发表机构
The University of Texas at El Paso（得克萨斯大学埃尔帕索分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出结合Student-t似然与非局部pMOM先验的稳健稀疏组DCM方法，通过EM-ReML算法估计，在模拟和fMRI应用中验证了其抗污染与稀疏选择优势。
AI 中文摘要
动态因果建模（DCM）估计有向有效连接，而参数经验贝叶斯（PEB）利用受试者特定的后验汇总支持组推断。标准PEB依赖于高斯模型和连续收缩，使其对非典型估计敏感，且无法区分可忽略效应与非零效应。我们开发了一种稳健且稀疏的组DCM扩展，将Student-t似然与使用非局部乘积矩（pMOM）板的尖峰-板先验相结合。Student-t分布的正态-伽马表示产生权重，用于降低非典型受试者-参数组合的权重。pMOM板在零处消失，锐化系数选择并产生包含概率。我们通过块协方差预白化传播第一级后验不确定性，并使用EM-ReML算法估计模型，该算法更新权重、包含概率、效应和方差分量。一项模拟研究表明，Student-t加权提供了针对污染的主要保护，非局部先验在强稀疏性下贡献最大，而当污染和稀疏性同时发生时，它们的组合最为有益。在一项公开共享的混合赌博fMRI应用中，我们发现了轻度异质性，包含支持集中在三个内在自连接上，并与标准SPM-PEB的方向一致性密切。因此，我们的框架为分层DCM增加了可解释的元素级稳健性诊断和稀疏系数选择，同时保留第一级不确定性。
英文摘要
Dynamic causal modeling (DCM) estimates directed effective connectivity, while parametric empirical Bayes (PEB) supports group inference using subject-specific posterior summaries. Standard PEB relies on Gaussian models and continuous shrinkage, making it sensitive to atypical estimates and unable to distinguish negligible from nonzero effects. We develop a robust and sparse group-DCM extension combining a Student-t likelihood with a spike-and-slab prior using a nonlocal product-moment (pMOM) slab. A normal--gamma representation of the Student-t distribution yields weights that downweight atypical subject--parameter combinations. The pMOM slab vanishes at zero, sharpening coefficient selection and yielding inclusion probabilities. We propagate first-level posterior uncertainty through block-covariance pre-whitening and estimate the model using an EM--ReML algorithm that updates weights, inclusion probabilities, effects, and variance components. A simulation study showed that Student-t weighting provided the main protection against contamination, the nonlocal prior contributed most under strong sparsity, and their combination was most beneficial when contamination and sparsity occurred together. In an openly shared mixed-gambles fMRI application, we found mild heterogeneity, concentrated inclusion support on three intrinsic self-connections, and close directional agreement with standard SPM-PEB. Our framework therefore adds interpretable element-level robustness diagnostics and sparse coefficient selection to hierarchical DCM while retaining first-level uncertainty.
