  Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a
diagnostic or prognostic tool for a wide variety of conditions, such as autism,
Alzheimer's disease, and stroke. While a growing number of studies have
demonstrated the promise of machine learning algorithms for rs-fMRI based
clinical or behavioral prediction, most prior models have been limited in their
capacity to exploit the richness of the data. For example, classification
techniques applied to rs-fMRI often rely on region-based summary statistics
and/or linear models. In this work, we propose a novel volumetric Convolutional
Neural Network (CNN) framework that takes advantage of the full-resolution 3D
spatial structure of rs-fMRI data and fits non-linear predictive models. We
showcase our approach on a challenging large-scale dataset (ABIDE, with N >
2,000) and report state-of-the-art accuracy results on rs-fMRI-based
discrimination of autism patients and healthy controls.
