  Resting-state functional MRI (rsfMRI) yields functional connectomes that can
serve as cognitive fingerprints of individuals. Connectomic fingerprints have
proven useful in many machine learning tasks, such as predicting
subject-specific behavioral traits or task-evoked activity. In this work, we
propose a surface-based convolutional neural network (BrainSurfCNN) model to
predict individual task contrasts from their resting-state fingerprints. We
introduce a reconstructive-contrastive loss that enforces subject-specificity
of model outputs while minimizing predictive error. The proposed approach
significantly improves the accuracy of predicted contrasts over a
well-established baseline. Furthermore, BrainSurfCNN's prediction also
surpasses test-retest benchmark in a subject identification task.
