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28 nips-2012-A systematic approach to extracting semantic information from functional MRI data


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Author: Francisco Pereira, Matthew Botvinick

Abstract: This paper introduces a novel classification method for functional magnetic resonance imaging datasets with tens of classes. The method is designed to make predictions using information from as many brain locations as possible, instead of resorting to feature selection, and does this by decomposing the pattern of brain activation into differently informative sub-regions. We provide results over a complex semantic processing dataset that show that the method is competitive with state-of-the-art feature selection and also suggest how the method may be used to perform group or exploratory analyses of complex class structure. 1


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[1] VD Blondel, JL Guillaume, R Lambiotte, and E Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, (10):1–12, 2008.

[2] Melissa K Carroll, Guillermo a Cecchi, Irina Rish, Rahul Garg, and a Ravishankar Rao. Prediction and interpretation of distributed neural activity with sparse models. NeuroImage, 44(1):112–22, January 2009.

[3] C.C. Chang and C.J. Lin. LIBSVM: a library for support vector machines. Technical report, 2001.

[4] Karl J Friston, John Ashburner, Stefan J Kiebel, Thomas E Nichols, and W D Penny. Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press, 2006.

[5] Christopher R Genovese, Nicole a Lazar, and Thomas Nichols. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15(4):870–8, 2002.

[6] Stephen Jos´ Hanson, Toshihiko Matsuka, and James V Haxby. Combinatorial codes in ventral temporal e lobe for object recognition: Haxby (2001) revisited: is there a ”face” area? NeuroImage, 23(1):156–66, 2004.

[7] Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference and prediction. Springer-Verlag, 2001.

[8] J. Haynes and G. Rees. Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7(7):523–34, 2006.

[9] Marcel Adam Just, Vladimir L Cherkassky, Sandesh Aryal, and Tom M Mitchell. A neurosemantic theory of concrete noun representation based on the underlying brain codes. PloS one, 5(1):e8622, 2010. 8

[10] N Kriegeskorte, R. Goebel, and P. Bandettini. Information-based functional brain mapping. Proceedings of the National Academy of Sciences, 103(10):3863, 2006.

[11] John Langford. Tutorial on Practical Prediction Theory for Classification. Journal of Machine Learning Research, 6:273–306, 2005.

[12] T. M. Mitchell, R. Hutchinson, R. S. Niculescu, F. Pereira, X. Wang, M. Just, and S. Newman. Learning to Decode Cognitive States from Brain Images. Machine Learning, 57(1/2):145–175, October 2004.

[13] T. M. Mitchell, S. V. Shinkareva, A. Carlson, K. Chang, V. L. Malave, R. A. Mason, and M. A. Just. Predicting human brain activity associated with the meanings of nouns. Science, 320(5880):1191–5, 2008.

[14] 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, 10(9):424–30, 2006.

[15] F Pereira and M Botvinick. Classification of functional magnetic resonance imaging data using informative pattern features. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11, page 940, 2011.

[16] F. Pereira and M. Botvinick. Information mapping with pattern classifiers: a comparative study. NeuroImage, 56(2):835–850, 2011.

[17] Peter Mondrup Rasmussen, Kristoffer Hougaard Madsen, Torben Ellegaard Lund, and Lars Kai Hansen. Visualization of nonlinear kernel models in neuroimaging by sensitivity maps. NeuroImage, 55(3):1120– 31, April 2011.

[18] Juliane Sch¨ fer and Korbinian Strimmer. A shrinkage approach to large-scale covariance matrix estimaa tion and implications for functional genomics. Statistical applications in genetics and molecular biology, 4:Article32, January 2005.

[19] N Tzourio-Mazoyer, B Landeau, D Papathanassiou, F Crivello, O Etard, N Delcroix, B Mazoyer, and M Joliot. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1):273–89, 2002. 9