Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis

  • Merlin Keller
  • Marc Lavielle
  • Matthieu Perrot
  • Alexis Roche
Conference paper

DOI: 10.1007/978-3-642-04271-3_55

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)
Cite this paper as:
Keller M., Lavielle M., Perrot M., Roche A. (2009) Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis. In: Yang GZ., Hawkes D., Rueckert D., Noble A., Taylor C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5762. Springer, Berlin, Heidelberg

Abstract

A new approach for fMRI group data analysis is introduced to overcome the limitations of standard voxel-based testing methods, such as Statistical Parametric Mapping (SPM). Using a Bayesian model selection framework, the functional network associated with a certain cognitive task is selected according to the posterior probabilities of mean region activations, given a pre-defined anatomical parcellation of the brain. This approach enables us to control a Bayesian risk that balances false positives and false negatives, unlike the SPM-like approach, which only controls false positives. On data from a mental calculation experiment, it detected the functional network known to be involved in number processing, whereas the SPM-like approach either swelled or missed the different activation regions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Merlin Keller
    • 1
    • 2
    • 3
  • Marc Lavielle
    • 2
    • 5
  • Matthieu Perrot
    • 1
    • 4
  • Alexis Roche
    • 1
  1. 1.LNAO, Neurospin, CEAGif-sur-YvetteFrance
  2. 2.Department of Probability and StatisticsUniversity of Paris SudFrance
  3. 3.PARIETAL team, INRIA SaclayFrance
  4. 4.INSERM U.797OrsayFrance
  5. 5.University René DescartesParisFrance

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