Human brain function as measured by functional magnetic resonance imaging
 (fMRI), exhibits a rich diversity. In response, understanding the individual variability
 of brain function and its association with behavior has become one of the
 major concerns in modern cognitive neuroscience. Our work is motivated by the
 view that generative models provide a useful tool for understanding this variability.
 To this end, this manuscript presents two novel generative models trained
 on real neuroimaging data which synthesize task-dependent functional brain images.
 Brain images are high dimensional tensors which exhibit structured spatial
 correlations. Thus, both models are 3D conditional Generative Adversarial networks
 (GANs) which apply Convolutional Neural Networks (CNNs) to learn an
 abstraction of brain image representations. Our results show that the generated
 brain images are diverse, yet task dependent. In addition to qualitative evaluation,
 we utilize the generated synthetic brain volumes as additional training data to improve
 downstream fMRI classifiers (also known as decoding, or brain reading).
 Our approach achieves significant improvements for a variety of datasets, classifi-
 cation tasks and evaluation scores. Our classification results provide a quantitative
 evaluation of the quality of the generated images, and also serve as an additional
 contribution of this manuscript.
Two novel GANs are constructed to generate high-quality 3D fMRI brain images and synthetic brain images greatly help to improve downstream classification tasks.