title: A Bayesian framework for global tractography.
abstract: We readdress the diffusion tractography problem in a global and probabilistic manner. Instead of tracking through local orientations, we parameterise the connexions between brain regions at a global level, and then infer on global and local parameters simultaneously in a Bayesian framework. This approach offers a number of important benefits. The global nature of the tractography reduces sensitivity to local noise and modelling errors. By constraining tractography to ensure a connexion is found, and then inferring on the exact location of the connexion, we increase the robustness of connectivity-based parcellations, allowing parcellations of connexions that were previously invisible to tractography. The Bayesian framework allows a direct comparison of the evidence for connecting and non-connecting models, to test whether the connexion is supported by the data. Crucially, by explicit parameterisation of the connexion between brain regions, we infer on a parameter that is shared with models of functional connectivity. This model is a first step toward the joint inference on functional and anatomical connectivity.
authors.affiliation: Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK. saad@fmrib.ox.ac.uk
authors.forename: S
authors.initials: S
authors.lastname: Jbabdi
authors.forename: M W
authors.initials: MW
authors.lastname: Woolrich
authors.forename: J L R
authors.initials: JL
authors.lastname: Andersson
authors.forename: T E J
authors.initials: TE
authors.lastname: Behrens
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D003196:Computer Graphics; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D004292:Dominance, Cerebral / Q000502:physiology; D005625:Frontal Lobe / Q000033:anatomy & histology; D005829:Geniculate Bodies / Q000033:anatomy & histology; D006225:Hand / Q000294:innervation; D000882:Haplorhini; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009044:Motor Cortex / Q000033:anatomy & histology; D009412:Nerve Fibers / Q000648:ultrastructure; D009415:Nerve Net / Q000033:anatomy & histology*; D016571:Neural Networks, Computer; D010296:Parietal Lobe / Q000033:anatomy & histology; D017397:Prefrontal Cortex / Q000033:anatomy & histology; D011699:Putamen / Q000033:anatomy & histology; D012984:Software; D013702:Temporal Lobe / Q000033:anatomy & histology; D013788:Thalamus / Q000033:anatomy & histology; D014793:Visual Cortex / Q000033:anatomy & histology