prodromal Alzheimer's disease (AD), represented by mild cognitive impairment (MCI) and subjective cognitive decline (SCD), represents a critical period for early intervention. Electroencephalography (EEG) represents a valuable, noninvasive technique for the detection of early neural alterations by recording online alterations in brain activity, that is, increased theta/delta power, reduction of alpha rhythms, and disrupted event-related potentials such as P300 and P3b. More sophisticated EEG biomarkers like connectivity patterns, fractal complexity, and microstates combined with machine learning are highly sensitive to differentiate prodromal AD from normal aging. EEG is also conducive to monitoring treatment response after pharmacologic and neuromodulatory treatment. With further development in high-density EEG, wearables, and AI analysis, EEG is poised to be a part of early diagnosis, personalized care, and longitudinal tracking of AD.