A method for diagnosis support of mild cognitive impairment through EEG rhythms source location during working memory tasks

被引:13
作者
San-Martin, Rodrigo [1 ]
Johns, Erin [2 ]
Mamani, Godofredo Quispe [3 ,4 ]
Tavares, Guilherme [1 ]
Phillips, Natalie A. [2 ]
Fraga, Francisco J. [3 ]
机构
[1] Fed Univ ABC, Ctr Math Comp & Cognit, Santo Andre, SP, Brazil
[2] Concordia Univ, Dept Psychol, Montreal, PQ, Canada
[3] Fed Univ ABC, Engn Modelling & Appl Social Sci Ctr, Santo Andre, SP, Brazil
[4] Univ Nacl Altiplano UNAP, Dept Estadist & Informat, Puno, Peru
基金
巴西圣保罗研究基金会;
关键词
Mild Cognitive Impairment; Alzheimer's disease; Working memory; Source localization (LORETA); Machine learning; Support vector machine (SVM); BRAIN ELECTROMAGNETIC TOMOGRAPHY; EARLY ALZHEIMERS-DISEASE; GRAY-MATTER LOSS; FUNCTIONAL CONNECTIVITY; DIFFERENTIAL-DIAGNOSIS; COMPLEXITY ANALYSIS; FMRI; DEMENTIA; METAANALYSIS; BIOMARKERS;
D O I
10.1016/j.bspc.2021.102499
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Objective: We investigated group differences in current source density (CSD) patterns from EEG signals before and after a working memory (WM) task performed by mild cognitive impaired (MCI) subjects and healthy elderly (HE). Methods: EEG was recorded during N-back WM tasks in 41 age-, sex- and education-matched participants divided into MCI (N = 19) and HE (N = 22) groups. EEG epochs were divided into pre- and post-stimulus periods, named herein as working memory epochs (WME) and event-related epochs (ERE), respectively. Frequency-domain CSD was extracted for both WME and ERE on delta, theta, alpha, beta, and gamma bands using LORETA. Group comparisons were performed under Statistical non-Parametric Mapping. Moreover, after feature selection, we performed cross-validation with a Support Vector Machine (SVM) classifier. Results: MCI displayed increased spectral CSD on delta and theta (low-frequency) and decreased spectral CSD on (high-frequency) alpha and beta bands when compared to HE. Surprisingly, MCI patients presented an increase in gamma at precuneus and a decrease at occipital cortex. Group prediction through SVM achieved 96% accuracy, 98% specificity and 93% sensitivity when WME and ERE spectral CSD features were combined. Conclusions: Our findings confirmed the overall EEG slowing observed in classical MCI resting-state EEG literature as well as alpha desynchronization changes observed in task-related EEG literature. Furthermore, they also revealed MCI abnormalities in the gamma band. Significance: Our frequency-domain analysis of CSD patterns in task-related EEG, focusing both on pre- and poststimulus periods, may be a clinically relevant tool to support MCI diagnosis.
引用
收藏
页数:10
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