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静息态网络的电生理特征可预测中风后的认知障碍。

Electrophysiological signatures of resting state networks predict cognitive deficits in stroke.

机构信息

IRCCS San Camillo Hospital, Venice, Italy.

IRCCS San Camillo Hospital, Venice, Italy; Laboratory of Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Belgium.

出版信息

Cortex. 2021 May;138:59-71. doi: 10.1016/j.cortex.2021.01.019. Epub 2021 Feb 12.

DOI:10.1016/j.cortex.2021.01.019
PMID:33677328
Abstract

Localized damage to different brain regions can cause specific cognitive deficits. However, stroke lesions can also induce modifications in the functional connectivity of intrinsic brain networks, which could be responsible for the behavioral impairment. Though resting state networks (RSNs) are typically mapped using fMRI, it has been recently shown that they can also be detected from high-density EEG. We build on a state-of-the-art approach to extract RSNs from 64-channels EEG activity in a group of right stroke patients and to identify neural predictors of their cognitive performance. Fourteen RSNs previously found in fMRI and high-density EEG studies on healthy participants were successfully reconstructed from our patients' EEG recordings. We then correlated EEG-RSNs functional connectivity with neuropsychological scores, first considering a wide frequency band (1-80 Hz) and then specific frequency ranges in order to examine the association between each EEG rhythm and the behavioral impairment. We found that visuo-spatial and motor impairments were primarily associated with the dorsal attention network, with contribution dependent on the specific EEG band. These findings are in line with the hypothesis that there is a core system of brain networks involved in specific cognitive domains. Moreover, our results pave the way for low-cost EEG-based monitoring of intrinsic brain networks' functioning in neurological patients to complement clinical-behavioral measures.

摘要

局部脑区损伤会导致特定的认知缺陷。然而,脑卒中病灶也会引起内在脑网络功能连接的改变,这可能是导致行为障碍的原因。尽管静息态网络(RSN)通常使用 fMRI 进行映射,但最近已经表明,它们也可以从高密度 EEG 中检测到。我们在一项最先进的方法的基础上,从一组右侧脑卒中患者的 64 通道 EEG 活动中提取 RSN,并确定其认知表现的神经预测因子。从我们患者的 EEG 记录中成功重建了先前在 fMRI 和高密度 EEG 研究中发现的 14 个 RSN。然后,我们将 EEG-RSN 功能连接与神经心理学评分相关联,首先考虑广泛的频带(1-80 Hz),然后是特定的频带范围,以检查每个 EEG 节律与行为障碍之间的关联。我们发现,视觉空间和运动障碍主要与背侧注意网络有关,其贡献取决于特定的 EEG 频段。这些发现与假设一致,即存在一个核心的脑网络系统参与特定的认知领域。此外,我们的结果为基于 EEG 的内在脑网络功能监测在神经患者中的应用铺平了道路,以补充临床行为测量。

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