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

Jeong Hwan Kook (), Michele Guindani (), Linlin Zhang () and Marina Vannucci ()
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Jeong Hwan Kook: Rice University
Michele Guindani: UC Irvine
Linlin Zhang: Schlumberger
Marina Vannucci: Rice University

Statistics in Biosciences, 2019, vol. 11, issue 1, No 2, 3-21

Abstract: 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.

Keywords: General linear model; MATLAB; Multi-subject fMRI data; Non-parametric variable selection priors; Spatio-temporal modeling; Variational Bayes (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s12561-017-9205-0

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