Robust and Sparse Group Dynamic Causal Modeling via Student-t Parametric Empirical Bayes and Nonlocal Priors

2026-09-06

AI Summary

该研究通过结合Student-t分布和非局部先验方法改进了动态因果模型（DCM）的鲁棒性和稀疏性，以解决标准参数经验贝叶斯方法对异常估计敏感及无法区分可忽略与非零效应的问题。

Abstract

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.

Problem

• Dynamic Causal Modeling
• Parametric Empirical Bayes
• Robustness
• Sparsity
• Group Inference

Innovation

• Student-t likelihood
• nonlocal prior
• spike-and-slab
• pMOM slab
• EM-ReML algorithm