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Using gaussian-process regression for meta-analytic neuroimaging inference based on sparse observations

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Salimi-Khorshidi, Gholamreza, Nichols, Thomas E., Smith, Stephen M. and Woolrich, Mark W.. (2011) Using gaussian-process regression for meta-analytic neuroimaging inference based on sparse observations. IEEE Transactions on Medical Imaging, Vol.30 (No.7). pp. 1401-1416. ISSN 0278-0062

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Abstract

The purpose of neuroimaging meta-analysis is to localize the brain regions that are activated consistently in response to a certain intervention. As a commonly used technique, current coordinate-based meta-analyses (CBMA) of neuroimaging studies utilize relatively sparse information from published studies, typically only using (x,y,z) coordinates of the activation peaks. Such CBMA methods have several limitations. First, there is no way to jointly incorporate deactivation information when available, which has been shown to result in an inaccurate statistic image when assessing a difference contrast. Second, the scale of a kernel reflecting spatial uncertainty must be set without taking the effect size (e.g., Z-stat) into account. To address these problems, we employ Gaussian-process regression (GPR), explicitly estimating the unobserved statistic image given the sparse peak activation “coordinate” and “standardized effect-size estimate” data. In particular, our model allows estimation of effect size at each voxel, something existing CBMA methods cannot produce. Our results show that GPR outperforms existing CBMA techniques and is capable of more accurately reproducing the (usually unavailable) full-image analysis results.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
R Medicine > R Medicine (General)
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): Gaussian processes, Brain -- Imaging, Meta-analysis, Bayesian statistical decision theory
Journal or Publication Title: IEEE Transactions on Medical Imaging
Publisher: IEEE
ISSN: 0278-0062
Official Date: July 2011
Volume: Vol.30
Number: No.7
Number of Pages: 16
Page Range: pp. 1401-1416
Identification Number: 10.1109/TMI.2011.2122341
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Research Councils UK (RCUK), GlaxoSmithKline (GSK)
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URI: http://wrap.warwick.ac.uk/id/eprint/38161

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