Constrained Bayesian ICA for Brain Connectome Inference

TL;DR

This paper introduces a constrained Bayesian ICA method for brain connectome inference that integrates multiple data sources, automatically selects model parameters, and provides uncertainty estimates, validated on synthetic and real data.

Contribution

It presents a novel Bayesian ICA approach that incorporates anatomical information, automates model selection, and quantifies uncertainty in brain network analysis.

Findings

Effective integration of multiple data sources improves connectome inference.

Automatic sparsity and module number selection enhances model robustness.

Uncertainty estimates aid in interpreting brain interactions.

Abstract

Brain connectomics is a developing field in neurosciences which strives to understand cognitive processes and psychiatric diseases through the analysis of interactions between brain regions. However, in the high-dimensional, low-sample, and noisy regimes that typically characterize fMRI data, the recovery of such interactions remains an ongoing challenge: how can we discover patterns of co-activity between brain regions that could then be associated to cognitive processes or psychiatric disorders? In this paper, we investigate a constrained Bayesian ICA approach which, in comparison to current methods, simultaneously allows (a) the flexible integration of multiple sources of information (fMRI, DWI, anatomical, etc.), (b) an automatic and parameter-free selection of the appropriate sparsity level and number of connected submodules and (c) the provision of estimates on the uncertainty of the recovered interactions. Our experiments, both on synthetic and real-life data, validate the flexibility of our method and highlight the benefits of integrating anatomical information for connectome inference.