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A Bayesian framework for global tractography
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Journal Article
A Bayesian framework for global tractography
2007
Overview
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.
Publisher
Elsevier Inc,Elsevier Limited
Subject
/ Animals
/ Diffusion Magnetic Resonance Imaging - methods
/ Dominance, Cerebral - physiology
/ Frontal Lobe - anatomy & histology
/ Geniculate Bodies - anatomy & histology
/ Humans
/ Image Processing, Computer-Assisted - methods
/ Imaging, Three-Dimensional - methods
/ Magnetic Resonance Imaging - methods
/ Motor Cortex - anatomy & histology
/ Nerve Fibers - ultrastructure
/ Nerve Net - anatomy & histology
/ Parietal Lobe - anatomy & histology
/ Prefrontal Cortex - anatomy & histology
/ Putamen - anatomy & histology
/ Software
/ Temporal Lobe - anatomy & histology
