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认知和静息状态下 MCI-AD 的 EEG 信号的复杂网络分析。

Complex network analysis of MCI-AD EEG signals under cognitive and resting state.

机构信息

Department of Electrical Engineering, National Institute of Technology, Calicut 673601, Kerala, India.

出版信息

Brain Res. 2020 May 15;1735:146743. doi: 10.1016/j.brainres.2020.146743. Epub 2020 Feb 27.

Abstract

OBJECTIVE

The purpose of this study is to characterize functional connectivity changes in mild cognitive impaired Alzheimer's disease (MCI-AD) under resting and cognitive task conditions.

METHOD

EEG signals were recorded under resting states (Eyes closed (EC) and Eyes open (EO)) and cognitive states (Mental Arithmetic Eyes closed (MAEC) and Mental Arithmetic Eyes open (MAEO)) conditions. Functional connectivity metrics were calculated using weighted phase lag index (WPLI). Topological features of the functional connectivity network were analyzed through minimum spanning tree (MST) algorithm. Betweenness centrality was estimated in five different regions of the brain to study hub importance.

RESULTS

An increase in values of eccentricity and diameter were observed in patient group in five frequency bands of delta, theta, alpha1, alpha 2 and beta bands under resting and cognitive states. A reduction in leaf fraction was observed in alpha 1 band of EO condition. The results indicated a reduction in functional integration in the brain network of MCI-AD patients. Analysis of MST parameters revealed a higher disintegrated network for the alpha band under EO protocol. The study of hub status in the network displayed an elevated hub status in the central region for the patient group under cognitive task. The study also observed increased integration in gamma band in MCI - AD subjects than healthy controls under cognitive load.

CONCLUSION

Disintegration of functional network in alpha band under eyes open protocol and elevated hub strength in central region during cognitive task could be distinguishing features that could aid early detection of AD.

摘要

目的

本研究旨在描述轻度认知障碍阿尔茨海默病(MCI-AD)在静息和认知任务状态下的功能连接变化。

方法

在静息状态(闭眼(EC)和睁眼(EO))和认知状态(闭眼心算(MAEC)和睁眼心算(MAEO))下记录 EEG 信号。使用加权相位滞后指数(WPLI)计算功能连接度量。通过最小生成树(MST)算法分析功能连接网络的拓扑特征。在大脑的五个不同区域估计介数中心度,以研究枢纽重要性。

结果

在静息和认知状态下,患者组在 delta、theta、alpha1、alpha2 和 beta 五个频带中观察到偏心度和直径值增加。EO 条件下 alpha1 带的叶分数减少。结果表明,MCI-AD 患者的大脑网络功能整合减少。MST 参数分析表明,EO 方案下的 alpha 带网络具有更高的不整合性。网络枢纽状态分析显示,在认知任务下,患者组的中央区域枢纽状态升高。研究还观察到认知负荷下 MCI-AD 受试者的 gamma 波段整合增加。

结论

EO 方案下 alpha 波段功能网络的不整合性和认知任务中中央区域枢纽强度的增加可能是有助于 AD 早期检测的特征。

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