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A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding

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Machine Learning and Interpretation in Neuroimaging

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7263))

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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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References

  1. 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)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. Tucholka, A.: Prise en compte de l’anatomie cérébrale individuelle dans les études d’IRM fonctionnelle. Ph.D. dissertation, Université Paris-Sud (2010)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33(1) (2010)

    Google Scholar 

  6. 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

  7. 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)

    MathSciNet  MATH  Google Scholar 

  8. Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273 (1995)

    MATH  Google Scholar 

  9. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. B 67, 301 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  10. MacKay, D.J.C.: Bayesian interpolation. Neural Comput. 4(3), 415–447 (1992)

    Article  MATH  Google Scholar 

  11. Neal, R.M.: Bayesian Learning for Neural Networks. Lecture Notes in Statistics, 1st edn. Springer (1996)

    Google Scholar 

  12. Michel, V., Eger, E., Keribin, C., Thirion, B.: Multiclass Sparse Bayesian Regression for fMRI-Based Prediction. International Journal of Biomedical Imaging 2011 (April 2011)

    Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. 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)

    Google Scholar 

  15. Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D (January 1992)

    Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34712-2

  • Online ISBN: 978-3-642-34713-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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