Fifty shades of gray, matter: Using bayesian priors to improve the power of whole-brain voxel- and connexelwise inferences

Krzysztof J. Gorgolewski, Pierre Louis Bazin, Haakon Engen, Daniel S. Margulies

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

Abstract

To increase the power of neuroimaging analyses, it is common practice to reduce the whole-brain search space to subset of hypothesis-driven regions-of-interest (ROIs). Rather than strictly constrain analyses, we propose to incorporate prior knowledge using probabilistic ROIs (pROIs) using a hierarchical Bayesian framework. Each voxel prior probability of being 'of-interest' or 'of-non-interest' is used to perform a weighted fit of a mixture model. We demonstrate the utility of this approach through simulations with various pROIs, and the applicability using a prior based on the NeuroSynth database search term 'emotion' for thresholding the fMRI results of an emotion processing task. The modular structure of pROI correction facilitates the inclusion of other innovations in Bayesian mixture modeling, and offers a foundation for balancing between exploratory analyses without neglecting prior knowledge.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
Pages194-197
Number of pages4
DOIs
StatePublished - 2013
Event2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013 - Philadelphia, PA, United States
Duration: Jun 22 2013Jun 24 2013

Publication series

NameProceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013

Other

Other2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
Country/TerritoryUnited States
CityPhiladelphia, PA
Period6/22/136/24/13

Keywords

  • Bayesian inference
  • fMRI priors
  • inference
  • mixture models

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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