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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 961257, 14 pages
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- R. S. J. Frackowiak, K. J. Friston, C. Frith et al., Human Brain Function, Academic Press, 2nd edition edition, 2003.
- M. Brett, W. Penny, and S. Kiebel, Introduction to Random Field Theory, Elsevier Press, 2004.
- D. R. Cox and H. D. Miller, The Theory of Stochastic Processes, Chapman and Hall, 1965.
- 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.
- 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.
- 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.
- 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.
- 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.
- C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
- V. N. Vapnik, The Nature of Statistical Learning Theory, vol. 8, Springer, 1995.
- 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.
- 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.
- 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.
- T. Joachims, Learning to Classify Text Using Support Vector Machines, Kluwer, 2002.
- 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.
- 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.
- S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, New York, NY, USA, 2004.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- F. Pereira and M. Botvinick, “Information mapping with pattern classifiers: a comparative study,” NeuroImage, vol. 56, no. 2, pp. 476–496, 2011.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000.
- 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.
- V. Schöpf, C. Windischberger, S. Robinson et al., “Model-free fMRI group analysis using FENICA,” NeuroImage, vol. 55, no. 1, pp. 185–193, 2011.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- R. E. Bellman, Adaptive Control Processes—A Guided Tour, Princeton University Press, Princeton, NJ, USA, 1961.
- 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.
- 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.
- T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 27, Springer, 2009.
- T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
- 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.
- 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.
- J. Ashburner and S. Klöppel, “Multivariate models of inter-subject anatomical variability,” NeuroImage, vol. 56, no. 2, pp. 422–439, 2011.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.