Information-Theoretic Based Feature Selection for Multi-Voxel Pattern Analysis of fMRI Data

  • Chun-An Chou
  • Kittipat “Bot” Kampa
  • Sonya H. Mehta
  • Rosalia F. Tungaraza
  • W. Art Chaovalitwongse
  • Thomas J. Grabowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7670)


Multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data is an emerging approach for probing the neural correlates of cognition. MVPA allows cognitive representations and processing to be modeled as distributed patterns of neural activity, which can be used to build a classification model to partition activity patterns according to stimulus conditions. In machine learning, MVPA is a very challenging classification problem because the number of voxels (features) greatly exceeds the number of data instances. Thus, there is a need to select informative voxels before building a classification model. We introduce a feature selection method based on mutual information (MI), which is used to quantify the statistical dependency between features and stimulus conditions. To evaluate the utility of our approach, we employed several linear classification algorithms on a publicly available fMRI data set that has been widely used to benchmark MVPA performance [1]. The computational results suggest that feature selection based on the MI ranking can drastically improve the classification accuracy. Additionally, high-ranked features provide meaningful insights into the functional-anatomic relationship of neural activity and the associated tasks.


Support Vector Machine Feature Selection Mutual Information fMRI Data Feature Ranking 
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.


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  1. 1.
    Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539), 2425–2430 (2001)CrossRefGoogle Scholar
  2. 2.
    Poldrack, R.A., Mumford, J.A., Nichols, T.E.: Handbook of functional MRI data analysis. Cambridge University Press (2011)Google Scholar
  3. 3.
    Shannon, C.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423, 623–656 (1948)Google Scholar
  4. 4.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  5. 5.
    Tsai, A., John, W., Fisher, I., Wible, C., William, M., Wells, I., Kim, J., Willsky, A.S.: Analysis of functional mri data using mutual information. In: Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 473–480 (1999)Google Scholar
  6. 6.
    Michel, V., Damon, C., Thirion, B.: Mutual information-based feature selection enhances fmri brain activity classification. In: IEEE International Symposium on Biomedical Imaging, pp. 592–595 (2008)Google Scholar
  7. 7.
    Gómez-Verdejo, V., Martínez-Ramón, M., Florensa-Vila, J., Oliviero, A.: Analysis of fmri time series with mutual information. Medical Image Analysis 16(2), 451–458 (2012)CrossRefGoogle Scholar
  8. 8.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual information based registration of medical images: a survey. IEEE Transactions on Medical Imaging 22, 986–1004 (2003)CrossRefGoogle Scholar
  9. 9.
    Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3(2), 185–205 (2005)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Tourassia, G.D., Frederick, E.D., Markey, M.K., Carey, E., Floyd, J.: Application of the mutual information criterion for feature selection in computer-aided diagnosis. Medical Physics 28(12), 2394–2402 (2001)CrossRefGoogle Scholar
  11. 11.
    Afshin-Pour, B., Soltanian-Zadeh, H., Hossein-Zadeh, G.A., Grady, C.L., Strother, S.C.: A mutual information-based metric for evaluation of fmri data-processing approaches. Human Brain Mapping 32(5), 699–715 (2011)CrossRefGoogle Scholar
  12. 12.
    Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X.: Learning to decode cognitive states from brain images. Machine Learning 57, 145–175 (2004)zbMATHCrossRefGoogle Scholar
  13. 13.
    Haynes, J.D., Rees, G.: Decoding mental states from brain activity in humans. Neuroscience 7, 523–534 (2006)Google Scholar
  14. 14.
    Mourão-Miranda, J., Bokde, A.L., Born, C., Hampel, H., Stetter, M.: Classifying brain states and determining the discriminating activation patterns: support vector machine on functional mri data. Neuroimage 28(4), 980–995 (2005)CrossRefGoogle Scholar
  15. 15.
    Mourão-Miranda, J., Reynaud, E., McGlone, F., Calvert, G., Brammer, M.: The impact of temporal compression and space selection on svm analysis of single-subject and multi-subject fmri data. Neuroimage 33(4), 1055–1065 (2006)CrossRefGoogle Scholar
  16. 16.
    Martino, F.D., Valente, G., Staeren, N., Ashburner, J., Goebel, R., Formisano, E.: Combining multivariate voxel selection and support vector machines for mapping and classification of fmri spatial patterns. NeuroImage 43, 44–58 (2008)CrossRefGoogle Scholar
  17. 17.
    Kuncheva, L.I., Rodréguez, J.J.: Classifier ensembles for fmri data analysis: An experiment. Magnetic Resonance Imaging 28, 583–593 (2010)CrossRefGoogle Scholar
  18. 18.
    Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of fmri data. RENDS in Cognitive Sciences 10(9), 424–430 (2006)CrossRefGoogle Scholar
  19. 19.
    Friston, K.J., Fletcher, P., Josephs, O., Holmes, A., Rugg, M.D., Turner, R.: Event-related fmri: characterizing differential responses. NeuroImage 7, 30–40 (1998)CrossRefGoogle Scholar
  20. 20.
    Pereira, F., Mitchell, T., Botvinick, M.: Machine learning classifiers and fmri: A tutorial overview. NeuroImage 45, 199–209 (2009)CrossRefGoogle Scholar
  21. 21.
    Mitchell, T.M.: Machine learning. McGraw Hill (1997)Google Scholar
  22. 22.
    Jordan, A.: On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in Neural Information Processing Systems 14, 841 (2002)Google Scholar
  23. 23.
    Hanson, S.J., Matsuka, T., Haxby, J.V.: Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001); revisited: is there a “face” area? NeuroImage 23, 156–166 (2004)CrossRefGoogle Scholar
  24. 24.
    O’Toole, A.J., Jiang, F., Abdi, H., Haxby, J.V.: Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience 17, 580–590 (2005)CrossRefGoogle Scholar
  25. 25.
    Mitchell, T.M., Shinkareva, S.V., Carlson, A., Chang, K.M., Malave, V.L., Mason, R.A., Just, M.A.: Predicting human brain activity associated with the meanings of nouns. Science 320, 1191–1195 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chun-An Chou
    • 1
    • 2
  • Kittipat “Bot” Kampa
    • 1
    • 2
  • Sonya H. Mehta
    • 1
    • 4
    • 5
  • Rosalia F. Tungaraza
    • 1
    • 5
  • W. Art Chaovalitwongse
    • 1
    • 2
    • 5
  • Thomas J. Grabowski
    • 1
    • 3
    • 5
  1. 1.Integrated Brain Imaging CenterUniversity of WashingtonSeattleUSA
  2. 2.Industrial & Systems EngineeringUniversity of WashingtonSeattleUSA
  3. 3.NeurologyUniversity of WashingtonSeattleUSA
  4. 4.PsychologyUniversity of WashingtonSeattleUSA
  5. 5.RadiologyUniversity of WashingtonSeattleUSA

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