  Purpose : Because functional MRI (fMRI) data sets are in general small, we
sought a data efficient approach to resting state fMRI classification of autism
spectrum disorder (ASD) versus neurotypical (NT) controls. We hypothesized that
a Deep Reinforcement Learning (DRL) classifier could learn effectively on a
small fMRI training set.
  Methods : We trained a Deep Reinforcement Learning (DRL) classifier on 100
graph-label pairs from the Autism Brain Imaging Data Exchange (ABIDE) database.
For comparison, we trained a Supervised Deep Learning (SDL) classifier on the
same training set.
  Results : DRL significantly outperformed SDL, with a p-value of 2.4 x
10^(-7). DRL achieved superior results for a variety of classifier performance
metrics, including an F1 score of 76, versus 67 for SDL. Whereas SDL quickly
overfit the training data, DRL learned in a progressive manner that generalised
to the separate testing set.
  Conclusion : DRL can learn to classify ASD versus NT in a data efficient
manner, doing so for a small training set. Future work will involve optimizing
the neural network for data efficiency and applying the approach to other fMRI
data sets, namely for brain cancer patients.
