Published in arXiv (Under submission), 2019
We investigate a constrained Bayesian ICA approach for connectome subnetwork discovery. In comparison to current methods, simultaneously allows (a) the flexible integration of multiple sources of information (fMRI, DTI, 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.
Recommended citation: Claire Donnat, Leonardo Tozzi and Susan Holmes (2019). "Constrained Bayesian ICA for Brain Connectomics 1." arXiv.