text: This paper proposes to use 3D conditional GAN models to generate fMRI scans.  Using the generated images, paper reports improvement in classification accuracy on various tasks. One claim of the paper is that a generative model of fMRI data can help to caracterize and understand variability of scans across subjects. Article is based on recent works such as Wasserstein GANs and AC-GANs by (Odena et al., 2016). Despite the rich literature of this recent topic  the related work section is rather convincing. Model presented extends IW-GAN by using 3D convolution and also by supervising the generator using sample labels. Major: - The size of the generated images is up to 26x31x22  which is limited (about half the size of the actual resolution of fMRI data).  As a consequence results on decoding learning task using low resolution images can end up worse than with the actual data (as pointed out). What it means is that the actual impact of the work is probably limited. - Generating high resolution images with GANs even on faces for which there is almost infinite data is still a challenge.  Here a few thousand data points are used.  So it raises too concerns: First is it enough? Using so-called learning curves is a good way to answer this.  Second is what are the contributions to the state-of-the-art of the 2 methods introduced?  Said differently, as there is no classification results using images produced by an another GAN architecture  it is hard to say that the extra complexity proposed here (which is a bit contribution of the work) is actually necessary. Minor: - Fonts in figure 4 are too small.