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Meta analysis of functional neuroimaging data via Bayesian spatial point processes
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Kang, Jian, Nichols, Thomas E., Johnson, Timothy D. and Wager, Tor D. (2011) Meta analysis of functional neuroimaging data via Bayesian spatial point processes. Journal of the American Statistical Association, Vol.106 (No.493). pp. 124-134. doi:10.1198/jasa.2011.ap09735
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Official URL: http://dx.doi.org/10.1198/jasa.2011.ap09735
Abstract
As the discipline of functional neuroimaging grows there is an increasing interest in meta analysis of brain imaging studies. A typical neuroimaging meta analysis collects peak activation coordinates (foci) from several studies and identifies areas of consistent activation. Most imaging meta analysis methods only produce null hypothesis inferences and do not provide an interpretable fitted model. To overcome these limitations, we propose a Bayesian spatial hierarchical model using a marked independent cluster process. We model the foci as offspring of a latent study center process, and the study centers are in turn offspring of a latent population center process. The posterior intensity function of the population center process provides inference on the location of population centers, as well as the interstudy variability of foci about the population centers. We illustrate our model with a meta analysis consisting of 437 studies from 164 publications, show how two subpopulations of studies can be compared and assess our model via sensitivity analyses and simulation studies. Supplemental materials are available online.
| 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 > Statistics Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) |
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| Library of Congress Subject Headings (LCSH): | Brain -- Imaging -- Data processing, Meta-analysis, Bayesian statistical decision theory, Stochastic processes | ||||
| Journal or Publication Title: | Journal of the American Statistical Association | ||||
| Publisher: | American Statistical Association | ||||
| ISSN: | 0162-1459 | ||||
| Official Date: | 1 March 2011 | ||||
| Dates: |
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| Volume: | Vol.106 | ||||
| Number: | No.493 | ||||
| Number of Pages: | 11 | ||||
| Page Range: | pp. 124-134 | ||||
| DOI: | 10.1198/jasa.2011.ap09735 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Funder: | National Institutes of Health (U.S.) (NIH) | ||||
| Grant number: | R01-MH069326, 1RC1DA028608, R21MH082308 (NIH) | ||||
| Version or Related Resource: | This item was also presented at ENAR Spring Meeting, New Orleans, 21-24 March, 2010. |
Data sourced from Thomson Reuters' Web of Knowledge
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