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.
Hum Brain Mapp. 2011 Jul;32(7):1141-60. doi: 10.1002/hbm.21098. Epub 2010 Sep 2.
The brain exhibits temporally coherent networks (TCNs) involving numerous cortical and sub-cortical regions both during the rest state and during the performance of cognitive tasks. TCNs represent the interactions between different brain areas, and understanding such networks may facilitate electroencephalography (EEG) source estimation. We propose a new method for examining TCNs using scalp EEG in conjunction with data obtained by functional magnetic resonance imaging (fMRI). In this approach, termed NEtwork based SOurce Imaging (NESOI), multiple TCNs derived from fMRI with independent component analysis (ICA) are used as the covariance priors of the EEG source reconstruction using Parametric Empirical Bayesian (PEB). In contrast to previous applications of PEB in EEG source imaging with smoothness or sparseness priors, TCNs play a fundamental role among the priors used by NESOI. NESOI achieves an efficient integration of the high temporal resolution EEG and TCN derived from the high spatial resolution fMRI. Using synthetic and real data, we directly compared the performance of NESOI with other distributed source inversion methods, with and without the use of fMRI priors. Our results indicated that NESOI is a potentially useful approach for EEG source imaging.
大脑在静息状态和执行认知任务时都表现出涉及众多皮质和皮质下区域的 temporally coherent networks (TCNs)。TCNs 代表了不同脑区之间的相互作用，理解这些网络可能有助于脑电图 (EEG) 源估计。我们提出了一种使用头皮 EEG 结合功能磁共振成像 (fMRI) 数据检查 TCN 的新方法。在这种称为基于网络的源成像 (NESOI) 的方法中，使用独立成分分析 (ICA) 从 fMRI 中获得的多个 TCN 用作 EEG 源重建的协方差先验，使用参数经验贝叶斯 (PEB)。与以前在 EEG 源成像中使用平滑或稀疏先验的 PEB 应用相比，TCNs 在 NESOI 中使用的先验中起着基本作用。NESOI 实现了高效的 EEG 与源自高空间分辨率 fMRI 的 TCN 的时间分辨率的集成。使用合成和真实数据，我们直接比较了 NESOI 与其他分布式源反演方法的性能，以及是否使用 fMRI 先验。我们的结果表明，NESOI 是一种用于 EEG 源成像的潜在有用方法。
