  Learning how to predict the brain connectome (i.e. graph) development and
aging is of paramount importance for charting the future of within-disorder and
cross-disorder landscape of brain dysconnectivity evolution. Indeed, predicting
the longitudinal (i.e., time-dependent ) brain dysconnectivity as it emerges
and evolves over time from a single timepoint can help design personalized
treatments for disordered patients in a very early stage. Despite its
significance, evolution models of the brain graph are largely overlooked in the
literature. Here, we propose EvoGraphNet, the first end-to-end geometric deep
learning-powered graph-generative adversarial network (gGAN) for predicting
time-dependent brain graph evolution from a single timepoint. Our EvoGraphNet
architecture cascades a set of time-dependent gGANs, where each gGAN
communicates its predicted brain graphs at a particular timepoint to train the
next gGAN in the cascade at follow-up timepoint. Therefore, we obtain each next
predicted timepoint by setting the output of each generator as the input of its
successor which enables us to predict a given number of timepoints using only
one single timepoint in an end- to-end fashion. At each timepoint, to better
align the distribution of the predicted brain graphs with that of the
ground-truth graphs, we further integrate an auxiliary Kullback-Leibler
divergence loss function. To capture time-dependency between two consecutive
observations, we impose an l1 loss to minimize the sparse distance between two
serialized brain graphs. A series of benchmarks against variants and ablated
versions of our EvoGraphNet showed that we can achieve the lowest brain graph
evolution prediction error using a single baseline timepoint. Our EvoGraphNet
code is available at http://github.com/basiralab/EvoGraphNet.
