BLENDS: Augmentation of Functional Magnetic Resonance Images for Machine Learning Using Anatomically Constrained Warping

Abstract

Introduction:

Data augmentation improves the accuracy of deep learning models when training data are scarce by synthesizing additional samples. This work addresses the lack of validated augmentation methods specific for synthesizing anatomically realistic four-dimensional (4D) (three-dimensional [3D] + time) images for neuroimaging, such as functional magnetic resonance imaging (fMRI), by proposing a new augmentation method.

Methods:

The proposed method, Brain Library Enrichment through Nonlinear Deformation Synthesis (BLENDS), generates new nonlinear warp fields by combining intersubject coregistration maps, computed using symmetric normalization, through spatial blending. These new warp fields can be applied to existing 4D fMRI to create new augmented images. BLENDS was tested on two neuroimaging problems using de-identified data sets: (1) the prediction of antidepressant response from task-based fMRI (original data set n = 163), and (2) the prediction of Parkinson's disease (PD) symptom trajectory from baseline resting-state fMRI regional homogeneity (original data set n = 43).

Results:

BLENDS readily generates hundreds of new fMRI from existing images, with unique anatomical variations from the source images, that significantly improve prediction performance. For antidepressant response prediction, augmenting each original image once (2 × the original training data) significantly increased prediction R ² from 0.055 to 0.098 ( $p<1e^{-6}$ ), whereas at 10 × augmentation R ² increased to 0.103. For the prediction of PD trajectory, 10 × augmentation R ² increased from −0.044 to 0.472 ( $p<1e^{-6}$ ).

Conclusions:

Augmentation of fMRI through nonlinear transformations with BLENDS significantly improved the performance of deep learning models on clinically relevant predictive tasks. This method will help neuroimaging researchers overcome data set size limitations and achieve more accurate predictive models.

Impact statement

Given deep learning success across life science, there is interest in their application to neuroimaging. Deep learning requires a large training dataset, hence data augmentation has been proposed to bolster model training, however, there is no validated augmentation for synthesizing anatomically realistic 4D images. To remedy, we propose BLENDS, which generates new fMRI by combining intersubject coregistrations into a warp field which is applied to existing 4D fMRI to create new images. Tests on predicting: (1) antidepressant response and (2) Parkinson's trajectory demonstrate significant performance improvements. The proposed general method helps researchers overcome limited data and achieve accurate predictive models.