The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
PLoS One. 2011;6(9):e24642. doi: 10.1371/journal.pone.0024642. Epub 2011 Sep 22.
EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.
脑电图 (EEG) 和功能磁共振成像 (fMRI) 记录测量了分布在大脑皮层中的多个相干网络的功能活动。从互补的神经电和血液动力学信号中识别网络相互作用有助于解释不同脑区之间的复杂关系。在本文中，提出了多模态功能网络连接 (mFNC) 用于网络空间中 EEG 和 fMRI 的融合。首先，分别使用空间独立成分分析 (ICA) 在每种模态中提取功能网络 (FN)。然后，通过格兰杰因果分析 (GCA) 探索每种模态中 FN 之间的相互作用。最后，使用基于网络的源成像 (NESOI) 将 fMRI FN 与 EEG FN 在空间域中匹配。对合成数据和真实数据的研究表明，mFNC 具有分别揭示每种模态以及它们组合的潜在神经网络的潜力。通过 mFNC，可以揭示 FN 之间的综合关系，从而深入探索特定任务或神经状态下的神经活动和代谢反应。
