PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Using Image Stimuli to Drive fMRI Analysis
David Hardoon, Janaina Mourao-Miranda, Michael Brammer and John Shawe-Taylor
In: 14th International Conference on Neural Information Processing, 13-16 Nov 2007, Kitakyushu, Japan.


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.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Brain Computer Interfaces
Multimodal Integration
ID Code:2976
Deposited By:David Hardoon
Deposited On:24 September 2007