Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis

  • Merlin Keller
  • Marc Lavielle
  • Matthieu Perrot
  • Alexis Roche
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)


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.


Posterior Probability Functional Network Statistical Parametric Mapping Registration Error Bayesian Model Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Friston, K.J.: 2. In: Human Brain Function, pp. 25–42. Academic Press, London (1997)Google Scholar
  2. 2.
    Nichols, T., Hayasaka, S.: Controlling the Familywise Error Rate in Functional Neuroimaging: A Comparative Review. Statistical Methods in Medical Research 12(5), 419–446 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Hayasaka, S., Nichols, T.: Validating Cluster Size Inference: Random Field and Permutation Methods. Neuroimage 20(4), 2343–2356 (2003)CrossRefGoogle Scholar
  4. 4.
    Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)CrossRefGoogle Scholar
  5. 5.
    Perrot, M., Rivière, D., Mangin, J.F.: Identifying cortical sulci from localizations, shape and local organization. In: 5th Proc. ISBI, Paris, France, pp. 420–423 (2008)Google Scholar
  6. 6.
    Thirion, B., Pinel, P., Mériaux, S., Roche, A., Dehaene, S., Poline, J.B.: Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses. Neuroimage 35(1), 105–120 (2007)CrossRefGoogle Scholar
  7. 7.
    Keller, M., Roche, A., Tucholka, A., Thirion, B.: Dealing with spatial normalization errors in fMRI group inference using hierarchical modeling. Statistica Sinica 18(4), 1357–1374 (2008)zbMATHMathSciNetGoogle Scholar
  8. 8.
    Friston, K., Glaser, D.E., Henson, R.N.A., Kiebel, S., Phillips, C., Ashburner, J.: Classical and Bayesian inference in neuroimaging: Applications. Neuroimage 16(2), 484–512 (2002)CrossRefGoogle Scholar
  9. 9.
    Smith, S.M., Nichols, T.E.: Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44(1), 83–98 (2009)CrossRefGoogle Scholar
  10. 10.
    Thirion, B., Tucholka, A., Keller, M., Pinel, P., Roche, A., Mangin, J.F., Poline, J.B.: High level group analysis of FMRI data based on Dirichlet process mixture models. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 482–494. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Bowman, D.F., Caffo, B., Bassett, S.S., Kilts, C.: A bayesian hierarchical framework for spatial modeling of FMRIdata. Neuroimage 39(1), 146–156 (2008)CrossRefGoogle Scholar
  12. 12.
    Makni, S., Idier, J., Vincent, T., Thirion, B., Dehaene-Lambertz, G., Ciuciu, P.: A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI. Neuroimage 41(3), 941–969 (2008)CrossRefGoogle Scholar
  13. 13.
    Penny, W.D., Trujillo-Barreto, N., Friston, K.J.: Bayesian fMRI time series analysis with spatial priors. Neuroimage 23(2), 350–362 (2005)CrossRefGoogle Scholar
  14. 14.
    Friston, K., Ashburner, J., Frith, C., Poline, J.B., Heather, J., Frackowiak, R.: Spatial registration and normalization of images. Hum. Brain Mapp. 3(3), 165–189 (1995)CrossRefGoogle Scholar
  15. 15.
    Chib, S.: Marginal likelihood from the Gibbs output. J. Amer. Statist. Assoc. 90, 1313–1321 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Kuhn, E., Lavielle, M.: Coupling a stochastic approximation of EM with a MCMC procedure. ESAIM P&S 8, 115–131 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Pinel, P., Thirion, B., Mériaux, S., Jobert, A., Serres, J., Le Bihan, D., Poline, J.B., Dehaene, S.: Fast reproducible identification and large-scale databasing of individual functional cognitive networks. BMC Neurosci. 8(1), 91 (2007)CrossRefGoogle Scholar
  18. 18.
    Cavanna, A.E., Trimble, M.R.: The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129(3), 564–583 (2006)CrossRefGoogle Scholar

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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