Liu Zhongming, de Zwart Jacco A, Chang Catie, Duan Qi, van Gelderen Peter, Duyn Jeff H
Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
Cereb Cortex. 2014 Nov;24(11):3080-9. doi: 10.1093/cercor/bht164. Epub 2013 Jun 24.
Spontaneous activity in the human brain occurs in complex spatiotemporal patterns that may reflect functionally specialized neural networks. Here, we propose a subspace analysis method to elucidate large-scale networks by the joint analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. The new approach is based on the notion that the neuroelectrical activity underlying the fMRI signal may have EEG spectral features that report on regional neuronal dynamics and interregional interactions. Applying this approach to resting healthy adults, we indeed found characteristic spectral signatures in the EEG correlates of spontaneous fMRI signals at individual brain regions as well as the temporal synchronization among widely distributed regions. These spectral signatures not only allowed us to parcel the brain into clusters that resembled the brain's established functional subdivision, but also offered important clues for disentangling the involvement of individual regions in fMRI network activity.
人类大脑中的自发活动以复杂的时空模式出现，这些模式可能反映了功能特化的神经网络。在此，我们提出一种子空间分析方法，通过联合分析脑电图（EEG）和功能磁共振成像（fMRI）数据来阐明大规模网络。新方法基于这样一种观念，即fMRI信号背后的神经电活动可能具有脑电图频谱特征，这些特征反映了区域神经元动力学和区域间相互作用。将这种方法应用于静息状态下的健康成年人，我们确实在个体脑区的自发fMRI信号的EEG相关性以及广泛分布区域之间的时间同步中发现了特征性频谱特征。这些频谱特征不仅使我们能够将大脑划分为类似于大脑已确立的功能分区的簇，还为厘清各个区域在fMRI网络活动中的参与情况提供了重要线索。
