Advertisement

Use of Pattern-Information Analysis in Vision Science: A Pragmatic Examination

  • Mathieu J. Ruiz
  • Jean-Michel Hupé
  • Michel Dojat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7588)

Abstract

MultiVoxel Pattern Analysis (MVPA) is presented as a successful alternative to the General Linear Model (GLM) for fMRI data analysis. We report different experiments using MVPA to master several key parameters. We found that 1) different feature selections provide similar classification accuracies with different interpretation depending on the underlying hypotheses, 2) paradigms should be created to maximize both Signal to Noise Ratio (SNR) and number of examples and 3) smoothing leads to opposite effects on classification depending on the spatial scale at which information is encoded and should be used with extreme caution.

Keywords

Machine Learning SVM Neurosciences Brain 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kamitani, Y., Tong, F.: Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8(5), 679–685 (2005)CrossRefGoogle Scholar
  2. 2.
    Gerardin, P., Kourtzi, Z., Mamassian, P.: Prior knowledge of illumination for 3D perception in the human brain. Proc. Natl. Acad. Sci. U.S.A. 107(37), 16309–16314 (2010)CrossRefGoogle Scholar
  3. 3.
    Reddy, L., Tsuchiya, N., Serre, T.: Reading the mind’s eye: decoding category information during mental imagery. Neuroimage 50(2), 818–825 (2010)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Ethofer, T., Van De Ville, D., Scherer, K., Vuilleumier, P.: Decoding of emotional information in voice-sensitive cortices. Curr. Biol. 19(12), 1028–1033 (2009)CrossRefGoogle Scholar
  6. 6.
    Brouwer, G.J., Heeger, D.J.: Decoding and reconstructing color from responses in human visual cortex. J. Neurosci. 29(44), 13992–14003 (2009)CrossRefGoogle Scholar
  7. 7.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetGoogle Scholar
  8. 8.
    Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1-3), 389–422 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    De Martino, F., 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(1), 44–58 (2008)CrossRefGoogle Scholar
  10. 10.
    Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10(9), 424–430 (2006)CrossRefGoogle Scholar
  11. 11.
    Mumford, J.A., Turner, B.O., Ashby, F.G., Poldrack, R.A.: Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. Neuroimage 59(3), 2636–2643 (2012)CrossRefGoogle Scholar
  12. 12.
    Coutanche, M.N., Thompson-Schill, S.L.: The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs. Neuroimage 61(4), 1113–1119 (2012)CrossRefGoogle Scholar
  13. 13.
    LaConte, S., Strother, S., Cherkassky, V., Anderson, J., Hu, X.: Support vector machines for temporal classification of block design fMRI data. Neuroimage 26(2), 317–329 (2005)CrossRefGoogle Scholar
  14. 14.
    Etzel, J.A., Valchev, N., Keysers, C.: The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machines. Neuroimage 54(2), 1159–1167 (2011)CrossRefGoogle Scholar
  15. 15.
    Freeman, J., Brouwer, G.J., Heeger, D.J., Merriam, E.P.: Orientation decoding depends on maps, not columns. J. Neurosci. 31(13), 4792–4804 (2011)CrossRefGoogle Scholar
  16. 16.
    Kriegeskorte, N., Cusack, R., Bandettini, P.: How does an fMRI voxel sample the neuronal activity pattern: compact-kernel or complex spatiotemporal filter? Neuroimage 49(3), 1965–1976 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mathieu J. Ruiz
    • 1
    • 2
  • Jean-Michel Hupé
    • 2
  • Michel Dojat
    • 1
  1. 1.GIN - INSERM U836 & Université J. FourierLa TroncheFrance
  2. 2.CerCo - CNRS UMR 5549 & Université de ToulouseToulouseFrance

Personalised recommendations