# Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder

arXiv: https://arxiv.org/abs/2206.11224 · HTML: https://mlsanity.com/paper/2206.11224/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis](https://mlsanity.com/paper/2007.14589/) (2020)
- [Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes](https://mlsanity.com/paper/2106.09812/) (2021)
- [Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images](https://mlsanity.com/paper/2008.02708/) (2020)
- [Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images](https://mlsanity.com/paper/1807.02908/) (2018)
- [Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets](https://mlsanity.com/paper/2010.10763/) (2020)
- [Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets](https://mlsanity.com/paper/2012.13321/) (2020)
- [Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images](https://mlsanity.com/paper/2102.02895/) (2021)

## Built on by (most cited first, 1 of 1)

- [Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices](https://mlsanity.com/paper/2211.14500/) (2022)

## Abstract (arXiv)

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.