  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.

Neurons and Cognition;Artificial Intelligence;Machine Learning;Image and Video Processing