Abstract
Classification of medical images in multi-subjects settings is a difficult challenge due to the variability that exists between individuals. Here we introduce a new graph-based framework designed to deal with inter-subject functional variability present in fMRI data. A graphical model is constructed to encode the functional, geometric and structural properties of local activation patterns. We then design a specific graph kernel, allowing to conduct SVM classification in graph space. Experiments conducted in an inter-subject classification task of patterns recorded in the auditory cortex show that it is the only approach to perform above chance level, among a wide range of tested methods.
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References
Auzias, G., Lefèvre, J., Le Troter, A., Fischer, C., Perrot, M., Régis, J., Coulon, O.: Model-Driven Harmonic Parameterization of the Cortical Surface. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS (LNAI), vol. 6892, pp. 310–317. Springer, Heidelberg (2011)
Caputo, B., Sim, K., Furesjo, F., Smola, A.: Appearance-based object recognition using svms: which kernel should i use? In: Proc. of NIPS Workshop on Stat. Methods for Computational Experiments in Visual Processing and Computer Vision (2002)
Clithero, J.A., Smith, D.V., Carter, R.M., Huettel, S.A.: Within- and cross-participant classifiers reveal different neural coding of information. NeuroImage 56(2), 699–708 (2011); Multivariate Decoding and Brain Reading
Van Essen, D.C., Dierker, D.L.: Surface-Based and probabilistic atlases of primate cerebral cortex. Neuron 56(2), 209–225 (2007)
Flandin, G., Kherif, F., Pennec, X., Malandain, G., Ayache, N., Poline, J.-B.: Improved Detection Sensitivity in Functional MRI Data Using a Brain Parcelling Technique. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002, Part I. LNCS, vol. 2488, pp. 467–474. Springer, Heidelberg (2002)
Gärtner, T.: Exponential and geometric kernels for graphs. In: NIPS Workshop on Unreal Data: Principles of Modeling Nonvectorial Data (2002)
Haussler, D.: Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, UC Santa Cruz (1999)
Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539), 2425–2430 (2001)
Haxby, J.V., Guntupalli, J.S., Connolly, A.C., Halchenko, Y.O., Conroy, B.R., Gobbini, M.I., Hanke, M., Ramadge, P.J.: A common, High-Dimensional model of the representational space in human ventral temporal cortex. Neuron 72(2), 404–416 (2011)
Humphries, C., Liebenthal, E., Binder, J.R.: Tonotopic organization of human auditory cortex. NeuroImage 50(3), 1202–1211 (2010)
Mahé, P., Vert, J.P.: Graph kernels based on tree patterns for molecules. Machine Learning 75, 3–35 (2009)
Michel, V., Gramfort, A., Varoquaux, G., Eger, E., Keribin, C., Thirion, B.: A supervised clustering approach for fMRI-based inference of brain states. Pattern Recognition 45(6), 2041–2049 (2012)
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)
Pavlidis, T.: Structural pattern recognition. Springer (1977)
Poldrack, R.A., Halchenko, Y.O., Hanson, S.J.: Decoding the Large-Scale structure of brain function by classifying mental states across individuals. Psychological Science 20(11), 1364–1372 (2009)
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Takerkart, S., Auzias, G., Thirion, B., Schön, D., Ralaivola, L. (2012). Graph-Based Inter-subject Classification of Local fMRI Patterns. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_23
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DOI: https://doi.org/10.1007/978-3-642-35428-1_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35427-4
Online ISBN: 978-3-642-35428-1
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