Abstract
Functional Magnetic Resonance Imaging (fMRI) provides a unique opportunity to study brain functional architecture, while being minimally invasive. Reverse inference, a.k.a. decoding, is a recent statistical analysis approach that has been used with success for deciphering activity patterns that are thought to fit the neuroscientific concept of population coding. Decoding relies on the selection of brain regions in which the observed activity is predictive of certain cognitive tasks. The accuracy of such a procedure is quantified by the prediction of the behavioral variable of interest – the target. In this paper, we discuss the optimality of decoding methods in two different settings, namely intra- and inter-subject kind of decoding. While inter-subject prediction aims at finding predictive regions that are stable across subjects, it is plagued by the additional inter-subject variability (lack of voxel-to-voxel correspondence), so that the best suited prediction algorithms used in reverse inference may not be the same in both cases. We benchmark different prediction algorithms in both intra- and inter-subjects analysis, and we show that using spatial regularization improves reverse inference in the challenging context of inter-subject prediction. Moreover, we also study the different maps of weights, and show that methods with similar accuracy may yield maps with very different spatial layout of the predictive regions.
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Dehaene, S., Le Clec’H, G., Cohen, L., Poline, J.-B., van de Moortele, P.-F., Le Bihan, D.: Inferring behavior from functional brain images. Nature Neuroscience 1, 549 (1998)
Cox, D.D., Savoy, R.L.: Functional magnetic resonance imaging (fMRI) ”brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 19, 261–270 (2003)
Tucholka, A.: Prise en compte de l’anatomie cérébrale individuelle dans les études d’IRM fonctionnelle. Ph.D. dissertation, Université Paris-Sud (2010)
Tahmasebi, A.M.: Quantification of Inter-subject Variability in Human Brain and Its Impact on Analysis of fMRI Data. Ph.D. dissertation, Queen’s University (2010)
Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33(1) (2010)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273 (1995)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. B 67, 301 (2005)
MacKay, D.J.C.: Bayesian interpolation. Neural Comput. 4(3), 415–447 (1992)
Neal, R.M.: Bayesian Learning for Neural Networks. Lecture Notes in Statistics, 1st edn. Springer (1996)
Michel, V., Eger, E., Keribin, C., Thirion, B.: Multiclass Sparse Bayesian Regression for fMRI-Based Prediction. International Journal of Biomedical Imaging 2011 (April 2011)
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)
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 (April 2011)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D (January 1992)
Michel, V., Gramfort, A., Varoquaux, G., Eger, E., Thirion, B.: Total variation regularization for fMRI-based prediction of behaviour. IEEE Transactions on Medical Imaging 30(7), 1328–1340 (2011)
Eger, E., Kell, C., Kleinschmidt, A.: Graded size sensitivity of object exemplar evoked activity patterns in human loc subregions. J. Neurophysiol. 100(4), 2038–2047 (2008)
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Michel, V., Gramfort, A., Eger, E., Varoquaux, G., Thirion, B. (2012). A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding. 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_1
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DOI: https://doi.org/10.1007/978-3-642-34713-9_1
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