Analysis of Alzheimer's Disease–Related Alterations in EEG Dynamics Using Integrated Instantaneous Frequency–Amplitude Microstates

Abstract

Disruptions in large-scale electroencephalography (EEG) dynamics are a hallmark of Alzheimer's disease (AD). However, conventional microstate analyses rely primarily on amplitude-based features and may overlook phase-related alterations in network organization. This study examined whether integrating instantaneous frequency (IF) and instantaneous amplitude (IA) into a unified microstate framework could better characterize AD-related EEG dynamics. Resting-state EEG data were recorded from 16 patients with AD and 18 healthy controls using 16 scalp electrodes. IF and IA were derived via the Hilbert transformation in the theta-to-alpha band (4-13 Hz), spatially normalized, and jointly modeled using a hidden Markov model with four hidden states to define the integrated IF-IA microstates. Temporal properties, including dwell time, fractional occurrence, and transition probabilities, were compared between groups. The analysis identified four recurrent IF-IA microstates. Patients with AD showed a significantly reduced occurrence of one integrated state together with a complementary, nonsignificant increase in another, whereas transition probabilities did not differ significantly between groups. These findings suggest that AD-related impairment is reflected in the altered prevalence of integrated phase and amplitude brain states-indicative of a reorganization of the joint coordination between IF and IA-supporting integrated IF-IA microstates as a complementary EEG-based approach for probing neurodegenerative network dysfunction.