A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
| dc.citation.firstpage | 638 | en_US |
| dc.citation.issueNumber | 2 | en_US |
| dc.citation.journalTitle | The Annals of Applied Statistics | en_US |
| dc.citation.lastpage | 666 | en_US |
| dc.citation.volumeNumber | 10 | en_US |
| dc.contributor.author | Zhang, Linlin | en_US |
| dc.contributor.author | Guindani, Michele | en_US |
| dc.contributor.author | Versace, Francesco | en_US |
| dc.contributor.author | Engelmann, Jeffrey M. | en_US |
| dc.contributor.author | Vannucci, Marina | en_US |
| dc.date.accessioned | 2017-05-09T20:16:07Z | en_US |
| dc.date.available | 2017-05-09T20:16:07Z | en_US |
| dc.date.issued | 2016 | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.citation | Zhang, Linlin, Guindani, Michele, Versace, Francesco, et al.. "A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data." <i>The Annals of Applied Statistics,</i> 10, no. 2 (2016) Project Euclid: 638-666. https://doi.org/10.1214/16-AOAS926. | en_US |
| dc.identifier.doi | https://doi.org/10.1214/16-AOAS926 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/94216 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Project Euclid | en_US |
| dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
| dc.subject.keyword | multi-subject fMRI | en_US |
| dc.subject.keyword | spatiotemporal linear regression | en_US |
| dc.subject.keyword | variable selection priors | en_US |
| dc.subject.keyword | variational Bayes | en_US |
| dc.title | A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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