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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.

Original publication




Journal article



Publication Date





116 - 129


Algorithms, Animals, Bayes Theorem, Brain, Brain Mapping, Computer Graphics, Diffusion Magnetic Resonance Imaging, Dominance, Cerebral, Frontal Lobe, Geniculate Bodies, Hand, Haplorhini, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Models, Statistical, Motor Cortex, Nerve Fibers, Nerve Net, Neural Networks (Computer), Parietal Lobe, Prefrontal Cortex, Putamen, Software, Temporal Lobe, Thalamus, Visual Cortex