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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. doi:10.1109/TMI.2011.2122341
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Official URL: http://dx.doi.org/10.1109/TMI.2011.2122341
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 | ||||
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| Subjects: | Q Science > QA Mathematics R Medicine > R Medicine (General) R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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| 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 | ||||
| Dates: |
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| Volume: | Vol.30 | ||||
| Number: | No.7 | ||||
| Number of Pages: | 16 | ||||
| Page Range: | pp. 1401-1416 | ||||
| DOI: | 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) |
Data sourced from Thomson Reuters' Web of Knowledge
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