The Library

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

Tools

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

Full text not available from this repository.

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

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

Dates:

Date	Event
July 2011	Published

Volume:

Vol.30

Number:

No.7

Number of Pages:

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)

References:

[1] A. Sutton, D. Jones, K. Abrams, T. Sheldon, and F. Song, Methods for
Meta-analysis in Medical Research. London: John Wiley, 2000.
[2] N. Lazar, B. Luna, J. A. Sweeney, and W. F. Eddy, “Combining
brains: A survey of methods for statistical pooling of information,”
NeuroImage, vol. 16, no. 2, pp. 538–50, 2002.
[3] G. Salimi-Khorshidi, S. Smith, J. Keltner, T. D. Wager, and T. E.
Nichols, “Meta-analysis of neuroimaging data: A comparison of
image-based and coordinate-based pooling of studies,” NeuroImage,
vol. 45, no. 3, pp. 810–23, Apr. 2009a.
[4] C. F. Beckmann, M. Jenkinson, and S. M. Smith, “General multilevel
linear modeling for group analysis in fMRI,” NeuroImage, vol. 20, no.
2, pp. 1052–63, 2003.
[5] M. W.Woolrich, T. E. Behrens, C. F. Beckmann, M. Jenkinson, and S.
M. Smith, “Multilevel linear modelling for fMRI group analysis using
Bayesian inference,” NeuroImage, vol. 21, no. 4, pp. 1732–47, 2004.
[6] A. Laird, J. Lancaster, and P. T. Fox, “Brainmap: The social evolution
of a functional neuroimaging database,” Neuroinformatics, vol. 3, pp.
65–78, 2005.
[7] P. Turkeltaub, G. Eden, K. Jones, and T. A. T. Zeffiro, “Meta-analysis
of the functional neuroanatomy of single-word reading: Method and
validation,” NeuroImage, vol. 16, no. 3.1, pp. 765–80, 2002.
[8] T. D. Wager, J. Jonides, and S. Reading, “Neuroimaging studies of
shifting attention: A meta-analysis,” NeuroImage, vol. 22, no. 4, pp.
1679–93, 2004.
[9] T. D.Wager, M. Lindquist, and L. Kapla, “Meta-analysis of functional
neuroimaging data: Current and future directions,” Soc Cogn Affect
Neurosci, vol. 2, no. 2, pp. 150–158, 2007.
[10] S. B. Eickhoff, A. R. Laird, C. Grefkes, L. E.Wang, K. Zilles, and P. T.
Fox, “Coordinate-based activation likelihood estimation meta-analysis
of neuroimaging data: A random-effects approach based on empirical
estimates of spatial uncertainty,” Hum Brain Mapp, 2009.
[11] J. Neumann, D. Cramon, and G. Lohmann, “Model-based clustering of
meta-analytic functional imaging data,” Hum Brain Mapp, vol. 29, no.
2, pp. 177–92, 2008.
[12] S. G. Costafreda, A. S. David, and M. J. Brammer, “A parametric approach
to voxel-based meta-analysis,” NeuroImage, vol. 46, no. 1, pp.
115–22, May 2009.
[13] C. Rasmussen and C. Williams, Gaussian Processes for Machine
Learning. : The MIT Press, 2006.
[14] M. Stein, Statistical Interpolation of Spatial Data: Some Theory for
Kriging. New York: Springer, 1999.
[15] A. Groves, M. Chappell, and M. W. Woolrich, “Combined spatial and
non-spatial prior for inference on MRI time-series,” NeuroImage, vol.
45, pp. 795–809, 2009.
[16] J. Copas and J. Shi, “A sensitivity analysis for publication bias in systematic
reviews,” Stat Methods Med Res, vol. 10, pp. 251–265, 2001.
[17] S. Smith, P. R. Bannister, C. Beckman, M. Brady, S. Clare, D. Flitney,
P. Hansen, M. Jenkinson, D. Leibovici, B. Ripley, M. Woolrich, and
J. Zhang, “FSL: New tools for functional and structural brain image
analysis,” NeuroImage, vol. 13, no. 6, pp. 249–249, 2001.
[18] M. Jenkinson, P. Bannister, M. Brady, and S. Smith, “Improved optimization
for the robust and accurate linear registration and motion
correction of brain images,” NeuroImage, vol. 17, no. 2, pp. 825–41,
2002.
[19] M. W. Woolrich, B. D. Ripley, M. Brady, and S. M. Smith, “Temporal
autocorrelation in univariate linear modeling of FMRI data,” NeuroImage,
vol. 14, no. 6, pp. 1370–86, 2001.
[20] M. Jenkinson and S. Smith, “A global optimisation method for robust
affine registration of brain images,” Med Image Anal, vol. 5, no. 2, pp.
143–56, 2001.
[21] L. Dice, “Measures of the amount of ecologic association between
species,” Ecology, vol. 26, no. 3, pp. 297–302, 1945.
[22] S. M. Smith and T. E. Nichols, “Threshold-free cluster enhancement:
Addressing problems of smoothing, threshold dependence and localisation
in cluster inference,” NeuroImage, vol. 44, no. 1, pp. 83–98,
2009.
[23] P. Bunch, J. Hamilton, G. Sanderson, and J. Hamilton, “A free response
approach to the measurement and characterization of radiographic observer
performance,” J. Appl. Photogr. Eng, vol. 4, pp. 166–172, 1978.
[24] G. Salimi-Khorshidi, S. Smith, and T. E. Nichols, “Bias and heterogeneity
in neuroimaging meta-analysis,” in 15th Annual Meeting of the
Organization for Human Brain Mapping Abstracts Online, 2009, vol.
SA-PM, p. 406.
[25] F. A. Nielsen, “Visualizing data mining results with the brede tools,”
Frontiers in Neuroinformatics, vol. 3, no. 26, 2009.
[26] K. L. Phan, T. Wager, S. F. Taylor, and I. Liberzon, “Functional neuroanatomy
of emotion: A meta-analysis of emotion activation studies
in PET and fMRI,” NeuroImage, vol. 16, no. 2, pp. 331–48, Jun. 2002.
[27] F. A. Nielsen and L. K. Hansen, “Modeling of activation data in the
brainmap database: Detection of outliers,” Hum Brain Mapp, vol. 15,
no. 3, pp. 146–56, 2002.
[28] F. Nielsen and L. K. Hansen, “Finding related functional neuroimaging
volumes,” Artif Intell Med, vol. 30, no. 2, pp. 141–51, 2004.