EEG Microstate Alterations in Eyes-Open and Eyes-Closed Resting States Across the Alzheimer’s Disease Continuum

Abstract

Background/Objective: Quantitative electroencephalography (qEEG) microstates, recorded under both eyes-open (EOR) and eyes-closed (ECR) resting conditions, provide a powerful neurophysiological approach for capturing the temporal dynamics of cognitive processing. Nevertheless, microstate syntax remains poorly delineated in mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The present study seeks to identify potential drivers of altered microstate topography and temporal dynamics across advancing stages of cognitive decline. Methods: Resting-state EEG (rEEG) was recorded from 60 participants (40–90 years old) in each of three groups: MCI, AD, and healthy controls (HC), under both EOR and ECR conditions. After artifact rejection with EEGLAB in MATLAB R2024a, the final dataset comprised 180 clean recordings per condition (EOR and ECR) and 360 recordings in total. Microstate analysis was performed using the MICROSTATELAB toolbox. Potential group differences in microstate topography were examined with topographic analysis of variance (TANOVA). Four canonical microstates were extracted and labeled A, B, C, and E, following the well-established classification scheme. Results: Analysis of microstate topographies revealed significant group-level differences in the EOR condition for Microstate A (auditory network; p = 0.003), Microstate C (salience network; p = 0.004), and Microstate E (executive network; p = 0.036). In contrast, no significant between-group differences emerged under the ECR condition (all p > 0.05). Within-group comparisons indicated that Microstate B (visual network) was the only class to differ between the two resting conditions in healthy controls and patients with MCI. In the AD group, however, condition-related differences extended to Microstates A, C, and E, suggesting a more distributed disruption of network dynamics in advanced disease. Temporally, Microstate B coverage was the only parameter to show a significant between-group difference, being greater in MCI than in AD under the EOR condition (p = 0.031); visual trends of increasing Microstate A duration and decreasing Microstate C occurrence toward AD did not reach statistical significance. Conclusions: This study’s rEEG microstate analysis uncovered condition-dependent and disease-sensitive alterations across the HC, MCI, and AD groups. The EOR condition proved more diagnostically informative, with significant group-level topographic differences emerging for Microstates A, C, and E, whereas the ECR condition yielded no reliable between-group effects. Microstate B coverage showed the only significant temporal alteration, distinguishing MCI from AD under EOR, while Microstate A and Microstate C showed non-significant trends warranting replication in larger cohorts.