Connectivity-Informed fMRI Activation Detection

  • Bernard Ng
  • Rafeef Abugharbieh
  • Gael Varoquaux
  • Jean Baptiste Poline
  • Bertrand Thirion
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)


A growing interest has emerged in studying the correlation structure of spontaneous and task-induced brain activity to elucidate the functional architecture of the brain. In particular, functional networks estimated from resting state (RS) data were shown to exhibit high resemblance to those evoked by stimuli. Motivated by these findings, we propose a novel generative model that integrates RS-connectivity and stimulus-evoked responses under a unified analytical framework. Our model permits exact closed-form solutions for both the posterior activation effect estimates and the model evidence. To learn RS networks, graphical LASSO and the oracle approximating shrinkage technique are deployed. On a cohort of 65 subjects, we demonstrate increased sensitivity in fMRI activation detection using our connectivity-informed model over the standard univariate approach. Our results thus provide further evidence for the presence of an intrinsic relationship between brain activity during rest and task, the exploitation of which enables higher detection power in task-driven studies.


activation detection connectivity prior fMRI resting-state 


  1. 1.
    Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.B., Frith, C.D., Frackowiak, R.S.J.: Statistical Parametric Maps in Functional Imaging: A General Linear Approach. Hum. Brain Mapp. 2, 189–210 (1995)CrossRefGoogle Scholar
  2. 2.
    Rogers, B.P., Morgan, V.L., Newton, A.T., Gore, J.C.: Assessing Functional Connectivity in the Human Brain by fMRI. Magn. Reson. Imaging 25, 1347–1357 (2007)CrossRefGoogle Scholar
  3. 3.
    Ng, B., Hamarneh, G., Abugharbieh, R.: Detecting Brain Activation in fMRI Using Group Random Walker. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 331–338. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Descombes, X., Kruggel, F., von Cramon, D.Y.: Spatio-Temporal fMRI Analysis Using Markov Random Fields. IEEE Trans. Med. Imaging 17, 1028–1039 (1998)CrossRefGoogle Scholar
  5. 5.
    Penny, W.D., Trujillo-Barreto, N.J., Friston, K.J.: Bayesian fMRI Time Series Analysis with Spatial Priors. NeuroImage 24, 350–362 (2005)CrossRefGoogle Scholar
  6. 6.
    Harrison, L.M., Penny, W.D., Asburner, J., Trujillo-Barreto, N.J., Friston, K.J.: Diffusion-based Spatial Priors for Imaging. NeuroImage 38, 677–695 (2007)CrossRefGoogle Scholar
  7. 7.
    Woolrich, M.W., Jenkinson, M., Brady, J.M., Smith, S.M.: Fully Bayesian Spatio-temporal Modeling of fMRI Data. IEEE Trans. Med. Imaging 23, 213–231 (2004)CrossRefGoogle Scholar
  8. 8.
    Fox, M.D., Raichle, M.E.: Spontaneous Fluctuations in Brain Activity Observed with Functional Magnetic Resonance Imaging. Nat. Rev. Neurosci. 8, 700–711 (2007)CrossRefGoogle Scholar
  9. 9.
    Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., Filippini, N., Watkins, K.E., Toro, R., Laird, A.R., Beckmann, C.F.: Correspondence of the Brain’s Functional Architecture During Activation and Rest. Proc. Natl. Acad. Sci. 106, 13040–13045 (2009)CrossRefGoogle Scholar
  10. 10.
    Fox, M.D., Greicius, M.: Clinical Applications of Resting State Functional Connectivity. Front. Syst. Neurosci. 4, 19 (2010)Google Scholar
  11. 11.
    Friedman, J., Hastie, T., Tibshirani, R.: Sparse Inverse Covariance Estimation with the Graphical LASSO. Biostats. 9, 432–441 (2008)CrossRefzbMATHGoogle Scholar
  12. 12.
    Chen, Y., Wiesel, A., Eldar, Y.C., Hero, A.O.: Shrinkage Algorithms for MMSE Covariance Estimation. IEEE Trans. Sig. Proc. 58, 5016–5029 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Minka, T.P.: Bayesian Linear Regression. Technical report, MIT Media Lab (2001)Google Scholar
  14. 14.
    Schmidt, M., Fung, G., Rosales, R.: Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 286–297. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Pinel, P., Thirion, B., Meriaux, S., Jober, A., Serres, J., Le Bihan, D., Poline, J.B., Dehaene, S.: Fast Reproducible Identification and Large-scale Databasing of Individual Functional Cognitive Networks. BioMed. Central Neurosci. 8, 91 (2007)Google Scholar
  16. 16.
    Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., Poline, J.B.: Dealing with the Shortcomings of Spatial Normalization: Multi-subject Parcellation of fMRI Datasets. Hum. Brain Mapp. 27, 678–693 (2006)CrossRefGoogle Scholar
  17. 17.
    Nichols, T., Hayasaka, S.: Controlling the Familywise Error Rate in Functional Neuroimaging: a Comparative Review. Stat. Methods Med. Research 12, 419–446 (2003)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bernard Ng
    • 1
  • Rafeef Abugharbieh
    • 1
  • Gael Varoquaux
    • 2
  • Jean Baptiste Poline
    • 2
  • Bertrand Thirion
    • 2
  1. 1.Biomedical Signal and Image Computing LabUBCCanada
  2. 2.Parietal Team, Neurospin, INRIA Saclay-Ile-de-FranceFrance

Personalised recommendations