Lei Xu, Valdes-Sosa Pedro A, Yao Dezhong
Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, 400715, PR China.
J Integr Neurosci. 2012 Sep;11(3):313-37. doi: 10.1142/S0219635212500203. Epub 2012 Sep 17.
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.
同步脑电图（EEG）和功能磁共振成像（fMRI）可提供关于大脑活动的互补性非侵入性信息，并且EEG/fMRI融合能够实现比单独使用每种模态更高的时空分辨率。本文聚焦于基于独立成分分析（ICA）的EEG/fMRI融合。为了理解相关问题，我们首先描述已开发的融合方法的潜力和局限性：fMRI约束EEG成像、EEG引导fMRI分析以及对称融合。然后，我们概述一些新开发的使用ICA以及数据驱动与模型驱动方法相结合的混合融合技术，特别提及了时空EEG/fMRI融合（STEFF）。最后，我们讨论方法学发展的当前趋势以及推断神经动力学时存在的局限性。
