Bayesian Spatiotemporal Modeling for Detecting Neuronal Activation via Functional Magnetic Resonance Imaging

  • Martin Bezener
  • Lynn E. Eberly
  • John Hughes
  • Galin JonesEmail author
  • Donald R. Musgrove
Part of the Springer Handbooks of Computational Statistics book series (SHCS)


We consider recent developments in Bayesian spatiotemporal models for detecting neuronal activation in fMRI experiment. A Bayesian approach typically results in complicated posterior distributions that can be of enormous dimension for a whole-brain analysis, thus posing a formidable computational challenge. Recently developed Bayesian approaches to detecting local activation have proved computationally efficient while requiring few modeling compromises. We review two such methods and implement them on a data set from the Human Connectome Project in order to show that, contrary to popular opinion, careful implementation of Markov chain Monte Carlo methods can be used to obtain reliable results in a matter of minutes.


Bayesian variable selection fMRI MCMC Spatiotemporal Areal model 



Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

John Hughes was supported by the Simons Foundation. Galin Jones was supported by the National Institutes of Health and the National Science Foundation. Donald R. Musgrove was supported by University of Minnesota Academic Health Center Faculty Research Development Grant.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Martin Bezener
    • 1
  • Lynn E. Eberly
    • 2
  • John Hughes
    • 3
  • Galin Jones
    • 4
    Email author
  • Donald R. Musgrove
    • 2
  1. 1.Stat-Ease, Inc.MinneapolisUSA
  2. 2.Division of BiostatisticsUniversity of Minnesota, Twin CitiesMinneapolisUSA
  3. 3.Department of Biostatistics and Informatics, Colorado School of Public HealthUniversity of ColoradoDenverUSA
  4. 4.School of StatisticsUniversity of Minnesota, Twin CitiesMinneapolisUSA

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