Abstract
We introduce a new unsupervised fMRI analysis method based on Kernel Canonical Correlation Analysis which differs from the class of supervised learning methods that are increasingly being employed in fMRI data analysis. Whereas SVM associates properties of the imaging data with simple specific categorical labels, KCCA replaces these simple labels with a label vector for each stimulus containing details of the features of that stimulus. We have compared KCCA and SVM analyses of an fMRI data set involving responses to emotionally salient stimuli. This involved first training the algorithm ( SVM, KCCA) on a subset of fMRI data and the corresponding labels/label vectors, then testing the algorithms on data withheld from the original training phase. The classification accuracies of SVM and KCCA proved to be very similar. However, the most important result arising from this study is that KCCA in able in part to extract many of the brain regions that SVM identifies as the most important in task discrimination blind to the categorical task labels.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Carlson, T.A., Schrater, P., He, S.: Patterns of activity in the categorical representations of objects. Journal of Cognitive Neuroscience 15, 704–717 (2003)
Wang, X., Hutchinson, R., Mitchell, T.M.: Training fmri classifiers to detect cognitive states across multiple human subjects. In: Proceedings of the 2003 Conference on Neural Information Processing Systems (2003)
Mitchell, T., Hutchinson, R., Niculescu, R., Pereira, F., Wang, X., Just, M., Newman, S.: Learning to decode cognitive states from brain images. Machine Learning 1-2, 145–175 (2004)
LaConte, S., Strother, S., Cherkassky, V., Anderson, J., Hu, X.: Support vector machines for temporal classification of block design fmri data. NeuroImage 26, 317–329 (2005)
Mourao-Miranda, J., Bokde, A.L.W., Born, C., Hampel, H., Stetter, S.: Classifying brain states and determining the discriminating activation patterns: support vector machine on functional mri data. NeuroImage 28, 980–995 (2005)
Haynes, J.D., Rees, G.: Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature Neuroscience 8, 686–691 (2005)
Davatzikos, C., Ruparel, K., Fan, Y., Shen, D.G., Acharyya, M., Loughead, J.W., Gur, R.C., Langleben, D.D.: Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection. NeuroImage 28, 663–668 (2005)
Kriegeskorte, N., Goebel, R., Bandettini, P.: Information-based functional brain mapping. PANAS 103, 3863–3868 (2006)
Mourao-Miranda, J., Reynaud, E., McGlone, F., Calvert, G., Brammer, M.: The impact of temporal compression and space selection on svm analysis of single-subject and multi-subject fmri data. NeuroImage (accepted, 2006)
Hardoon, D.R., Saunders, C., Szedmak, S., Shawe-Taylor, J.: A correlation approach for automatic image annotation. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 681–692. Springer, Heidelberg (2006)
Wismuller, A., Meyer-Base, A., Lange, O., Auer, D., Reiser, M.F., Sumners, D.: Model-free functional mri analysis based on unsupervised clustering. Journal of Biomedical Informatics 37, 10–18 (2004)
Ciuciu, P., Poline, J., Marrelec, G., Idier, J., Pallier, C., Benali, H.: Unsupervised robust non-parametric estimation of the hemodynamic response function for any fmri experiment. IEEE TMI 22, 1235–1251 (2003)
O’Toole, A.J., Jiang, F., Abdi, H., Haxby, J.V.: Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience 17(4), 580–590 (2005)
Friman, O., Borga, M., Lundberg, P., Knutsson, H.: Adaptive analysis of fMRI data. NeuroImage 19, 837–845 (2003)
Friman, O., Carlsson, J., Lundberg, P., Borga, M., Knutsson, H.: Detection of neural activity in functional MRI using canonical correlation analysis. Magnetic Resonance in Medicine 45(2), 323–330 (2001)
Hardoon, D.R., Shawe-Taylor, J., Friman, O.: KCCA for fMRI Analysis. In: Proceedings of Medical Image Understanding and Analysis, London, UK (2004)
Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE International Conference on Computer vision, Kerkyra, Greece, pp. 1150–1157 (1999)
Hardoon, D.R., Mourao-Miranda, J., Brammer, M., Shawe-Taylor, J.: Unsupervised analysis of fmri data using kernel canonical correlation. NeuroImag (in press, 2007)
Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: International Conference on Computer Vision and Pattern Recognition, pp. 257–263 (2003)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Boser, B.E., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: D. Proc. Fifth Ann. Workshop on Computational Learning Theory, pp. 144–152. ACM, New York (1992)
Fyfe, C., Lai, P.L.: Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems 10, 365–377 (2001)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Computation 16, 2639–2664 (2004)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Stephan, K.E., Harrison, L.M., Penny, W.D., Friston, K.J.: Biophysical models of fmri responses. Current Opinion in Neurobiology 14, 629–635 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hardoon, D.R., Mourão-Miranda, J., Brammer, M., Shawe-Taylor, J. (2008). Using Image Stimuli to Drive fMRI Analysis. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_50
Download citation
DOI: https://doi.org/10.1007/978-3-540-69158-7_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69154-9
Online ISBN: 978-3-540-69158-7
eBook Packages: Computer ScienceComputer Science (R0)