title: From connectivity models to region labels: identifying foci of a neurological disorder.
abstract: We propose a novel approach to identify the foci of a neurological disorder based on anatomical and functional connectivity information. Specifically, we formulate a generative model that characterizes the network of abnormal functional connectivity emanating from the affected foci. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. We employ the variational expectation-maximization algorithm to fit the model and subsequently identify both the afflicted regions and the differences in connectivity induced by the disorder. We demonstrate our method on a population study of schizophrenia.
authors.forename: Archana
authors.initials: A
authors.lastname: Venkataraman
authors.forename: Marek
authors.initials: M
authors.lastname: Kubicki
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D016022:Case-Control Studies; D056324:Diffusion Tensor Imaging / Q000379:methods; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D012559:Schizophrenia / Q000503:physiopathology*