Brain activation and connectivity analyses in task-based functional magnetic resonance imaging (fMRI) experiments with multiple subjects are currently at the forefront of data-driven neuroscience. In such experiments, interest often lies in understanding activation of brain voxels due to external stimuli and strong association or connectivity between the measurements on a set of pre-specified groups of brain voxels, also known as regions of interest (ROI). This article proposes a joint Bayesian additive mixed modeling framework that simultaneously assesses brain activation and connectivity patterns from multiple subjects. In particular, fMRI measurements from each individual obtained in the form of a multi-dimensional array/tensor at each time are regressed on functions of the stimuli. We impose a low-rank parallel factorization decomposition on the tensor regression coefficients corresponding to the stimuli to achieve parsimony. Multiway stick-breaking shrinkage priors are employed to infer activation patterns and associated uncertainties in each voxel. Further, the model introduces region-specific random effects which are jointly modeled with a Bayesian Gaussian graphical prior to account for the connectivity among pairs of ROIs. Empirical investigations under various simulation studies demonstrate the effectiveness of the method as a tool to simultaneously assess brain activation and connectivity. The method is then applied to a multi-subject fMRI dataset from a balloon-analog risk-taking experiment, showing the effectiveness of the model in providing interpretable joint inference on voxel-level activations and inter-regional connectivity associated with how the brain processes risk. The proposed method is also validated through simulation studies and comparisons to other methods used within the neuroscience community.

中文翻译：

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fMRI 实验中体素激活和区域间连通性的联合贝叶斯估计

在基于任务的功能磁共振成像 (fMRI) 实验中，对多个受试者进行的大脑激活和连接分析目前处于数据驱动神经科学的前沿。在此类实验中，兴趣通常在于了解由于外部刺激引起的脑体素激活以及一组预先指定的脑体素组（也称为感兴趣区域 (ROI)）的测量值之间的强关联或连通性。本文提出了一个联合贝叶斯加法混合建模框架，可同时评估多个受试者的大脑激活和连接模式。特别是，每次以多维阵列/张量的形式获得的每个人的 fMRI 测量结果都对刺激的函数进行了回归。我们对与刺激相对应的张量回归系数进行低秩并行分解分解，以实现简约。采用多路破坏收缩先验来推断每个体素中的激活模式和相关的不确定性。此外，该模型引入了特定于区域的随机效应，在考虑 ROI 对之间的连通性之前，这些随机效应与贝叶斯高斯图联合建模。各种模拟研究下的实证研究证明了该方法作为同时评估大脑激活和连通性的工具的有效性。然后将该方法应用于来自气球模拟冒险实验的多主题 fMRI 数据集，展示了该模型在提供关于体素级激活和与大脑如何处理风险相关的区域间连通性的可解释联合推理方面的有效性。所提出的方法还通过模拟研究和与神经科学界使用的其他方法的比较得到验证。