Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan; ATR Neural Information Analysis Laboratories, Kyoto, Japan.
ATR Neural Information Analysis Laboratories, Kyoto, Japan; Brain Functional Imaging Technologies Group, CiNet, Osaka, Japan.
Neuroimage. 2015 Jan 15;105:408-27. doi: 10.1016/j.neuroimage.2014.09.066. Epub 2014 Oct 5.
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.
我们提出了一种 MEG 源重建方法，该方法可以同时重建源幅度并识别整个大脑中的源相互作用。在提出的方法中，全变量自回归 (MAR) 模型构建了源之间的有向相互作用（即有效连通性）。MAR 系数（MAR 矩阵的条目）受从扩散 MRI 推断出的整个大脑解剖网络的先验知识约束。此外，为了提高我们方法的准确性和稳健性，我们在空间活动模式上应用 fMRI 先验，并在 MAR 系数上应用稀疏先验。MEG 数据的观测过程、源动力学以及一系列先验条件被组合到一个贝叶斯框架中，使用状态空间表示。使用变分贝叶斯学习算法从参数（例如源幅度和 MAR 系数）进行联合估计。通过在 MEG 源重建的背景下构建源动力学，并统一源幅度和相互作用的估计，我们可以在无需选择感兴趣区域的情况下识别有效连通性。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估。与非动态方法相比，在该方法中，在没有动态约束的情况下在源重建后估计相互作用，所提出的动态方法在模拟中提高了大多数性能指标，并在实际数据应用中提供了更好的生理学解释和受试者间一致性。
