EEG microstate complexity for aiding early diagnosis of Alzheimer's disease

Introduction: Electroencephalogram (EEG) is a potentially useful clinical tool for aiding diagnosis of Alzheimer9s disease (AD). We hypothesized we can increase the accuracy of EEG for aiding diagnosis of AD using microstates, which are epochs of quasi-stability at the millisecond scale. Methods: EEG was collected from two independent cohorts of AD and control participants and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. Microstates were analysed, including a novel measure of complexity. Results: Microstate complexity significantly decreased in AD, and when combined with a spectral EEG measure, could classify AD with sensitivity and specificity >80%. These results were validated on an independent cohort and were also found to be generalizable to predict progression from MCI to AD. Additionally, microstates associated with the frontoparietal network were altered in AD. Discussion: EEG has potential to be a non-invasive functional biomarker that predicts progression from MCI to AD.