Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631787
Title: Diffusion-based spatial priors for imaging
Author: Harrison, L. M.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2008
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Abstract:
We describe a Bayesian scheme to analyze images, which uses spatial priors encoded by a diffusion kernel, based on a weighted graph Laplacian. This provides a general framework to formulate a spatial model, whose parameters can be optimised. The standard practice using the software statistical parametric mapping (SPM) is to smooth imaging data using a fixed Gaussian kernel as a pre-processing step before applying a mass-univariate statistical model (e.g., a general linear model) to provide images of parameter estimates (Friston et al., 2006). This entails the strong assumption that data are generated smoothly throughout the brain. An alternative is to include smoothness in a multivariate statistical model (Penny et al., 2005). The advantage of the latter is that each parameter field is smoothed automatically, according to a measure of uncertainty, given the data. Explicit spatial priors enable formal model comparison of different prior assumptions, e.g. that data are generated from a stationary (i.e. fixed throughout the brain) or non-stationary spatial process. We describe the motivation, background material and theory used to formulate diffusion-based spatial priors for fMRI data and apply it to three different datasets, which include standard and high-resolution data. We compare mass-univariate ordinary least squares estimates of smoothed data and three Bayesian models spatially independent, stationary and non-stationary spatial models of non-smoothed data. The latter of which can be used to preserve boundaries between functionally selective regional responses of the brain, thereby increasing the spatial detail of inferences about cortical responses to experimental input.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.631787  DOI: Not available
Share: