Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments
Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments
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DOI：
10.1007/s11336-020-09727-0
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发表时间：
2020-09
期刊：
影响因子：
3
通讯作者：
Daniel Spencer;Rajarshi Guhaniyogi;R. Prado
中科院分区：
文献类型：
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作者：
Daniel Spencer;Rajarshi Guhaniyogi;R. Prado
在基于任务的功能性磁共振成像（fMRI）实验中对多个受试者的大脑激活和连接性分析目前处于数据驱动神经科学的最前沿。在这样的实验中，兴趣通常在于理解由于外部刺激引起的脑体素的激活以及对一组预先指定的脑体素组（也称为感兴趣区域（ROI））的测量之间的强关联或连接。本文提出了一个联合贝叶斯添加剂混合建模框架，同时评估大脑激活和连接模式，从多个主题。特别是，fMRI测量从每个人获得的多维阵列/张量的形式在每个时间回归的功能的刺激。我们对与刺激对应的张量回归系数进行低秩并行因式分解，以实现简约性。采用多路棒断裂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，该模型引入了特定区域的随机效应，这些随机效应在考虑成对ROI之间的连接性之前与贝叶斯高斯图形联合建模。各种模拟研究下的实证调查表明，该方法作为一种工具，同时评估大脑激活和连接的有效性。然后，该方法被应用到一个多学科的功能磁共振成像数据集从气球模拟冒险实验，显示模型的有效性，在提供可解释的联合推理体素水平的激活和区域间的连接与大脑如何处理风险。所提出的方法也验证了通过模拟研究和神经科学界内使用的其他方法的比较。
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.