Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs

Abstract

Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with 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 (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, 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. The results from 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 leave-one-participant-out cross-validation (LOPO-CV) classification 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 other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

Electroencephalogram Criticality in Cognitive Impairment: A Monitoring Biomarker?

“…Flores-Sandoval et al demonstrated a lower EEG spectral power ratio in patients with amyloid-positive amnestic mild cognitive impairment compared with cognitively normal subjects [40]. Trihn et al evaluated task-induced intra-subject spectral power variability of resting-state EEGs and suggested its use in the early detection of MCI [41]. Our findings are in line with previously conducted research and indicate the possible use of EEG criticality as a screening tool in patients undergoing cognitive training or rehabilitation.…”

Non-Invasive Solutions to Identify Distinctions Between Healthy and Mild Cognitive Impairments Participants

“…Several studies have investigated different biomarkers in order to diagnose and assess neurodegenerative disease and MCI using biosensors. However, these biomarkers are not ideal solutions for healthcare systems, because they are expensive, time-consuming, and invasive [30] . Furthermore, several attempts have been made to develop biomarkers for the diagnosis of MCI [31] , [32] .…”

Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

“…There is a need for economic, objectively quantifiable, and non-invasive measures of identification and diagnosis to increase therapeutic options and avoid progression from MCI to dementia. Currently, most EEG exams or training models are based on historical diagnoses using the MMSE ( Al-Nuaimi et al, 2021 ; Trinh et al, 2021 ), which reinforces the need for improved and more reliable diagnostic tools.…”