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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 ISSN 0162-1459.

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Official URL: http://dx.doi.org/10.1198/jasa.2011.ap09735

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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
Subjects: Q Science > QA Mathematics
R Medicine > R Medicine (General)
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
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:
DateEvent
1 March 2011Published
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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