fMRI-SI-STBF: An fMRI-informed Bayesian electromagnetic spatio-temporal extended source imaging
fMRI-SI-STBF: An fMRI-informed Bayesian electromagnetic spatio-temporal extended source imaging
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fMRI-SI-STBF：基于 fMRI 的贝叶斯电磁时空扩展源成像
DOI：
10.1016/j.neucom.2021.06.066
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发表时间：
2021-10
期刊：
影响因子：
6
通讯作者：
Guan Cuntai
中科院分区：
文献类型：
--
作者：
Liu Ke;Yu Zhu Liang;Wu Wei;Chen Xun;Gu Zhenghui;Guan Cuntai
功能磁共振成像（fMRI）和脑电图（EEG）相结合的多模态功能神经成像技术，具有高时空分辨率的脑功能恢复能力，对神经科学研究和临床诊断具有重要意义。然而，fMRI和EEG活动之间的定位不对准可能会降低fMRI约束EEG源成像（ESI）技术的准确性。为了充分利用fMRI和EEG的互补时空分辨率，在数据驱动的方式，我们提出了一种非对称的方法EEG/fMRI融合，称为fMRI知情源成像的基础上时空基函数（fMRI-SI-STBF）。fMRI-SI-STBF采用从fMRI和EEG信号定义的聚类中导出的协方差分量（CC）作为经验贝叶斯框架内的空间先验。此外，fMRI-SI-STBF通过矩阵分解将当前源矩阵表示为若干未知时间基函数（TBF）的线性组合。每个fMRI通知和EEG通知的CC的相对贡献，以及TBF的数量和概况，都是使用变分贝叶斯推理基于EEG数据自动确定的。我们的研究结果表明，fMRI-SI-STBF可以有效地利用有效的fMRI信息ESI和无效的fMRI先验是强大的。这种鲁棒性对于实际ESI是必不可少的，因为考虑到fMRI是神经活动的间接测量，fMRI先验的有效性通常不清楚。此外，与仅使用空间约束的方法相比，fMRI-SI-STBF可以通过结合时间约束来实现性能改善。在数值模拟方面，fMRI-SI-STBF比现有的EEG-fMRI ESI方法更准确地重建了源的范围、位置和时间过程（即，fwMNE、fMRI-SI-SBF）和无fMRI先验的ESI方法（即，wMNE、LORETA、SBL、SI-STBF、SI-SBF），由较小的空间色散表示（平均SD< 5 mm）、定位误差距离（平均DLE< 2 mm）、形状误差（平均SE< 0.9）和较大的模型证据值。
Multimodal functional neuroimaging by integrating functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has the promise of recovering brain activities with high spatiotemporal resolution, which is crucial for neuroscience research and clinical diagnosis. However, the misalignment of the localizations between fMRI and EEG activities may degrade the accuracy of the fMRI-constrained EEG source imaging (ESI) technique. To leverage the complementary spatiotemporal resolution of fMRI and EEG in a data-driven fashion, we propose an asymmetric approach for EEG/fMRI fusion, termed fMRI-informed source imaging based on spatiotemporal basis functions (fMRI-SI-STBF). fMRI-SI-STBF employs the covariance components (CCs) derived from clusters defined by fMRI and EEG signals as spatial priors within the empirical Bayesian framework. Additionally, fMRI-SI-STBF represents the current source matrix as a linear combination of several unknown temporal basis functions (TBFs) by matrix decomposition. The relative contribution of each of the fMRI-informed and EEG-informed CCs, as well as the number and profiles of the TBFs, are all automatically determined based on the EEG data using variational Bayesian inference. Our results demonstrate that fMRI-SI-STBF can effectively utilize valid fMRI information for ESI and is robust to invalid fMRI priors. This robustness is essential for practical ESI since the validity of fMRI priors is often unclear considering that fMRI is an indirect measure of neural activity. Moreover, fMRI-SI-STBF can achieve performance improvement by incorporating temporal constraints compared to methods that use spatial constraints only. For the numerical simulations, fMRI-SI-STBF reconstructs the source extents, locations and time courses more accurately than existing EEG-fMRI ESI methods (ie, fwMNE, fMRI-SI-SBF) and ESI methods without fMRI priors (ie, wMNE, LORETA, SBL, SI-STBF, SI-SBF), indicated by the smaller spatial dispersion (average SD< 5 mm), distance of localization error (average DLE< 2 mm), shape error (average SE< 0.9) and larger model evidence values.
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