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

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  • Silvia Montagna
  • Tor Wager
  • Lisa Feldman Barrett
  • Timothy D. Johnson
  • Thomas E. Nichols

Abstract

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta†analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate†Based Meta†Analysis (CBMA). Neuroimaging meta†analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study†specific log intensity function is characterized as a linear combination of a high†dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study†level covariates (meta†regression), significantly expanding the capabilities of the current neuroimaging meta†analysis methods available. We apply our methodology to synthetic data and neuroimaging meta†analysis datasets.

Suggested Citation

  • Silvia Montagna & Tor Wager & Lisa Feldman Barrett & Timothy D. Johnson & Thomas E. Nichols, 2018. "Spatial Bayesian latent factor regression modeling of coordinate†based meta†analysis data," Biometrics, The International Biometric Society, vol. 74(1), pages 342-353, March.
  • Handle: RePEc:bla:biomet:v:74:y:2018:i:1:p:342-353
    DOI: 10.1111/biom.12713
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    References listed on IDEAS

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    1. Silvia Montagna & Surya T. Tokdar & Brian Neelon & David B. Dunson, 2012. "Bayesian Latent Factor Regression for Functional and Longitudinal Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1064-1073, December.
    2. Kang, Jian & Johnson, Timothy D. & Nichols, Thomas E. & Wager, Tor D., 2011. "Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 124-134.
    3. Gerhard Arminger & Bengt Muthén, 1998. "A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolis-hastings algorithm," Psychometrika, Springer;The Psychometric Society, vol. 63(3), pages 271-300, September.
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    1. Pantelis Samartsidis & Shaun R. Seaman & Silvia Montagna & André Charlett & Matthew Hickman & Daniela De Angelis, 2020. "A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1437-1459, October.
    2. Cui Guo & Jian Kang & Timothy D. Johnson, 2022. "A spatial Bayesian latent factor model for image‐on‐image regression," Biometrics, The International Biometric Society, vol. 78(1), pages 72-84, March.

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