Objective . To infer information flow in the white matter of the brain and recover cortical activity using functional MRI, diffusion MRI, and MEG without a manual selection of the white matter connections of interest. Approach . A Bayesian network which encodes the priors knowledge of possible brain states is built from imaging data. Diffusion MRI is used to enumerate all possible connections between cortical regions. Functional MRI is used to prune connections without manual intervention and increase the likelihood of specific regions being active. MEG data is used as evidence into this network to obtain a posterior distribution on cortical regions and connections. Main results . We show that our proposed method is able to identify connections associated with the a sensory–motor task. This allows us to build the Bayesian network with no manual selection of connections of interest. Using sensory–motor MEG evoked response as evidence into this network, our meth...

中文翻译：

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使用扩散MRI，功能性MRI和MEG进行信息流的非侵入式推理。

目标。要推断大脑白质中的信息流并使用功能性MRI，扩散MRI和MEG恢复皮质活动，而无需手动选择感兴趣的白质连接。方法。从成像数据中建立了一个贝叶斯网络，该网络对可能的大脑状态的先验知识进行编码。扩散MRI用于枚举皮质区域之间的所有可能连接。功能性MRI用于修剪连接而无需人工干预，并增加了特定区域活跃的可能性。MEG数据用作进入该网络的证据，以获取皮质区域和连接的后验分布。主要结果。我们表明，我们提出的方法能够识别与感觉运动任务相关的连接。这使我们无需手动选择感兴趣的连接即可构建贝叶斯网络。使用感觉运动MEG引起的反应作为该网络的证据，我们的方法...