A graph-based brain parcellation method extracting sparse networks

FMRI is a powerful tool for assessing the functioning of the brain. The analysis of resting-state fMRI allows to describe the functional relationship between the cortical areas. Since most connectivity analysis methods suffer from the curse of dimensionality, the cortex needs to be first partitioned into regions of coherent activation patterns. Once the signals of these regions of interest have been extracted, estimating a sparse approximation of the inverse of their correlation matrix is a classical way to robustly describe their functional interactions. In this paper, we address both objectives with a novel parcellation method based on Markov Random Fields that favors the extraction of sparse networks of regions. Our method relies on state of the art rsfMRI models, naturally adapts the number of parcels to the data and is guaranteed to provide connected regions due to the use of shape priors. The second contribution of this paper resides in two novel sparsity enforcing potentials. Our approach is validated with a publicly available dataset.