1 The Mind Research Network, Albuquerque, New Mexico.
2 Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico.
Brain Connect. 2019 Apr;9(3):251-262. doi: 10.1089/brain.2018.0608.
The importance of how brain networks function together to create brain states has become increasingly recognized. Therefore, an investigation of eyes-open resting-state dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) via both functional magnetic resonance imaging (fMRI) and a novel magnetoencephalography (MEG) pipeline was completed. The fMRI analysis used a spatial independent component analysis (ICA) to determine the networks on which the dFNC was based. The MEG analysis utilized a source space activity estimate (minimum norm estimate [MNE]/dynamic statistical parametric mapping [dSPM]) whose result was the input to a spatial ICA, on which the networks of the MEG dFNC were based. We found that dFNC measures reveal significant differences between HC and SP, which depended on the imaging modality. Consistent with previous findings, a dFNC analysis predicated on fMRI data revealed HC and SP remain in different overall brain states (defined by a k-means clustering of network correlations) for significantly different periods of time, with SP spending less time in a highly connected state. The MEG dFNC, in contrast, revealed group differences in more global statistics: SP changed between meta-states (k-means cluster states that are allowed to overlap in time) significantly more often and to states that were more different, relative to HC. MEG dFNC also revealed a highly connected state where a significant difference was observed in interindividual variability, with greater variability among SP. Overall, our results show that fMRI and MEG reveal between-group functional connectivity differences in distinct ways, highlighting the utility of using each of the modalities individually, or potentially a combination of modalities, to better inform our understanding of disorders such as schizophrenia.
大脑网络如何协同工作以产生脑状态的重要性已逐渐得到认可。因此，我们通过功能磁共振成像（fMRI）和新型脑磁图（MEG）管道，完成了对健康对照者（HC）和精神分裂症患者（SP）睁眼静息态动态功能网络连接（dFNC）的研究。fMRI 分析采用空间独立成分分析（ICA）确定 dFNC 所基于的网络。MEG 分析利用源空间活动估计（最小范数估计[MNE]/动态统计参数映射[dSPM]），其结果是空间 ICA 的输入，MEG dFNC 的网络基于该空间 ICA。我们发现，dFNC 测量值揭示了 HC 和 SP 之间的显著差异，这些差异取决于成像方式。与先前的发现一致，基于 fMRI 数据的 dFNC 分析表明，HC 和 SP 在不同的整体脑状态（由网络相关性的 k-均值聚类定义）中停留的时间显著不同，SP 在高度连接状态下停留的时间更少。相比之下，MEG dFNC 揭示了更全局的统计差异：SP 在元状态（允许时间重叠的 k-均值聚类状态）之间的转换更为频繁，并且与 HC 相比，转换到更为不同的状态。MEG dFNC 还揭示了一个高度连接的状态，在这个状态中，个体间的变异性存在显著差异，SP 之间的变异性更大。总的来说，我们的结果表明，fMRI 和 MEG 以不同的方式揭示了组间功能连接的差异，突出了单独使用每种模态或可能组合使用多种模态的效用，以更好地了解精神分裂症等疾病。
