%0 Journal Article
%T Unifying inference on brain network variations in neurological diseases: The Alzheimer's case
%A Daniele Durante
%A Madelaine Daianu
%A Neda Jahanshad
%A Paul M. Thompson
%A David B. Dunson
%J Statistics
%D 2015
%I arXiv
%X There is growing interest in understanding how the structural interconnections among brain regions change with the occurrence of neurological diseases. Diffusion weighted MRI imaging has allowed researchers to non-invasively estimate a network of structural cortical connections made by white matter tracts, but current statistical methods for relating such networks to the presence or absence of a disease cannot exploit this rich network information. Standard practice considers each edge independently or summarizes the network with a few simple features. We enable dramatic gains in biological insight via a novel unifying methodology for inference on brain network variations associated to the occurrence of neurological diseases. The key of this approach is to define a probabilistic generative mechanism directly on the space of network configurations via dependent mixtures of low-rank factorizations, which efficiently exploit network information and allow the probability mass function for the brain network-valued random variable to vary flexibly across the group of patients characterized by a specific neurological disease and the one comprising age-matched cognitively healthy individuals.
%U http://arxiv.org/abs/1510.05391v1