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静息态网络分析揭示原发性震颤患者大脑功能连接的改变。

Resting-State Network Analysis Reveals Altered Functional Brain Connectivity in Essential Tremor.

机构信息

Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan.

Department of Neurology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi, Taiwan.

出版信息

Brain Connect. 2024 Sep;14(7):382-390. doi: 10.1089/brain.2024.0004. Epub 2024 Jul 25.

Abstract

Essential tremor (ET) comprises motor and non-motor-related features, whereas the current neuro-pathogenetic basis is still insufficient to explain the etiologies of ET. Although cerebellum-associated circuits have been discovered, the large-scale cerebral network connectivity in ET remains unclear. This study aimed to characterize the ET in terms of functional connectivity as well as network. We hypothesized that the resting-state network (RSN) within cerebrum could be altered in patients with ET. Resting-state functional magnetic resonance imaging (fMRI) was used to evaluate the inter- and intra-network connectivity as well as the functional activity in ET and normal control. Correlation analysis was performed to explore the relationship between RSN metrics and tremor features. Comparison of inter-network connectivity indicated a decreased connectivity between default mode network and ventral attention network in the ET group ( < 0.05). Differences in functional activity (assessed by amplitude of low-frequency fluctuation, ALFF) were found in several brain regions participating in various RSNs ( < 0.05). The ET group generally has higher degree centrality over normal control. Correlation analysis has revealed that tremor features are associated with inter-network connectivity (|r| = 0.135-0.506), ALFF (|r| = 0.313-0.766), and degree centrality (|r| = 0.523-0.710). Alterations in the cerebral network of ET were detected by using resting-state fMRI, demonstrating a potentially useful approach to explore the cerebral alterations in ET.

摘要

特发性震颤(ET)包括运动和非运动相关的特征,而目前的神经发病机制基础仍不足以解释 ET 的病因。虽然已经发现了与小脑相关的回路,但 ET 中的大规模大脑网络连接仍不清楚。本研究旨在从功能连接和网络的角度来描述 ET。我们假设 ET 患者的大脑静息状态网络(RSN)可能会发生改变。使用静息态功能磁共振成像(fMRI)来评估 ET 和正常对照组的网络间和网络内连接以及功能活动。进行相关性分析以探讨 RSN 指标与震颤特征之间的关系。网络间连接的比较表明 ET 组默认模式网络和腹侧注意网络之间的连接减少(<0.05)。参与各种 RSN 的几个脑区的功能活动(通过低频波动幅度评估,ALFF)存在差异(<0.05)。ET 组的节点度普遍高于正常对照组。相关性分析表明,震颤特征与网络间连接(|r|=0.135-0.506)、ALFF(|r|=0.313-0.766)和节点度(|r|=0.523-0.710)相关。使用静息态 fMRI 检测到 ET 的大脑网络改变,这表明这是一种探索 ET 大脑改变的潜在有用方法。

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