Unsupervised analysis of fMRI data using Kernel Canonical Correlation
David Hardoon, Janaina Mourao-Miranda, Michael Brammer and John Shawe-Taylor
We introduce a new unsupervised fMRI analysis method based on Kernel Canonical Correlation Analysis which differs from the class of supervised learning methods ( e,g the Support Vector Machine) that are increasingly being employed in fMRI data analysis. Whereas SVM associates properties of the imaging data with simple specific categorical labels ( e.g -1, 1 indicating experimental conditions 1 and 2), 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 able to extract many of the brain regions that SVM identifies as the most important in task discrimination blind to the categorical task labels. Instead, the stimulus category is effectively derived from the vector of image features.