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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. ISSN 0162-1459

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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

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):

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:

Date	Event
1 March 2011	Published

Volume:

Vol.106

Number:

No.493

Number of Pages:

Page Range:

pp. 124-134

Identifier:

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.

References:

Baddeley, A., Møller, J., and Waagepetersen, R. (2000), “Non- and Semi-
Parametric Estimation of Interaction in Inhomogeneous Point Patterns,”
Statistica Neerlandica, 54, 329–350. [132]
Eickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K., and
Fox, P. T. (2009), “Coordinate-Based Activation Likelihood Estimation
Meta-Analysis of Neuroimaging Data: A Random-Effects Approach Based
on Empirical Estimates of Spatial Uncertainty,” Human Brain Mapping, 30,
2907–2926. [124,125,129]
Escobar, M. D., and West, M. (1995), “Bayesian Density Estimation and Inference
Using Mixtures,” Journal of the American Statistical Association, 90,
577–588. [129]
Feng, C., Narayana, S., Lancaster, J. L., Jerabek, P. A., Arnow, T. L., Zhu, F.,
Tan, L. H., Fox, P. T., and Gao, J. (2004), “CBF Changes During Brain
Activation: fMRI vs. PET,” NeuroImage, 22, 443–446. [125]
Fox, P. T., Lancaster, J. L., Parsons, L. M., Xiong, J., and Zamarripa, F. (1997),
“Functional Volumes Modeling: Theory and Preliminary Assessment,” Human
Brain Mapping, 5, 306–311. [124]
Gelman, A., and Rubin, D. B. (1992), “Inference From Iterative Simulation
Using Multiple Sequences,” Statistical Science, 7, 457–472. [129]
Illian, J., Penttinen, A., Stoyan, H., and Stoyan, D. (2008), Statistical Analysis
and Modelling of Spatial Point Patterns, West Sussex, England: Wiley.
[125]
Illian, J. B., Møller, J., and Waagepetersen, R. P. (2009), “Hierarchical Spatial
Point Process Analysis for a Plant Community With High Biodiversity,”
Environmental and Ecological Statistics, 16, 389–405. [132]
Jezzard, P., Matthews, P. M., and Smith, S. M. (eds.) (2001), Functional MRI:
An Introduction to Methods, Oxford, England: Oxford University Press.
[124]
Kober, H., Barrett, L. F., Joseph, J., Bliss-Moreau, E., Lindquist, K., andWager,
T. D. (2008), “Functional Grouping and Cortical-Subcortical Interactions
in Emotion: A Meta-Analysis of Neuroimaging Studies,” NeuroImage, 42,
998–1031. [124,128,129]
Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., Woods, R.,
Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, L., Thompson, P.,
MacDonald, D., Iacoboni, M., Schormann, T., Amunts, K., Palomero-
Gallagher, N., Geyer, S., Parsons, L., Narr, K., Kabani, N., Le Goualher,
G., Boomsma, D., Cannon, T., Kawashima, R., and Mazoyer, B. (2001),
“A Probabilistic Atlas and Reference System for the Human Brain: International
Consortium for Brain Mapping (ICBM),” Philosophical Transactions
of the Royal Society of London, Ser. B, 356, 1293–1322. [124]
Møller, J., and Waagepetersen, R. P. (2004), Statistical Inference and Simulation
for Spatial Point Processes, Boca Raton, FL: Chapman & Hall/CRC.
[125,126,133]
(2007), “Modern Statistics for Spatial Point Processes” (with discussion),
Scandinavian Journal of Statistics, 34, 643–711. [125,126]
Nielsen, F. A., and Hansen, L. K. (2002), “Modeling of Activation Data in the
BrainMap Database: Detection of Outliers,” Human Brain Mapping, 15,
146–156. [124]
Phelps, E. A., and LeDoux, J. E. (2005), “Contributions of the Amygdala to
Emotion Processing: From Animal Models to Human Behavior,” Neuron,
48, 175–187. [129]
Press, S. J. (1982), Applied Multivariate Analysis (2nd ed.), New York: Dover.
[129]
Radua, J., and Mataix-Cols, D. (2009), “Voxel-Wise Meta-Analysis of Grey
Matter Changes in Obsessive-Compulsive Disorder,” The British Journal of
Psychiatry, 195, 393–402. [124]
Raichle, M. (2003), “Functional Brain Imaging and Human Brain Function,”
Journal of Neuroscience, 23, 3959–3962. [124]
Salimi-Khorshidi, G., Smith, S. M., Keltner, J. R., Wager, T. D., and Nichols,
T. E. (2009), “Meta-Analysis of Neuroimaging Data: A Comparison of
Image-Based and Coordinate-Based Pooling of Studies,” NeuroImage, 45,
810–823. [124]
Thirion, B., Pinel, P., Mériaux, S., Roche, A., Dehaene, S., and Poline, J.-B.
(2007), “Analysis of a Large fMRI Cohort: Statistical and Methodological
Issues for Group Analyses,” Neuroimage, 35, 105–120. [124]
Turkeltaub, P. E., Eden, G. F., Jones, K. M., and Zeffiro, T. A. (2002), “Meta-
Analysis of the Functional Neuroanatomy of Single-Word Reading: Method
and Validation,” NeuroImage, 16, 765–780. [124]
van Lieshout, M. N. M., and Baddeley, A. J. (2002), “Extrapolating and Interpolating
Spatial Patterns,” in Spatial Cluster Modelling, eds. A. B. Lawson
and D. G. T. Denison, Boca Raton, FL: Chapman & Hall/CRC, chapter 4,
pp. 61–86. [125]
Wager, T. D., Jonides, J., and Reading, S. (2004), “Neuroimaging Studies of
Shifting Attention: A Meta-Analysis,” NeuroImage, 22, 1679–1693. [124]
Wager, T. D., Lindquist, M., and Kaplan, L. (2007), “Meta-Analysis of Functional
Neuroimaging Data: Current and Future Directions,” Social Cognitive
and Affective Neuroscience, 2, 150–158. [129]