Multivoxel Pattern Analysis Using Information-Preserving EMD

  • Zareen Mehboob
  • Hujun Yin
  • Sophie M. Wuerger
  • Laura M. Parkes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7435)

Abstract

This paper presents a quantitative analysis on fMRI data using the information-preserving mode decomposition. Multivoxel patterns in fMRI responses in a cognitive experiment were analyzed for spatial selectivity to color perceptions of neurons in the Lateral Geniculate Nucleus (LGN) and the primary visual cortex (V1). The performance of the new method is tested and evaluated in a case study and the results are compared with the previous findings on the same dataset. While conforming to the previous study, the new results have shown improved classification of patterns for unique hues in V1.

Keywords

Mutual Information Empirical Mode Decomposition Lateral Geniculate Nucleus Primary Visual Cortex Color Stimulus 
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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References

  1. 1.
    Mehboob, Z., Yin, H.: Information quantification of empirical mode decomposition and applications to field potentials. International Journal of Neural Systems 21(1), 49–63 (2011)CrossRefGoogle Scholar
  2. 2.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. Roy. Soc. Lond. A 454, 903–1005 (1998)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Sweeney-Reed, C.M., Nasuto, S.J.: A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition. Journal of Computational Neuroscience 23, 79–111 (2007)CrossRefGoogle Scholar
  4. 4.
    Abbot, L.F., Dayan, P.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press (2001)Google Scholar
  5. 5.
    Parkes, L.M., Marsman, J.C., Oxley, D.C., Goulermas, J.Y., Wuerger, S.M.: Multivoxel fMRI analysis of color tuning in human primary visual cortex. Journal of Vision 9(1), 1–13 (2009)CrossRefGoogle Scholar
  6. 6.
    Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Aschouten, J.L., Pietrini, P.: Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539), 2425–2430 (2001)CrossRefGoogle Scholar
  7. 7.
    De Valois, R.L., De Valois, K.K., Mahon, L.E.: Contribution of S opponent cells to color appearance. Proceedings of the National Academy of Sciences of the United States of America 97, 512–517 (2000)CrossRefGoogle Scholar
  8. 8.
    Wuerger, S.M., Atkinson, P., Cropper, S.: The cone inputs to the unique-hue mechanisms. Vision Research 45(25-26), 3210–3223 (2005)CrossRefGoogle Scholar
  9. 9.
    Horwitz, G.D., Chichilnisky, E.J., Albright, T.D.: Cone inputs to simple and complex cells in V1 of awake macaque. Journal of Neurophysiology 97, 3070–3081 (2007)CrossRefGoogle Scholar
  10. 10.
    Brouwer, G.J., Heeger, D.J.: Decoding and reconstructing color from responses in human visual cortex. Journal of Neuroscience 29(44), 13992–14003 (2009)CrossRefGoogle Scholar
  11. 11.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New York (2000)Google Scholar
  12. 12.
    De Martino, F., Gentile, F., Esposito, F., Balsi, M., Di Salle, F., Goebel, R.: Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers. Neuroimage 34(1), 177–194 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zareen Mehboob
    • 1
    • 3
  • Hujun Yin
    • 1
  • Sophie M. Wuerger
    • 4
  • Laura M. Parkes
    • 2
  1. 1.School of Electrical and Electronic EngineeringThe University of ManchesterUK
  2. 2.Imaging Sciences, School of MedicineThe University of ManchesterUK
  3. 3.Department of Computer ScienceCOMSATS Institute of Information TechnologyPK
  4. 4.School of PsychologyUniversity of LiverpoolUK

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