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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 961257, 14 pages
http://dx.doi.org/10.1155/2012/961257
Review Article

Multivoxel Pattern Analysis for fMRI Data: A Review

1Laboratoire d'Informatique, Mathématique, Intelligence Artificielle et Reconnaissance de Formes (LIMIARF), Faculté des Sciences, Université Mohammed V-Agdal, 4 Avenue Ibn Battouta, BP 1014, Rabat, Morocco
2Institut de Neurosciences de la Timone (INT), UMR 7289 CNRS, and Aix Marseille Université, 27 boulevard Jean Moulin, 13385 Marseille, France
3Institut de Neurosciences des Systèmes (INS), UMR 1106 INSERM, and Faculté de Médecine, Aix Marseille Université, 27 boulevard Jean Moulin, 13005 Marseille, France

Received 10 July 2012; Revised 27 September 2012; Accepted 25 October 2012

Academic Editor: Reinoud Maex

Copyright © 2012 Abdelhak Mahmoudi 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. S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation,” Proceedings of the National Academy of Sciences of the United States of America, vol. 87, no. 24, pp. 9868–9872, 1990. View at Publisher · View at Google Scholar · View at Scopus
  2. K. K. Kwong, J. W. Belliveau, D. A. Chesler et al., “Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation,” Proceedings of the National Academy of Sciences of the United States of America, vol. 89, no. 12, pp. 5675–5679, 1992. View at Publisher · View at Google Scholar · View at Scopus
  3. N. K. Logothetis, J. Pauls, M. Augath, T. Trinath, and A. Oeltermann, “Neurophysiological investigation of the basis of the fMRI signal,” Nature, vol. 412, no. 6843, pp. 150–157, 2001. View at Publisher · View at Google Scholar · View at Scopus
  4. P. Jezzard, M. P. Matthews, and M. S. Smith, “Functional MRI: an introduction to methods,” Journal of Magnetic Resonance Imaging, vol. 17, no. 3, pp. 383–383, 2003.
  5. K. J. Friston, C. D. Frith, P. F. Liddle, and R. S. J. Frackowiak, “Comparing functional (PET) images: the assessment of significant change,” Journal of Cerebral Blood Flow and Metabolism, vol. 11, no. 4, pp. 690–699, 1991. View at Scopus
  6. A. R. McIntosh, C. L. Grady, J. V. Haxby, J. M. Maisog, B. Horwitz, and C. M. Clark, “Within-subject transformations of PET regional cerebral blood ow data: ANCOVA, ratio, and z-score adjustments on empirical data,” Human Brain Mapping, vol. 4, no. 2, pp. 93–102, 1996.
  7. K. J. Friston, A. P. Holmes, C. J. Price, C. Büchel, and K. J. Worsley, “Multisubject fMRI studies and conjunction analyses,” NeuroImage, vol. 10, no. 4, pp. 385–396, 1999. View at Publisher · View at Google Scholar · View at Scopus
  8. M. J. McKeown, S. Makeig, G. G. Brown et al., “Analysis of fMRI data by blind separation into independent spatial components,” Human Brain Mapping, vol. 6, no. 3, pp. 160–188, 1998.
  9. U. Kjems, L. K. Hansen, J. Anderson et al., “The quantitative evaluation of functional neuroimaging experiments: mutual information learning curves,” NeuroImage, vol. 15, no. 4, pp. 772–786, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. R. S. J. Frackowiak, K. J. Friston, C. Frith et al., Human Brain Function, Academic Press, 2nd edition edition, 2003.
  11. M. Brett, W. Penny, and S. Kiebel, Introduction to Random Field Theory, Elsevier Press, 2004.
  12. D. R. Cox and H. D. Miller, The Theory of Stochastic Processes, Chapman and Hall, 1965.
  13. K. A. Norman, S. M. Polyn, G. J. Detre, and J. V. Haxby, “Beyond mind-reading: multi-voxel pattern analysis of fMRI data,” Trends in Cognitive Sciences, vol. 10, no. 9, pp. 424–430, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. 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
  15. J. V. Haxby, M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten, and P. Pietrini, “Distributed and overlapping representations of faces and objects in ventral temporal cortex,” Science, vol. 293, no. 5539, pp. 2425–2430, 2001. View at Publisher · View at Google Scholar · View at Scopus
  16. P. E. Downing, A. J. Wiggett, and M. V. Peelen, “Functional magnetic resonance imaging investigation of overlapping lateral occipitotemporal activations using multi-voxel pattern analysis,” Journal of Neuroscience, vol. 27, no. 1, pp. 226–233, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Davatzikos, K. Ruparel, Y. Fan et al., “Classifying spatial patterns of brain activity with machine learning methods: application to lie detection,” NeuroImage, vol. 28, no. 3, pp. 663–668, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. 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
  19. V. N. Vapnik, The Nature of Statistical Learning Theory, vol. 8, Springer, 1995.
  20. M. Timothy, D. Alok, V. Svyatoslav et al., “Support Vector Machine classification and characterization of age-related reorganization of functional brain networks,” NeuroImage, vol. 60, no. 1, pp. 601–613, 2012.
  21. E. Formisano, F. De Martino, and G. Valente, “Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning,” Magnetic Resonance Imaging, vol. 26, no. 7, pp. 921–934, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. 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 Publisher · View at Google Scholar · View at Scopus
  23. T. Joachims, Learning to Classify Text Using Support Vector Machines, Kluwer, 2002.
  24. C. C. Chang and C. J. Lin, “LIBSVM: a library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Hanke, Y. O. Halchenko, P. B. Sederberg, S. J. Hanson, J. V. Haxby, and S. Pollmann, “PyMVPA: a python toolbox for multivariate pattern analysis of fMRI data,” Neuroinformatics, vol. 7, no. 1, pp. 37–53, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, New York, NY, USA, 2004.
  27. 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
  28. K. H. Brodersen, T. M. Schofield, A. P. Leff et al., “Generative embedding for Model-Based classification of FMRI data,” PLoS Computational Biology, vol. 7, no. 6, Article ID e1002079, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. S. P. Ku, A. Gretton, J. Macke, and N. K. Logothetis, “Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys,” Magnetic Resonance Imaging, vol. 26, no. 7, pp. 1007–1014, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. M. Misaki, Y. Kim, P. A. Bandettini, and N. Kriegeskorte, “Comparison of multivariate classifiers and response normalizations for pattern-information fMRI,” NeuroImage, vol. 53, no. 1, pp. 103–118, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. 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
  32. 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
  33. T. Schmah, G. Yourganov, R. S. Zemel, G. E. Hinton, S. L. Small, and S. C. Strother, “Comparing classification methods for longitudinal fMRI studies,” Neural Computation, vol. 22, no. 11, pp. 2729–2762, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. F. Pereira and M. Botvinick, “Information mapping with pattern classifiers: a comparative study,” NeuroImage, vol. 56, no. 2, pp. 476–496, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. Y. Kamitani and Y. Sawahata, “Spatial smoothing hurts localization but not information: pitfalls for brain mappers,” NeuroImage, vol. 49, no. 3, pp. 1949–1952, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. H. P. Op de Beeck, “Against hyperacuity in brain reading: spatial smoothing does not hurt multivariate fMRI analyses?” NeuroImage, vol. 49, no. 3, pp. 1943–1948, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. J. D. Swisher, J. C. Gatenby, J. C. Gore et al., “Multiscale pattern analysis of orientation-selective activity in the primary visual cortex,” Journal of Neuroscience, vol. 30, no. 1, pp. 325–330, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. H. Shen, L. Wang, Y. Liu, and D. Hu, “Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI,” NeuroImage, vol. 49, no. 4, pp. 3110–3121, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. R. Sayres, D. Ress, and K. G. Spector, “Identifying distributed object representations in human extrastriate visual cortex,” in Proceedings of the Neural Information Processing Systems (NIPS '05), 2005.
  40. M. B. Åberg and J. Wessberg, “An evolutionary approach to the identification of informative voxel clusters for brain state discrimination,” IEEE Journal on Selected Topics in Signal Processing, vol. 2, no. 6, pp. 919–928, 2008. View at Publisher · View at Google Scholar · View at Scopus
  41. S. J. Kiebel and K. J. Friston, “Statistical parametric mapping for event-related potentials: I. Generic considerations,” NeuroImage, vol. 22, no. 2, pp. 492–502, 2004. View at Publisher · View at Google Scholar · View at Scopus
  42. A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000. View at Publisher · View at Google Scholar · View at Scopus
  43. D. B. Rowe and R. G. Hoffmann, “Multivariate statistical analysis in fMRI,” IEEE Engineering in Medicine and Biology Magazine, vol. 25, no. 2, pp. 60–64, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. V. Schöpf, C. Windischberger, S. Robinson et al., “Model-free fMRI group analysis using FENICA,” NeuroImage, vol. 55, no. 1, pp. 185–193, 2011. View at Publisher · View at Google Scholar · View at Scopus
  45. S. A. R. B. Rombouts, J. S. Damoiseaux, R. Goekoop et al., “Model-free group analysis shows altered BOLD FMRI networks in dementia,” Human Brain Mapping, vol. 30, no. 1, pp. 256–266, 2009. View at Publisher · View at Google Scholar · View at Scopus
  46. C. Chu, A. -L. Hsu, K. -H. Chou, P. Bandettini, and C. Lin, “Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images,” NeuroImage, vol. 60, no. 1, pp. 59–70, 2011.
  47. J. D. Haynes and G. Rees, “Predicting the orientation of invisible stimuli from activity in human primary visual cortex,” Nature Neuroscience, vol. 8, no. 5, pp. 686–691, 2005. View at Publisher · View at Google Scholar · View at Scopus
  48. Y. Kamitani and F. Tong, “Decoding the visual and subjective contents of the human brain,” Nature Neuroscience, vol. 8, no. 5, pp. 679–685, 2005. View at Publisher · View at Google Scholar · View at Scopus
  49. N. Kriegeskorte, R. Goebel, and P. Bandettini, “Information-based functional brain mapping,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 10, pp. 3863–3868, 2006. View at Publisher · View at Google Scholar · View at Scopus
  50. M. Björnsdotter, K. Rylander, and J. Wessberg, “A Monte Carlo method for locally multivariate brain mapping,” NeuroImage, vol. 56, no. 2, pp. 508–516, 2011. View at Publisher · View at Google Scholar · View at Scopus
  51. R. E. Bellman, Adaptive Control Processes—A Guided Tour, Princeton University Press, Princeton, NJ, USA, 1961.
  52. R. Kohavi, “. A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of the International Joint Conference on Artificial Intelligence, vol. 14, pp. 1137–1143, Citeseer, 1995.
  53. S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, “Introduction to machine learning for brain imaging,” NeuroImage, vol. 56, no. 2, pp. 387–399, 2011. View at Publisher · View at Google Scholar · View at Scopus
  54. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 27, Springer, 2009.
  55. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006. View at Publisher · View at Google Scholar · View at Scopus
  56. S. M. Smith and T. E. Nichols, “Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference,” NeuroImage, vol. 44, no. 1, pp. 83–98, 2009. View at Publisher · View at Google Scholar · View at Scopus
  57. L. Yan, R. Dodier, M. C. Mozer, and R. Wolniewicz, “Optimizing classifier performance via an approximation to the Wilcoxon-Mann-Whitney Statistic,” in Proceedings of the 20th International Conference on Machine Learning (ICML '03), vol. 20, p. 848, AAAI Press, August 2003. View at Scopus
  58. J. Ashburner and S. Klöppel, “Multivariate models of inter-subject anatomical variability,” NeuroImage, vol. 56, no. 2, pp. 422–439, 2011. View at Publisher · View at Google Scholar · View at Scopus
  59. A. P. Holmes, R. C. Blair, J. D. G. Watson, and I. Ford, “Nonparametric analysis of statistic images from functional mapping experiments,” Journal of Cerebral Blood Flow and Metabolism, vol. 16, no. 1, pp. 7–22, 1996. View at Scopus
  60. T. E. Nichols and A. P. Holmes, “Nonparametric permutation tests for functional neuroimaging: a primer with examples,” Human Brain Mapping, vol. 15, no. 1, pp. 1–25, 2002. View at Publisher · View at Google Scholar · View at Scopus
  61. A. Eklund, M. Andersson, and H. Knutsson, “Fast random permutation tests enable objective evaluation of methods for single subject fMRI analysis,” International Journal of Biomedical Imaging, vol. 2011, Article ID 627947, 15 pages, 2011.
  62. P. Golland and B. Fischl, “Permutation tests for classification: towards statistical significance in image-based studies,” in Proceedings of the Conference of Information Processing in Medical Imaging, pp. 330–341, August 2003.
  63. P. Golland, F. Liang, S. Mukherjee, and D. Panchenko, “Permutation tests for classification,” in Proceedings of the 18th Annual Conference on Learning Theory (COLT '05), pp. 501–515, August 2005.
  64. A. M. Smith, B. K. Lewis, U. E. Ruttimann et al., “Investigation of low frequency drift in fMRI signal,” NeuroImage, vol. 9, no. 5, pp. 526–533, 1999. View at Publisher · View at Google Scholar · View at Scopus