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NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data

Author

Listed:
  • Jeong Hwan Kook

    (Rice University)

  • Michele Guindani

    (UC Irvine)

  • Linlin Zhang

    (Schlumberger)

  • Marina Vannucci

    (Rice University)

Abstract

In this paper, we introduce NPBayes-fMRI, a user-friendly MATLAB GUI that implements a unified, probabilistically coherent non-parametric Bayesian framework for the analysis of task-related fMRI data from multi-subject experiments. The modeling approach is based on a spatio-temporal linear regression model that specifically accounts for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject non-parametric variable selection prior. A characteristic feature of the approach is that it results in a clustering of the subjects into subgroups characterized by similar brain responses, while simultaneously producing group-level as well as subject-level activation maps. This is distinct from two-stage “group analysis” approaches traditionally considered in the fMRI literature, which separate the inference on the individual fMRI time courses from the inference at the population level. Here, we first describe the models and a Variational Bayes algorithm for posterior inference. Next, we introduce the toolbox and illustrate its features via an example.

Suggested Citation

  • Jeong Hwan Kook & Michele Guindani & Linlin Zhang & Marina Vannucci, 2019. "NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 3-21, April.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:1:d:10.1007_s12561-017-9205-0
    DOI: 10.1007/s12561-017-9205-0
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    References listed on IDEAS

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    1. Wenguang Sun & Brian J. Reich & T. Tony Cai & Michele Guindani & Armin Schwartzman, 2015. "False discovery control in large-scale spatial multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 59-83, January.
    2. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
    3. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    4. Jaesik Jeong & Marina Vannucci & Kyungduk Ko, 2013. "A Wavelet-Based Bayesian Approach to Regression Models with Long Memory Errors and Its Application to fMRI Data," Biometrics, The International Biometric Society, vol. 69(1), pages 184-196, March.
    5. Smith, Michael & Fahrmeir, Ludwig, 2007. "Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 417-431, June.
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    Cited by:

    1. Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.

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