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脑电图微状态特异性功能连接与脑动力学中与中风相关的改变

EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics.

作者信息

Hao Zexuan, Zhai Xiaoxue, Cheng Dandan, Pan Yu, Dou Weibei

机构信息

Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.

Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.

出版信息

Front Neurosci. 2022 May 11;16:848737. doi: 10.3389/fnins.2022.848737. eCollection 2022.

Abstract

The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients ( = 31) and healthy populations ( = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.

摘要

大脑作为一个复杂的动态分布式信息处理系统,即使在静息状态下,也涉及大规模脑网络的协调,如神经同步和快速脑状态转换。然而,脑状态的神经机制以及脑损伤后功能障碍对脑动力学的影响仍知之甚少。为此,我们提出了一种基于微状态的方法来探索与每个微状态类别相关的功能连接模式。我们利用闭眼静息态脑电图数据的微状态特征,研究亚急性中风患者(n = 31)和健康人群(n = 23)之间的微状态动力学是否存在差异,并进一步检查微状态特征与行为之间的相关性。本研究的一个重要发现是,每个微状态类别都与一种独特的功能连接模式相关,并且在不同组(包括一个独立数据集)中高度一致。虽然中风患者的连接模式有所减弱,但模式的框架在一定程度上得以保留。尽管如此,与健康对照组相比,中风患者在微状态A、B和C的大多数参数上表现出显著差异。值得注意的是,微状态C表现出与微状态A和B相反的差异模式。另一方面,左侧与右侧中风患者以及下肢训练前后患者的所有微状态参数均无显著差异。此外,仅使用微状态特征开发了支持向量机(SVM)模型,并在患者和对照组之间实现了适度的区分。此外,在微状态特异性功能连接与下肢运动评分之间观察到显著的负相关。总体而言,这些结果表明,中风患者微状态动力学的变化似乎具有状态选择性、代偿性,并且与中风后脑功能障碍及随后的功能重新配置有关。这些发现为理解微状态的神经机制、揭示中风相关的脑动力学改变以及探索中风患者的新治疗方法提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/452e/9131012/d43dde68c5d0/fnins-16-848737-g001.jpg

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