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International Journal of Biomedical Imaging
Volume 2011, Article ID 350838, 13 pages
http://dx.doi.org/10.1155/2011/350838
Research Article

Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

1PARIETAL Team, INRIA Saclay-Île-de-France, 91191 Saclay, France
2Laboratoire de Mathématiques, Université Paris-Sud 11, 91400 Orsay, France
3CEA, DSV, I2BM, NeuroSpin, 91191 Gif-sur-Yvette, France
4CEA, DSV, I2BM, INSERM U562, 91191 Gif-sur-Yvette, France
5SELECT Team, INRIA Saclay-Île-de-France, 91400, France

Received 23 December 2010; Revised 3 March 2011; Accepted 7 April 2011

Academic Editor: Kenji Suzuki

Copyright © 2011 Vincent Michel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. J. Friston, A. P. Holmes, K. J. Worsley, J. P. Poline, C. D. Frith, and R. S. J. Frackowiak, “Statistical parametric maps in functional imaging: a general linear approach,” Human Brain Mapping, vol. 2, no. 4, pp. 189–210, 1994. View at Google Scholar · View at Scopus
  2. S. Dehaene, G. Le Clec'H, L. Cohen, J. B. Poline, P. F. van de Moortele, and D. Le Bihan, “Inferring behavior from functional brain images,” Nature Neuroscience, vol. 1, no. 7, pp. 549–550, 1998. View at Google Scholar · View at Scopus
  3. D. D. Cox and R. L. Savoy, “Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex,” NeuroImage, vol. 19, no. 2, pp. 261–270, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. P. Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press, 2001.
  5. J. D. Haynes and G. Rees, “Predicting the stream of consciousness from activity in human visual cortex,” Current Biology, vol. 15, no. 14, pp. 1301–1307, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. T. M. Mitchell, R. Hutchinson, R. S. Niculescu et al., “Learning to decode cognitive states from brain images,” Machine Learning, vol. 57, no. 1-2, pp. 145–175, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. S. LaConte, S. Strother, V. Cherkassky, J. Anderson, and X. Hu, “Support vector machines for temporal classification of block design fMRI data,” NeuroImage, vol. 26, no. 2, pp. 317–329, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Mourão-Miranda, A. L. W. Bokde, C. Born, H. Hampel, and M. Stetter, “Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data,” NeuroImage, vol. 28, no. 4, pp. 980–995, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. S. J. Hanson and Y. O. Halchenko, “Brain reading using full brain support vector machines for object recognition: there is no face identification area,” Neural Computation, vol. 20, no. 2, pp. 486–503, 2008. View at Google Scholar
  10. O. Yamashita, M. A. Sato, T. Yoshioka, F. Tong, and Y. Kamitani, “Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns,” NeuroImage, vol. 42, no. 4, pp. 1414–1429, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Ryali, K. Supekar, D. A. Abrams, and V. Menon, “Sparse logistic regression for whole-brain classification of fMRI data,” NeuroImage, vol. 51, no. 2, pp. 752–764, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. F. De Martino, G. Valente, N. Staeren, J. Ashburner, R. Goebel, and E. Formisano, “Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns,” NeuroImage, vol. 43, no. 1, pp. 44–58, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Chu, Y. Ni, G. Tan, C. J. Saunders, and J. Ashburner, “Kernel regression for fMRI pattern prediction,” NeuroImage, vol. 56, no. 2, pp. 662–673, 2011. View at Publisher · View at Google Scholar
  15. H. Liu, M. Palatucci, and J. Zhang, “Blockwise coordinate descent procedures for the multi-task Lasso, with applications to neural semantic basis discovery,” in Proceedings of the 26th International Conference On Machine Learning (ICML '09), pp. 649–656, June 2009. View at Scopus
  16. M. K. Carroll, G. A. Cecchi, I. Rish, R. Garg, and A. R. Rao, “Prediction and interpretation of distributed neural activity with sparse models,” NeuroImage, vol. 44, no. 1, pp. 112–122, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. C. M. Bishop, Pattern Recognition and Machine Learning, Information Science and Statistics, Springer, Berlin, Germany, 1st edition, 2007.
  18. M. Tipping, The Relevance Vector Machine, Morgan Kaufmann, 2000.
  19. Y. Qi, T. P. Minka, R. W. Picard, and Z. Ghahramani, “Predictive automatic relevance determination by expectation propagation,” in Proceedings of the 21st International Conference on Machine Learning (ICML '04), ACM Press, 2004.
  20. D. Wipf and S. Nagarajan, “A new view of automatic relevance determination,” in Advances in Neural Information Processing Systems, vol. 20, pp. 1625–1632, MIT Press, 2008. View at Google Scholar
  21. Y. Ni, C. Chu, C. J. Saunders, and J. Ashburner, “Kernel methods for fmri pattern prediction,” in Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN '08), pp. 692–697, 2008.
  22. K. Uǧurbil, L. Toth, and D. S. Kim, “How accurate is magnetic resonance imaging of brain function?” Trends in Neurosciences, vol. 26, no. 2, pp. 108–114, 2003. View at Publisher · View at Google Scholar · View at Scopus
  23. K. Friston, C. Chu, J. Mourão-Miranda et al., “Bayesian decoding of brain images,” NeuroImage, vol. 39, no. 1, pp. 181–205, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Steck and T. S. Jaakkola, “On the dirichlet prior and bayesian regularization,” in Advances in Neural Information Processing Systems, vol. 15, pp. 697–704, 2002. View at Google Scholar
  25. E. I. George and R. E. McCulloch, “Variable selection via gibbs sampling,” Journal of the American Statistical Association, vol. 88, no. 423, pp. 881–889, 1993. View at Google Scholar
  26. S. Geman and D. Geman, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, Morgan Kaufmann, 1987.
  27. H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Statistical Society. Series B, vol. 67, no. 2, pp. 301–320, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Scopus
  29. G. Hughes, “On the mean accuracy of statistical pattern recognizers,” IEEE Transactions on Information Theory, vol. 14, no. 1, pp. 55–63, 1968. View at Google Scholar
  30. J. Friedman, T. Hastie, and R. Tibshirani, “Regularization paths for generalized linear models via coordinate descent,” Journal of Statistical Software, vol. 33, no. 1, pp. 1–22, 2010. View at Google Scholar · View at Scopus
  31. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
  32. scikit-learn, version 0.2, 2010, http://scikit-learn.sourceforge.net/.
  33. E. Eger, C. A. Kell, and A. Kleinschmidt, “Graded size sensitivity of object-exemplar-evoked activity patterns within human LOC subregions,” Journal of Neurophysiology, vol. 100, no. 4, pp. 2038–2047, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Chib and I. Jeliazkov, “Marginal Likelihood from the Metropolis-Hastings Output,” Journal of the American Statistical Association, vol. 96, no. 453, pp. 270–281, 2001. View at Google Scholar · View at Scopus
  35. J. H. Albert and S. Chib, “Bayesian analysis of binary and polychotomous response data,” Journal of the American Statistical Association, vol. 88, no. 422, pp. 669–679, 1993. View at Google Scholar
  36. R. E. McCulloch, N. G. Polson, and P. E. Rossi, “A Bayesian analysis of the multinomial probit model with fully identified parameters,” Journal of Econometrics, vol. 99, no. 1, pp. 173–193, 2000. View at Google Scholar · View at Scopus