Spatial Bayesian latent factor regression modeling of coordinate‐based meta‐analysis data

Spatial Bayesian latent factor regression modeling of coordinate‐based meta‐analysis data IntroductionFunctional magnetic resonance imaging (fMRI) has become an essential, non‐invasive, tool for learning patterns of activation in the working human brain (e.g., Pekka, ; Wager et al., ). Whenever a brain region is engaged in a particular task, there is an increased demand for oxygen in that region which is met by a localised increase in blood flow. The MRI scanner captures such changes in local oxygenation via a mechanism called the Blood Oxygenation Level‐Dependent (BOLD) effect; see, for example, Brown et al. () for a brief introduction on fMRI. The great popularity that fMRI has achieved in recent years is supported by various software packages that implement computationally efficient analysis through a mass univariate approach (MUA). Specifically, MUA consists of fitting a general linear regression model at each voxel independently of every other voxel, thus producing images of parameter estimates and test statistics. These images are then thresholded to identify significant voxels or clusters of voxels, and significance is typically determined via random field theory (Worsley et al., ) or permutation methods (Nichols and Holmes, ). Despite its simplicity, the MUA lacks an explicit spatial model. Even though the activation of nearby voxels is correlated, estimation with the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Spatial Bayesian latent factor regression modeling of coordinate‐based meta‐analysis data

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12713
Publisher site
See Article on Publisher Site

Abstract

IntroductionFunctional magnetic resonance imaging (fMRI) has become an essential, non‐invasive, tool for learning patterns of activation in the working human brain (e.g., Pekka, ; Wager et al., ). Whenever a brain region is engaged in a particular task, there is an increased demand for oxygen in that region which is met by a localised increase in blood flow. The MRI scanner captures such changes in local oxygenation via a mechanism called the Blood Oxygenation Level‐Dependent (BOLD) effect; see, for example, Brown et al. () for a brief introduction on fMRI. The great popularity that fMRI has achieved in recent years is supported by various software packages that implement computationally efficient analysis through a mass univariate approach (MUA). Specifically, MUA consists of fitting a general linear regression model at each voxel independently of every other voxel, thus producing images of parameter estimates and test statistics. These images are then thresholded to identify significant voxels or clusters of voxels, and significance is typically determined via random field theory (Worsley et al., ) or permutation methods (Nichols and Holmes, ). Despite its simplicity, the MUA lacks an explicit spatial model. Even though the activation of nearby voxels is correlated, estimation with the

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ;

References

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