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.
Brain Topogr. 2012 Jan;25(1):27-38. doi: 10.1007/s10548-011-0187-9. Epub 2011 May 6.
Brain functional networks extracted from fMRI can improve the accuracy of EEG source localization. However, the coupling between EEG and fMRI remains poorly understood, i.e., whether fMRI networks provide information about the magnitude of neural activity, and whether neural sources demonstrate temporal correlations within each network. In this paper, we present an improved version of the NEtwork-based SOurce Imaging method (iNESOI) through Bayesian model comparison. Different models correspond to various matching between EEG and fMRI, and the appropriate one is selected by data with the model evidence. Synthetic and real data tests show that iNESOI has potential to select the appropriate fMRI priors to reach a better source reconstruction than some other typical approaches.
从 fMRI 中提取的大脑功能网络可以提高 EEG 源定位的准确性。然而，EEG 和 fMRI 之间的耦合仍然理解得很差，即 fMRI 网络是否提供有关神经活动幅度的信息，以及神经源是否在每个网络内表现出时间相关性。在本文中，我们通过贝叶斯模型比较展示了基于网络的源成像方法（iNESOI）的改进版本。不同的模型对应于 EEG 和 fMRI 之间的各种匹配，通过具有模型证据的数据选择合适的模型。合成和真实数据测试表明，iNESOI 有潜力选择适当的 fMRI 先验来实现比其他一些典型方法更好的源重建。
