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
中科院分区:
文献类型:
--
作者:
Moussa MN;Steen MR;Laurienti PJ;Hayasaka S
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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DOI:
10.1073/pnas.0504136102
发表时间:
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影响因子:
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通讯作者:
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