The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 10, Number 2 (2016), 638-666.
A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
In this paper we propose a unified, probabilistically coherent framework for the analysis of task-related brain activity in multi-subject fMRI experiments. 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. In our modeling approach we consider a spatiotemporal linear regression model and specifically account for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject nonparametric variable selection prior. For posterior inference, in addition to Markov chain Monte Carlo sampling algorithms, we develop suitable variational Bayes algorithms. We show on simulated data that variational Bayes inference achieves satisfactory results at more reduced computational costs than using MCMC, allowing scalability of our methods. In an application to data collected to assess brain responses to emotional stimuli our method correctly detects activation in visual areas when visual stimuli are presented.
Ann. Appl. Stat., Volume 10, Number 2 (2016), 638-666.
Received: May 2015
Revised: December 2015
First available in Project Euclid: 22 July 2016
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Zhang, Linlin; Guindani, Michele; Versace, Francesco; Engelmann, Jeffrey M.; Vannucci, Marina. A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data. Ann. Appl. Stat. 10 (2016), no. 2, 638--666. doi:10.1214/16-AOAS926. https://projecteuclid.org/euclid.aoas/1469199888
- Supplement to “A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data”. The supplementary material [Zhang et al. (2016)] contains a detailed description of the MCMC steps and of the VB inner and outer loops.