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Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

机译:功能性脑结合的多时间尺度混合组件:双峰EEG-FMRI分解

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摘要

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FCEEGfMRI
机译:并发脑电图(EEG)和功能磁共振成像(FMRI)跨越时间桥脑连接。在同时EEG-FMRI休息状态记录期间,全脑功能连通性(FC)强度在空间上与模式相关。然而,跨模式调查通常保持持续相关,EEG-FMRI连接的联合分析在很大程度上是未开发的。在这里,我们调查了(空间)独立的FC网络,在模态之间链接。我们将最近提出的混合连接独立分量分析(Connica)框架应用于两个并发EEG-FMRI休息状态数据集(共40个科目)。在两个数据集中都发现了两个强大的组件。第一组件具有均匀分布的EEG频率指纹,主要连接到两种方式中的内在连接网络(ICN)。相反,第二个组件对不同的EEG频率敏感,并且主要与FMRI中的ICN内连接相连,而是在脑电图中连接到ICN间连接。第一混合组件表明,众所周知的ICNS跨度时间尺寸,从FCEEGFMRI的所有规范频率中的毫秒范围内的连接动态

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