title: Joint generative model for fMRI/DWI and its application to population studies.
abstract: We propose a novel probabilistic framework to merge information from DWI tractography and resting-state fMRI correlations. In particular, we model the interaction of latent anatomical and functional connectivity templates between brain regions and present an intuitive extension to population studies. We employ a mean-field approximation to fit the new model to the data. The resulting algorithm identifies differences in latent connectivity between the groups. We demonstrate our method on a study of normal controls and schizophrenia patients.
authors.affiliation: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.
authors.forename: Archana
authors.initials: A
authors.lastname: Venkataraman
authors.forename: Yogesh
authors.initials: Y
authors.lastname: Rathi
authors.forename: Marek
authors.initials: M
authors.lastname: Kubicki
authors.forename: Carl-Fredrik
authors.initials: CF
authors.lastname: Westin
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D001921:Brain / Q000473:pathology* / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005071:Evoked Potentials; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D012559:Schizophrenia / Q000175:diagnosis / Q000473:pathology* / Q000503:physiopathology*; D013382:Subtraction Technique