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Distinct Patterns of Functional Connectivity During the Comprehension of Natural, Narrative Speech
International Journal of Neural Systems ( IF 8 ) Pub Date : 2019-12-16 , DOI: 10.1142/s0129065720500070
Yongjie Zhu 1, 2 , Jia Liu 2 , Tapani Ristaniemi 2 , Fengyu Cong 1, 2
Affiliation  

Recent continuous task studies, such as narrative speech comprehension, show that fluctuations in brain functional connectivity (FC) are altered and enhanced compared to the resting state. Here, we characterized the fluctuations in FC during comprehension of speech and time-reversed speech conditions. The correlations of Hilbert envelope of source-level EEG data were used to quantify FC between spatially separate brain regions. A symmetric multivariate leakage correction was applied to address the signal leakage issue before calculating FC. The dynamic FC was estimated based on a sliding time window. Then, principal component analysis (PCA) was performed on individually concatenated and temporally concatenated FC matrices to identify FC patterns. We observed that the mode of FC induced by speech comprehension can be characterized with a single principal component. The condition-specific FC demonstrated decreased correlations between frontal and parietal brain regions and increased correlations between frontal and temporal brain regions. The fluctuations of the condition-specific FC characterized by a shorter time demonstrated that dynamic FC also exhibited condition specificity over time. The FC is dynamically reorganized and FC dynamic pattern varies along a single mode of variation during speech comprehension. The proposed analysis framework seems valuable for studying the reorganization of brain networks during continuous task experiments.

中文翻译:

在理解自然、叙述性语音过程中功能连接的不同模式

最近的连续任务研究,例如叙述性语音理解,表明与静止状态相比,大脑功能连接 (FC) 的波动发生了改变和增强。在这里,我们描述了在理解语音和时间反转语音条件期间 FC 的波动。源级脑电图数据的希尔伯特包络的相关性用于量化空间分离的大脑区域之间的 FC。在计算 FC 之前,应用对称多元泄漏校正来解决信号泄漏问题。基于滑动时间窗口估计动态FC。然后,对单独连接和时间连接的 FC 矩阵进行主成分分析 (PCA) 以识别 FC 模式。我们观察到由语音理解引起的 FC 模式可以用单个主成分来表征。特定条件的 FC 表明额叶和顶叶脑区之间的相关性降低,而额叶和颞脑区之间的相关性增加。以较短时间为特征的特定条件 FC 的波动表明,动态 FC 随着时间的推移也表现出条件特异性。FC 是动态重组的,并且 FC 动态模式在语音理解过程中沿着单一的变化模式变化。所提出的分析框架对于在连续任务实验中研究大脑网络的重组似乎很有价值。特定条件的 FC 表明额叶和顶叶脑区之间的相关性降低,而额叶和颞脑区之间的相关性增加。以较短时间为特征的特定条件 FC 的波动表明,动态 FC 随着时间的推移也表现出条件特异性。FC 是动态重组的,并且 FC 动态模式在语音理解过程中沿着单一的变化模式变化。所提出的分析框架对于在连续任务实验中研究大脑网络的重组似乎很有价值。特定条件的 FC 表明额叶和顶叶脑区之间的相关性降低,而额叶和颞脑区之间的相关性增加。以较短时间为特征的特定条件 FC 的波动表明,动态 FC 随着时间的推移也表现出条件特异性。FC 是动态重组的,并且 FC 动态模式在语音理解过程中沿着单一的变化模式变化。所提出的分析框架对于在连续任务实验中研究大脑网络的重组似乎很有价值。FC 是动态重组的,并且 FC 动态模式在语音理解过程中沿着单一的变化模式变化。所提出的分析框架对于在连续任务实验中研究大脑网络的重组似乎很有价值。FC 是动态重组的,并且 FC 动态模式在语音理解过程中沿着单一的变化模式变化。所提出的分析框架对于在连续任务实验中研究大脑网络的重组似乎很有价值。
更新日期:2019-12-16
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