Brain Functional Alterations of Multilayer Network After Stroke: A Case–Control Study Based on EEG Signals

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yingying Hao;Xiaoling Chen;Juan Wang;Tengyu Zhang;Haihong Zhao;Yinan Yang;Ping Xie
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Abstract

Effective description of the brain function after stroke is the key to accurate rehabilitation assessment, and it is of great significance to explore the nonlinear complexity characteristics of the brain from the perspective of complex networks. In this study, we investigated the brain functional connectivity alterations after stroke by constructing a multilayer network model. First, we obtained multichannel EEG signals in different frequency bands ( $\theta $ , $\alpha $ , $\beta $ , and $\gamma $ ) during the multijoint compound movement. Furthermore, we introduced the weighted phase lag index (wPLI) and Kullback–Leibler (KL)-modulation index (MI) to construct the within-frequency subnetworks (WFNs) and cross-frequency subnetworks (CFNs), respectively. Then, the multilayer network was constructed by the aforementioned subnetworks. Calculating the multiplex participation coefficient (MPC) and multiplex clustering coefficient (MCC) to explore differences in connection strength within subnetworks. The algebraic connectivity was used to compare the differences in multilayer network topology from a global perspective. $\beta $ frequency band WFN showed significantly stronger connectivity in a healthy group compared with stroke patients. Conversely, the $\theta $ - $\gamma $ CFN in patients exhibited significantly higher connectivity strength compared with controls, while the opposite was true for $\alpha $ - $\beta $ CFN. There were significant differences in network nodes between the left and right brain regions in controls, whereas the distribution of MPC in both hemispheres was evenly distributed in the patients. Global metrics indicated that the algebraic connectivity of the patients’ brain network was significantly lower than that of the controls. These findings have important implications for understanding the brain functional connectivity in stroke and developing effective rehabilitation and therapeutic strategies.
脑卒中后多层网络的脑功能改变:基于脑电信号的病例对照研究
有效描述脑卒中后的脑功能是准确进行康复评估的关键,而从复杂网络的角度探索脑的非线性复杂性特征具有重要意义。本研究通过构建多层网络模型研究了脑卒中后大脑功能连接的改变。首先,我们获得了多关节复合运动过程中不同频段(θ、α、β和γ)的多通道脑电信号。此外,我们还引入了加权相位滞后指数(wPLI)和Kullback-Leibler(KL)-调制指数(MI),分别构建了频率内子网络(WFN)和跨频率子网络(CFN)。然后,由上述子网络构建多层网络。计算多路参与系数(MPC)和多路聚类系数(MCC),探讨子网络内部连接强度的差异。利用代数连接性从全局角度比较多层网络拓扑结构的差异。 与脑卒中患者相比,健康组的贝塔频带WFN显示出明显更强的连接性。相反,与对照组相比,患者的$theta $ - $gamma $ CFN表现出明显更高的连接强度,而$α $ - $beta $ CFN则相反。对照组左右脑区域的网络节点存在明显差异,而患者两半球的 MPC 分布均匀。全局指标表明,患者大脑网络的代数连接性明显低于对照组。这些发现对了解脑卒中患者的大脑功能连接以及制定有效的康复和治疗策略具有重要意义。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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