Nichols, Thomas E., Xu, L. and Johnson, T. D. (2009) A hierarchical spatial Bayesian model for multisubject functional MRI data. In: Oxford-Warwick Statistics Seminar, University of Warwick, Coventry, UK, 5 Nov 2009 (Unpublished)

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Abstract

There are a rich collection of tools available for making inference for fMRI data, but most are based on the 'Mass Univariate' approach where univariate models are individually fit at each voxel. A critical shortcoming of these methods is that they cannot explicitly model the spatial structure of fMRI signals. For multi-subject fMRI analyses, I argue this is particularly crucial, since even after atlas warping there is considerable spatial variability in activation location over subjects. I will present a hierarchical spatial model for multi-subject fMRI analyses, where latent population- and individual centres fit the anticipated focal signals. The model uses priors for identifiability and full posterior sampling to provide inference on a variety of measures of interest unavailable in a mass-univariate framework, including population prevalence of activation and inter-subject spread of activation about population centres. I show evaluations of the model with simulations and demonstrate it with real data. Time permitting, I will also show how this framework generalizes to other settings, including 'spatial meta-analyses' and lesion modelling in Multiple Sclerosis.

Item Type:	Conference Item (Lecture)
Subjects:	Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science > Statistics Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Imaging -- Statistical methods, Magnetic resonance imaging, Bayesian statistical decision theory
Date:	5 November 2009
Status:	Not Peer Reviewed
Publication Status:	Unpublished
Realised As:
Version or Related Resource:	Also given at Functional MRI Facility, National Institutes of Health, 20 January, 2010; Massachusetts Institute of Technology, CSAIL Seminar, 6 May, 2010; University of Bristol, Department of Mathematics, Stats Seminar, 21 May, 2010; University of Warwick, Bioimaging Group, 26 May, 2010; and the University of Nottingham, Department of Statistics Seminar, 9 December 2010.
Conference Paper Type:	Lecture
Title of Event:	Oxford-Warwick Statistics Seminar
Type of Event:	Other
Location of Event:	University of Warwick, Coventry, UK
Date(s) of Event:	5 Nov 2009
URI:	http://wrap.warwick.ac.uk/id/eprint/41140

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