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There is growing interest in understanding how the structuralinterconnections among brain regions change with the occurrence of neurologicaldiseases. Diffusion weighted MRI imaging has allowed researchers tonon-invasively estimate a network of structural cortical connections made bywhite matter tracts, but current statistical methods for relating such networksto the presence or absence of a disease cannot exploit this rich networkinformation. Standard practice considers each edge independently or summarizesthe network with a few simple features. We enable dramatic gains in biologicalinsight via a novel unifying methodology for inference on brain networkvariations associated to the occurrence of neurological diseases. The key ofthis approach is to define a probabilistic generative mechanism directly on thespace of network configurations via dependent mixtures of low-rankfactorizations, which efficiently exploit network information and allow theprobability mass function for the brain network-valued random variable to varyflexibly across the group of patients characterized by a specific neurologicaldisease and the one comprising age-matched cognitively healthy individuals.

Unifying inference on brain network variations in neurological diseases: The Alzheimer's case