We explore transferring learning between fMRI datasets. A method is introduced to improve prediction accuracy on a primary fMRI dataset by jointly learning a model using other secondary fMRI datasets. We assume the secondary datasets are directly or indirectly linked to the primary dataset through sets of partially shared subjects. This method is particularly useful when the primary dataset is small. Using six fMRI datasets linked by various subsets of shared subjects, we show that the method yields improved performance in various predictive tasks. Our tests are performed on a variety of regions of interest in the brain and across various stimuli.
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