Interpretable whole-brain prediction analysis with GraphNet

► We introduce robust, interpretable models for prediction with whole-brain fMRI data. ► These use a sparsity-inducing penalty that automatically selects important voxels. ► They also include a graph penalty to structure the solution. ► They outperform state-of-the-art classifiers on whole-brain fMRI data. ► They predict outcomes on new data collected years after the data used for training.