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Identifying Individuals with Mild Cognitive Impairment using Working Memory Induced Intra-Subject Variability of Resting-State EEGs
Frontiers in Computational Neuroscience ( IF 3.2 ) Pub Date : 2021-06-30 , DOI: 10.3389/fncom.2021.700467
Thanh-Tung Trinh , Chia-Fen Tsai , Yu-Tsung Hsiao , Chun-Ying Lee , Chien-Te Wu , Yi-Hung Liu

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.

中文翻译:

使用工作记忆诱导的静息状态脑电图的受试者内变异性识别轻度认知障碍的个体

患有轻度认知障碍 (MCI) 的个体处于发展为痴呆症(例如阿尔茨海默病、AD)的高风险中。早期检测 MCI 的可靠且有效的方法已成为一项关键挑战。尽管与其他昂贵或有风险的实验室测试相比,脑电图 (EEG) 似乎是早期检测 MCI 的理想替代措施,但在健康对照 (HC) 和 MCI 患者之间寻找有效的 EEG 特征分类仍有待探索。在这里,我们提出,任务引起的静息态脑电图的受试者内变异性可以成为早期检测 MCI 的令人鼓舞的候选脑电图特征。具体来说,我们提取了在参与者执行认知耗尽的工作记忆任务之前和之后的静息状态 EEG 的任务诱导的受试者内频谱功率变异性(通过运行之间的相似性测量)作为候选特征。我们对 74 名参与者(23 名患有 AD、24 名患有 MCI、27 名 HC)的结果表明,HC 组中额叶和中央头皮区域的运行间相似性高于 AD 或 MCI 组。此外,使用特征选择方案和支持向量机 (SVM) 分类器,运行间相似性在 MCI 和 HC (80.39%) 组之间以及 AD 与 HC 组之间 (78%) 之间显示出令人鼓舞的分类性能,其分类性能优于从单次运行静息态脑电图中提取的光谱功率。
更新日期:2021-06-30
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