We apply support vector machines (SVMs) in the context of fMRI analysis, in order to identify brain regions that are predictive of the experimental conditions. For the spatial SVM, we use the data within local 3D windows, called a searchlight, to train an SVM classifier to distinguish different experimental protocol conditions. Brain regions with high classification accuracy are identified as being implicated in the experimental task. Similarly for the temporal SVM, we use temporal sequences for every voxel to train a classifier.
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