Document Detail


Graph-partitioned spatial priors for functional magnetic resonance images.
MedLine Citation:
PMID:  18790064     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Spatial models of functional magnetic resonance imaging (fMRI) data allow one to estimate the spatial smoothness of general linear model (GLM) parameters and eschew pre-process smoothing of data entailed by conventional mass-univariate analyses. Recently diffusion-based spatial priors [Harrison, L.M., Penny, W., Daunizeau, J., and Friston, K.J. (2008). Diffusion-based spatial priors for functional magnetic resonance images. NeuroImage.] were proposed, which provide a way to formulate an adaptive spatial basis, where the diffusion kernel of a weighted graph-Laplacian (WGL) is used as the prior covariance matrix over GLM parameters. An advantage of these is that they can be used to relax the assumption of isotropy and stationarity implicit in smoothing data with a fixed Gaussian kernel. The limitation of diffusion-based models is purely computational, due to the large number of voxels in a brain volume. One solution is to partition a brain volume into slices, using a spatial model for each slice. This reduces computational burden by approximating the full WGL with a block diagonal form, where each block can be analysed separately. While fMRI data are collected in slices, the functional structures exhibiting spatial coherence and continuity are generally three-dimensional, calling for a more informed partition. We address this using the graph-Laplacian to divide a brain volume into sub-graphs, whose shape can be arbitrary. Their shape depends crucially on edge weights of the graph, which can be based on the Euclidean distance between voxels (isotropic) or on GLM parameters (anisotropic) encoding functional responses. The result is an approximation the full WGL that retains its 3D form and also has potential for parallelism. We applied the method to high-resolution (1 mm(3)) fMRI data and compared models where a volume was divided into either slices or graph-partitions. Models were optimized using Expectation-Maximization and the approximate log-evidence computed to compare these different ways to partition a spatial prior. The high-resolution fMRI data presented here had greatest evidence for the graph partitioned anisotropic model, which was best able to preserve fine functional detail.
Authors:
L M Harrison; W Penny; G Flandin; C C Ruff; N Weiskopf; K J Friston
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-08-23
Journal Detail:
Title:  NeuroImage     Volume:  43     ISSN:  1095-9572     ISO Abbreviation:  Neuroimage     Publication Date:  2008 Dec 
Date Detail:
Created Date:  2008-11-05     Completed Date:  2009-02-10     Revised Date:  2014-02-24    
Medline Journal Info:
Nlm Unique ID:  9215515     Medline TA:  Neuroimage     Country:  United States    
Other Details:
Languages:  eng     Pagination:  694-707     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Brain Mapping / methods*
Computer Simulation
Evoked Potentials, Visual / physiology*
Humans
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods*
Information Storage and Retrieval / methods*
Magnetic Resonance Imaging / instrumentation,  methods*
Models, Neurological
Phantoms, Imaging
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique*
Visual Cortex / physiology*
Grant Support
ID/Acronym/Agency:
056750//Wellcome Trust; 088130//Wellcome Trust; //Wellcome Trust
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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