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, classification 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.
The work is motivated by a real challenge of neuroimaging analysis: how to increase the amount of data to support the learning of brain decoding.
The contribution seems to mix two objectives: on one hand to prove that it is possible to do data augmentation for fMRI brain decoding, on the other hand to design (or better to extend) a new model (to be more precise two models).
Concerning the first objective the empirical results do not provide meaningful support that the generative model is really effective. The improvement is really tiny and a statistical test (not included in the analysis) probably wouldn't pass a significant threshold.  This analysis is missing a straw man. It is not clear whether the difference in the evaluation measures is related to the greater number of examples or by the specific generative model.
Concerning the contribution of the model, one novelty is the conditional formulation of the discriminator. The design of the empirical evaluation doesn't address the analysis of the impact of this new formulation. It is not clear whether the supposed improvement is related to the conditional formulation. 
Figure 3 and Figure 5 illustrate the brain maps generated for Collection 1952 with ICW-GAN and for collection 503 with ACD-GAN. It is not clear how the authors operated the choices of these figures. From the perspective of neuroscience a reader,  would expect to look at the brain maps for the same collection with different methods. The pairwise brain maps would support the interpretation of the generated data. It is worthwhile to remember that the location of brain activations is crucial to detect whether the brain decoding (classification) relies on artifacts or confounds.
Minor comments
- typos: "a first application or this" => "a first application of this" (p.2)
- "qualitative quality" (p.2)