  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.
