Consistency of network modules in resting-state FMRI connectome data.

Consistency of network modules in resting-state FMRI connectome data.
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DOI:
10.1371/journal.pone.0044428
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发表时间:
2012
期刊:
影响因子:
3.7
通讯作者:
Hayasaka S
Hayasaka S
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Moussa MN;Steen MR;Laurienti PJ;Hayasaka S

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在休息时,fMRI测量的自发脑活动由许多不同的静息状态网络(rsn)总结,这些网络遵循相似的时间过程。使用空间ICA(独立成分分析),这种网络已经被一致地识别出来。此外,基于图论的网络分析也被应用于静息状态fMRI数据,识别相似的rsn,尽管通常在较粗的空间分辨率。在这项工作中,我们以体素级分辨率检查了194名受试者的静息状态fMRI网络,并使用一种称为缩放包容性(SI)的度量来检查受试者间rsn的一致性,该度量总结了网络间模块化分区的一致性。我们的SI分析表明,一些rsn在受试者中是稳健的,与ICA识别的相应rsn相当。我们还发现,一些通常报告的rsn在受试者之间不太一致。这是ICAs和基于图的网络分析之间的rsn在可比分辨率上的第一次直接比较。
At rest, spontaneous brain activity measured by fMRI is summarized by a number of distinct resting state networks (RSNs) following similar temporal time courses. Such networks have been consistently identified across subjects using spatial ICA (independent component analysis). Moreover, graph theory-based network analyses have also been applied to resting-state fMRI data, identifying similar RSNs, although typically at a coarser spatial resolution. In this work, we examined resting-state fMRI networks from 194 subjects at a voxel-level resolution, and examined the consistency of RSNs across subjects using a metric called scaled inclusivity (SI), which summarizes consistency of modular partitions across networks. Our SI analyses indicated that some RSNs are robust across subjects, comparable to the corresponding RSNs identified by ICA. We also found that some commonly reported RSNs are less consistent across subjects. This is the first direct comparison of RSNs between ICAs and graph-based network analyses at a comparable resolution.
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