Inference of brain functional connectivity networks from resting-state fMRI data is a key focus in neuroimaging. This paper introduces new Bayesian approaches for inferring a functional connectivity graph from multivariate resting-state fMRI time series of a single subject. Our methods rely on novel Bayesian priors on correlation matrices and a dedicated prior elicitation framework, which translates prior beliefs about the expected level and variability of correlations into interpretable hyperparameter choices, enabling the construction of expert-informed priors. When combined with a Gaussian likelihood, these priors also exhibit computational advantages. Compared to most existing methods for this problem that estimate constant weights, our model provides distributional weights defined by the posterior distributions for the connectivity graph, yielding more robust point estimates through the regularizing effect of expert-informed priors, evaluating uncertainty, and enabling a range of post-inference analyses. In particular, we derive a procedure for identifying significant connectivities based on posterior distributions of weights and credible sets. To the best of our knowledge, only one existing Bayesian functional connectivity model is applicable to single-subject resting-state fMRI data, making our approach a valuable addition to the field and demonstrating superior performance in our experiments.

翻译：基于静息态fMRI数据推断大脑功能连接网络是神经影像学的研究重点。本文提出了从单个受试者的多变量静息态fMRI时间序列中推断功能连接图的新贝叶斯方法。我们的方法依赖于相关矩阵上的新颖贝叶斯先验和专用先验启发框架，该框架将关于相关预期水平与变异性的先验信念转化为可解释的超参数选择，从而能够构建专家知识引导的先验。当与高斯似然结合时，这些先验还具有计算优势。与大多数现有针对该问题估计恒定权重的方法不同，我们的模型通过连接图的后验分布提供分布权重，通过专家知识引导先验的正则化效应产生更稳健的点估计，评估不确定性，并支持一系列推后分析。特别是，我们基于权重的后验分布和可信集推导了识别显著连接性的流程。据我们所知，目前仅有一种现有贝叶斯功能连接模型适用于单个受试者的静息态fMRI数据，这使得我们的方法成为该领域的有益补充，并在实验中展现出更优的性能。