Unsupervised analysis of fMRI data using kernel canonical correlation

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 (of pleasant and unpleasant), then testing the algorithms on data withheld from the original training phase. The classification accuracies of SVM and …