Objective . Understanding how brain regions interact to perform a specific task is very challenging. EEG and MEG are two non-invasive imaging modalities that allow the measurement of brain activation with high temporal resolution. Several works in EEG/MEG source reconstruction show that estimating brain activation can be improved by considering spatio-temporal constraints but only few of them use structural information to do so. Approach . In this work, we present a source reconstruction algorithm that uses brain structural connectivity, estimated from diffusion MRI (dMRI), to constrain the EEG/MEG source reconstruction. Contrarily to most source reconstruction methods which reconstruct activation for each time instant, the proposed method estimates an initial reconstruction for the first time instants and a multivariate autoregressive model that explains the data in further time instants. This autoregressive model can be thought as an estimation of the effective co...

中文翻译：

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结构连通性可重建大脑激活和大脑区域之间的有效连通性。

目标。了解大脑区域如何相互作用以执行特定任务非常具有挑战性。EEG和MEG是两种非侵入性成像方式，可以以高时间分辨率测量大脑的活动。EEG / MEG源重构中的一些工作表明，通过考虑时空约束可以改善估计大脑激活的能力，但只有少数人使用结构信息来做到这一点。方法。在这项工作中，我们提出了一种源重建算法，该算法使用从扩散MRI（dMRI）估计的大脑结构连接性来约束EEG / MEG源重建。与大多数源重建方法相反，大多数重建方法都是​​在每个瞬间重建激活，所提出的方法估计了第一时间点的初始重构，并建立了一个多元自回归模型，该模型可以解释更多时间点的数据。这种自回归模型可以被认为是对有效系数的估计。