Brain function is organized in coordinated modes of spatio-temporal activity (functional networks) exhibiting an intrinsic baseline structure with variations under different experimental conditions. Existing approaches for uncovering such network structures typically do not explicitly model shared and differential patterns across networks, thus potentially reducing the detection power. We develop an integrative modeling approach for jointly modeling multiple brain networks across experimental conditions. The proposed Bayesian Joint Network Learning approach develops flexible priors on the edge probabilities involving a common intrinsic baseline structure and differential effects specific to individual networks. Conditional on these edge probabilities, connection strengths are modeled under a Bayesian spike and slab prior on the off-diagonal elements of the inverse covariance matrix. The model is fit under a posterior computation scheme based on Markov chain Monte Carlo. Numerical simulations illustrate that the proposed joint modeling approach has increased power to detect true differential edges while providing adequate control on false positives and achieving greater accuracy in the estimation of edge strengths compared to existing methods. An application of the method to fMRI Stroop task data provides unique insights into brain network alterations between cognitive conditions which existing graphical modeling techniques failed to reveal.

中文翻译：

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多脑功能网络的贝叶斯联合建模


大脑功能以时空活动（功能网络）的协调模式进行组织，表现出内在的基线结构，在不同的实验条件下会发生变化。用于揭示此类网络结构的现有方法通常不会显式地建模跨网络的共享和差异模式，因此可能会降低检测能力。我们开发了一种综合建模方法，用于在实验条件下联合建模多个大脑网络。所提出的贝叶斯联合网络学习方法在边缘概率上开发灵活的先验，涉及共同的内在基线结构和特定于各个网络的差异效应。以这些边缘概率为条件，在逆协方差矩阵的非对角线元素上的贝叶斯峰值和平板先验下对连接强度进行建模。该模型在基于马尔可夫链蒙特卡罗的后验计算方案下进行拟合。数值模拟表明，与现有方法相比，所提出的联合建模方法增强了检测真实差分边缘的能力，同时对误报提供了充分的控制，并在边缘强度估计方面实现了更高的准确性。该方法应用于 fMRI Stroop 任务数据，为认知条件之间的大脑网络变化提供了独特的见解，而现有的图形建模技术无法揭示这一点。