MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks
MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks
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DOI：
10.1016/j.neuroimage.2014.09.066
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发表时间：
2015-01-15
期刊：
影响因子：
5.7
通讯作者：
Sato, Masa-aki
中科院分区：
文献类型：
--
作者：
Fukushima, Makoto;Yamashita, Okito;Sato, Masa-aki
我们提出了一种MEG源重建方法，可以同时重建源振幅并识别整个大脑的源相互作用。在提出的方法中，一个完整的多元自回归（MAR）模型描述了源之间的直接相互作用（即有效连接）。磁共振成像系数（磁共振成像矩阵的条目）受到从扩散MRI推断的全脑解剖网络的先验知识的约束。此外，为了提高方法的准确性和鲁棒性，我们对空间活动模式应用了fMRI先验，对MAR系数应用了稀疏先验。利用状态空间表示将MEG数据的观测过程、源动态和一系列先验组合到贝叶斯框架中。通过变分贝叶斯学习算法对源振幅和MAR系数等参数进行联合估计。通过在脑磁图源重建中建立源动态，统一源振幅和相互作用的估计，我们可以在不需要选择感兴趣区域的情况下识别有效的连通性。我们的方法分别在模拟和实验数据上进行了定量和定性评价。与非动态方法相比，非动态方法在没有动态约束的情况下对源重构后的相互作用进行估计，所提出的动态方法改善了仿真中的大部分性能指标，并在实际数据应用中提供了更好的生理解释和主体间一致性。(C) 2014年作者。Elsevier Inc.出版。
We present an MEG source reconstruction method that simultaneously reconstructs source amplitudes and identifies source interactions across the whole brain. In the proposed method, a full multivariate autoregressive (MAR) model formulates directed interactions (i.e., effective connectivity) between sources. The MAR coefficients (the entries of the MAR matrix) are constrained by the prior knowledge of whole-brain anatomical networks inferred from diffusion MRI. Moreover, to increase the accuracy and robustness of our method, we apply an fMRI prior on the spatial activity patterns and a sparse prior on the MAR coefficients. The observation process of MEG data, the source dynamics, and a series of the priors are combined into a Bayesian framework using a state-space representation. The parameters, such as the source amplitudes and the MAR coefficients, are jointly estimated from a variational Bayesian learning algorithm. By formulating the source dynamics in the context of MEG source reconstruction, and unifying the estimations of source amplitudes and interactions, we can identify the effective connectivity without requiring the selection of regions of interest. Our method is quantitatively and qualitatively evaluated on simulated and experimental data, respectively. Compared with non-dynamic methods, in which the interactions are estimated after source reconstruction with no dynamic constraints, the proposed dynamic method improves most of the performance measures in simulations, and provides better physiological interpretation and inter-subject consistency in real data applications. (C) 2014 The Authors. Published by Elsevier Inc.