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Sparse Spatio-temporal Inference of Electromagnetic Brain Sources

机译：电磁脑源的时空稀疏推断

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The electromagnetic brain activity measured via MEG (or EEG) can be interpreted as arising from a collection of current dipoles or sources located throughout the cortex. Because the number of candidate locations for these sources is much larger than the number of sensors, source reconstruction involves solving an inverse problem that is severely underdetermined. Bayesian graphical models provide a powerful means of incorporating prior assumptions that narrow the solution space and lead to tractable posterior distributions over the unknown sources given the observed data. In particular, this paper develops a hierarchical, spatio-temporal Bayesian model that accommodates the principled computation of sparse spatial and smooth temporal M/EEG source reconstructions consistent with neurophysiological assumptions in a variety of event-related imaging paradigms. The underlying methodology relies on the notion of automatic relevance determination (ARD) to express the unknown sources via a small collection of spatio-temporal basis functions. Experiments with several data sets provide evidence that the proposed model leads to improved source estimates. The underlying methodology is also well-suited for estimation problems that arise from other brain imaging modalities such as functional or diffusion weighted MRI.
机译：通过MEG（或EEG）测得的电磁脑活动可以解释为是由位于整个皮质的电流偶极子或源的集合引起的。因为这些源的候选位置的数量远大于传感器的数量，所以源重建涉及解决严重不确定的反问题。贝叶斯图形模型提供了一种强大的方法，可以结合先验假设，这些先验假设会缩小求解空间并在给定观测数据的情况下在未知源上导致可预测的后验分布。特别是，本文开发了一种分层的时空贝叶斯模型，该模型可适应与各种事件相关的成像范式中的神经生理学假设相一致的稀疏空间和平滑时间M / EEG源重构的原理性计算。基本方法论依靠自动相关性确定（ARD）的概念，通过少量时空基础函数集合来表达未知来源。使用多个数据集进行的实验提供了证据，表明所提出的模型可以改善源估计。基本的方法学也非常适合因其他大脑成像模式（例如功能或扩散加权MRI）而引起的估计问题。

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