title: Bayesian analysis of neuroimaging data in FSL.
abstract: Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.
authors.affiliation: University of Oxford Centre for Functional MRI of the Brain, UK. woolrich@fmrib.ox.ac.uk
authors.forename: Mark W
authors.initials: MW
authors.lastname: Woolrich
authors.forename: Saad
authors.initials: S
authors.lastname: Jbabdi
authors.forename: Brian
authors.initials: B
authors.lastname: Patenaude
authors.forename: Michael
authors.initials: M
authors.lastname: Chappell
authors.forename: Salima
authors.initials: S
authors.lastname: Makni
authors.forename: Timothy
authors.initials: T
authors.lastname: Behrens
authors.forename: Christian
authors.initials: C
authors.lastname: Beckmann
authors.forename: Mark
authors.initials: M
authors.lastname: Jenkinson
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Smith
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D012984:Software