  We present an empirical evaluation of fMRI data augmentation via synthesis.
For synthesis we use generative mod-els trained on real neuroimaging data to
produce novel task-dependent functional brain images. Analyzed generative
mod-els include classic approaches such as the Gaussian mixture model (GMM),
and modern implicit generative models such as the generative adversarial
network (GAN) and the variational auto-encoder (VAE). In particular, the
proposed GAN and VAE models utilize 3-dimensional convolutions, which enables
modeling of high-dimensional brain image tensors with structured spatial
correlations. The synthesized datasets are then used to augment classifiers
designed to predict cognitive and behavioural outcomes. Our results suggest
that the proposed models are able to generate high-quality synthetic brain
images which are diverse and task-dependent. Perhaps most importantly, the
performance improvements of data aug-mentation via synthesis are shown to be
complementary to the choice of the predictive model. Thus, our results suggest
that data augmentation via synthesis is a promising approach to address the
limited availability of fMRI data, and to improve the quality of predictive
fMRI models.
