Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs - Trinh, Thanh-Tung; Tsai, Chia-Fen; Hsiao, Yu-Tsung; Lee, Chun-Ying; Wu, Chien-Te; Liu, Yi-Hung (Frontiers in Computational Neuroscience, 2021)
Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e.g., Alzheimer disease, AD). A reliable and effective approach for early detections of MCI has become a critical challenge. Although compared to other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between Healthy Controls (HC) and individuals with MCI remains to be largely unexplored. Here we propose that the task-induced intra-subject variability of the resting-state EEGs can be an encouraging candidate EEG feature for the early detection of MCI. Specifically, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. Our results with 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to the spectral powers extracted from single run resting-state EEGs. Our results therefore suggest that the task-induced intra-subject EEG variability has the potential to serve as a neurophysiological feature for early detection of individuals with MCI.