FMRI analysis through bayesian variable selection with a spatial prior

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel spatial Bayesian method for simultaneous activation detection and hemodynamic response function (HRF) estimation of functional magnetic resonance imaging (fMRI) data. A Bayesian variable selection approach is used to induce shrinkage and sparsity, with a spatial prior on latent variables representing activated hemodynamic response components. Then, the activation map is generated from the full spectrum of posterior inference constructed through a Markov chain Monte Carlo scheme, and HRFs at different voxels are estimated non-parametrically with information pooling from neighboring voxels. By integrating functional activation detection and HRFs estimation in a unified framework, our method is more robust to noise and less sensitive to model mis-specification.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2009
Pages714-717
Number of pages4
DOIs
StatePublished - Nov 17 2009
Event2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Other

Other2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
CountryUnited States
CityBoston, MA
Period6/28/097/1/09

Keywords

  • Activation detection
  • Bayesian variable selection
  • Hemodynamic response function
  • Markov chain Monte Carlo (MCMC)
  • Spatial prior

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Xia, J., Liang, F., & Wang, Y. M. (2009). FMRI analysis through bayesian variable selection with a spatial prior. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (pp. 714-717). [5193147] (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). https://doi.org/10.1109/ISBI.2009.5193147