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We demonstrate a method to build an autoregressive model for the whole brain without carrying out any aggregation of the fMRI data. The model gives biologically meaningful results and has several desirable properties. We show that the model gives significantly improved prediction on unseen data as compared to baseline methods. The voxels with better prediction are distributed throughout the brain,...
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...
Information about various visual attributes, such as orientation or color, is represented in the primate striate cortex (VI) before reaching extrastriate cortices. Previous studies, using single-or multi-unit recordings, have reached conflicting conclusions regarding the question of whether these different attributes are represented by separate populations of neurons in VI. We addressed this question...
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