Predicting Task fMRI Contrasts from Resting-State fMRI Using Sparse 3D Convolutions

Functional Magnetic Resonance Imaging (fMRI) enables the non-invasive mapping of brain activity during various cognitive processes, and understanding how specific cognitive tasks activate distinct brain regions is crucial for both neuroscience and clinical applications. However, acquiring task-based fMRI (tfMRI) is costly, time-consuming, and often infeasible for individuals unable to perform tasks. Resting-state fMRI (rsfMRI), which is more widely available and does not require task compliance, can instead be leveraged to infer task-related activations. In this work, we propose BrainSparseCNN, a sparse 3D convolutional neural network that exploits the brain's spatial structure to predict tfMRI contrasts from rsfMRI while efficiently processing high-dimensional neuroimaging data. BrainSparseCNN achieves up to 7% higher Pearson correlation than surface-based and dense volumetric competitors, with statistically significant gains (p < 0.01) across all tasks, furthermore improving spatial alignment, subject identification accuracy, and saliency interpretability while maintaining computational efficiency. Overall, BrainSparseCNN provides a more precise, interpretable, and scalable framework for inferring individual functional activation maps from resting-state data, with code publicly available at https://github.com/univanxx/brainsparsecnn.