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健康对照组和帕金森病患者认知动作控制过程中大脑电生理网络的时空动力学。

Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients.

机构信息

Univ Rennes, LTSI - U1099, F-35000 Rennes, France.

Univ Rennes, LTSI - U1099, F-35000 Rennes, France; Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon.

出版信息

Neuroimage. 2022 Sep;258:119331. doi: 10.1016/j.neuroimage.2022.119331. Epub 2022 Jun 1.

Abstract

Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.

摘要

在与帕金森病(PD)相关的认知症状中,患者常报告认知动作控制(CAC)发生改变。CAC 使我们能够抑制自动行为,转而执行目标导向行为。CAC 的实施是时间分辨的,可以说与功能脑网络的动态变化有关。然而,涉及的电生理功能网络、它们的动态变化以及这些变化如何受到 PD 的影响仍然未知。在这项研究中,为了解决这一知识空白,10 名 PD 患者和 10 名健康对照者(HC)在进行西蒙任务的同时接受高密度脑电图(HD-EEG)记录。使用在β(12-25Hz)和γ(30-45Hz)频带中的锁相值来估计源水平动态连通矩阵。使用时间独立成分分析作为降维工具来分离与任务相关的脑网络状态。量化了典型微状态指标,以研究这些状态在个体水平上的存在。我们的结果首先证实 PD 患者在任务中难以抑制自动反应。在组水平上,我们在β波段发现了三个功能网络状态,涉及额颞叶、颞顶叶和额额连接,具有典型的与 CAC 相关的前额叶和扣带回节点(例如,下额前皮质)。当分析微状态指标时,PD 患者和 HC 之间这些网络的存在没有差异,并且与行为没有发现稳健的相关性。在γ波段,发现了五个网络,包括一个额颞叶网络,与β波段中发现的网络相同。这些网络还包括在不同神经影像学模态中先前确定的与 CAC 相关的节点。与β网络一样,PD 患者和 HC 之间在个体水平上没有发现差异。有趣的是,在两个频带中,个体水平上的主导网络从未是被任务最持久调制的网络。总的来说,这项研究确定了在 CAC 期间观察到的动态功能脑网络,但没有强调这些网络中与 PD 相关的变化,这些变化可能解释了行为变化。尽管可能需要其他新方法来研究个体水平上与任务相关的网络的存在,但这项研究仍强调基于任务的动态功能连通性是理解 PD 及其他认知障碍的有前途的方法。

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