Prior elicitation for Bayesian estimation of single-subject connectivity networks

TL;DR

This paper presents novel Bayesian methods with expert-informed priors for inferring single-subject brain connectivity networks from resting-state fMRI data, offering improved robustness and uncertainty quantification.

Contribution

Introduces new Bayesian priors and a prior elicitation framework for single-subject connectivity inference, enhancing interpretability and computational efficiency.

Findings

Our model provides distributional weights with credible intervals.

The approach outperforms existing methods in experiments.

It enables identification of significant connectivities based on posterior distributions.

Abstract

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.