  Autism spectrum disorder (ASD) is associated with behavioral and
communication problems. Often, functional magnetic resonance imaging (fMRI) is
used to detect and characterize brain changes related to the disorder.
Recently, machine learning methods have been employed to reveal new patterns by
trying to classify ASD from spatio-temporal fMRI images. Typically, these
methods have either focused on temporal or spatial information processing.
Instead, we propose a 4D spatio-temporal deep learning approach for ASD
classification where we jointly learn from spatial and temporal data. We employ
4D convolutional neural networks and convolutional-recurrent models which
outperform a previous approach with an F1-score of 0.71 compared to an F1-score
of 0.65.
