Hybrid multivariate morphology using lattice auto-associative memories for resting-state fMRI network discovery

Analysis of fMRI data, specifically resting-state fMRI data, is performed here from the point of view of a hybrid Multivariate Mathematical Morphology induced by a supervised h-ordering defined on the fMRI time series by the response of Lattice Auto-associative Memories built from specific fMRI voxels. The supervised h-ordering values and the results of morphological filters, i.e. a morphological top-hat, allow to identify some brain networks depending on the seed voxel value. Results on a set of resting state fMRI images of schizophrenia patients and healthy controls show that these networks can be dependent on the subject class, thus providing discriminant findings that may be useful for machine learning approaches.