Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning

TL;DR

This paper introduces a novel anatomically-informed data augmentation method for fMRI that enhances deep learning prediction accuracy, demonstrated by a 26% improvement in antidepressant response prediction.

Contribution

It presents a new data augmentation technique tailored for fMRI images that significantly improves predictive modeling performance over existing methods.

Findings

26% improvement in prediction accuracy

Augmentation enhances performance before hyperparameter tuning

Method outperforms natural image augmentation techniques

Abstract

The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help mitigate such training obstacles, most data augmentation methods have been developed for natural images as in computer vision tasks such as CIFAR, not for medical images. This work helps to fills in this gap by proposing a method for generating new functional Magnetic Resonance Images (fMRI) with realistic brain morphology. This method is tested on a challenging task of predicting antidepressant treatment response from pre-treatment task-based fMRI and demonstrates a 26% improvement in performance in predicting response using augmented images. This improvement compares favorably to state-of-the-art augmentation methods for natural images. Through an ablative test, augmentation is also shown to substantively improve performance when applied before hyperparameter optimization. These results suggest the optimal order of operations and support the role of data augmentation method for improving predictive performance in tasks using fMRI.