Abstract
A multi voxel pattern analysis classification framework suitable for neuroimaging data is introduced. The framework includes a novel feature extraction method that uses local modeling based on domain specific knowledge, and therefore, can produce better whole-brain global classification performance using a smaller number of features. In particular, the method includes spherical searchlights in combination with local SVM modeling. The performance of the framework is demonstrated on a challenging fMRI classification problem, and is found to be superior to the performance of state-of-the-art feature selection methods used in neuroimaging.
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References
Bellman, R.E.: Adaptive control processes - A guided tour. Princeton University Press, Princeton (1961)
Cavanna, A.E., Trimble, M.R.: The precuneus: a review of its functional anatomy and behavioural correlates. Brain: A Journal of Neurology 129(Pt 3), 564–583 (2006), http://www.ncbi.nlm.nih.gov/pubmed/16399806
Detre, G.J., Polyn, S.M., Moore, C.D., Natu, V.S., Singer, B.D., Cohen, J.D., Haxby, J.V., Norman, K.A.: The Multi-Voxel Pattern Analysis (MVPA) toolbox a Poster presented at the Annual Meeting of the Organization for Human Brain Mapping (2006)
Goebel, R., Esposito, F., Formisano, E.: Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Human Brain Mapping 27(5), 392–401 (2006), http://www.ncbi.nlm.nih.gov/pubmed/16596654
Guyon, I., Elisseefi, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3(7-8), 1157–1182 (2003), http://dl.acm.org/citation.cfm?id=944968
Kriegeskorte, N., Goebel, R., Bandettini, P.: Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America 103(10), 3863–3868 (2006), http://www.ncbi.nlm.nih.gov/pubmed/16537458
Mur, M., Bandettini, P., Kriegeskorte, N.: Revealing representational content with pattern-information fMRI–an introductory guide. Social Cognitive and Affective Neuroscience 4(1), 101 (2009), http://www.pubmedcentral.nih.gov/2656880 http://scan.oxfordjournals.org/content/4/1/101.short
Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences 10(9), 424–430 (2006), http://www.ncbi.nlm.nih.gov/pubmed/16899397
Pereira, F., Botvinick, M.: Information mapping with pattern classifiers: A comparative study. NeuroImage (May 2010), http://www.ncbi.nlm.nih.gov/pubmed/20488249
Pereira, F., Botvinick, M.: Classification of Functional Magnetic Resonance Imaging Data using Informative Pattern Features Categories and Subject Descriptors. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 940–946. ACM, New York (2011)
Pereira, F., Mitchell, T., Botvinick, M.: Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45(1 suppl.) S199–S209 (2009), http://www.ncbi.nlm.nih.gov/pubmed/19070668
Vapnik, V.N.: Statistical learning theory, 1st edn. Wiley (1998)
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Jamshy, S., Perez, O., Yeshurun, Y., Hendler, T., Intrator, N. (2012). Searchlight Based Feature Extraction. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_3
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DOI: https://doi.org/10.1007/978-3-642-34713-9_3
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