  Multivariate dynamical processes can often be intuitively described by a
weighted connectivity graph between components representing each individual
time-series. Even a simple representation of this graph as a Pearson
correlation matrix may be informative and predictive as demonstrated in the
brain imaging literature. However, there is a consensus expectation that
powerful graph neural networks (GNNs) should perform better in similar
settings. In this work, we present a model that is considerably shallow than
deep GNNs, yet outperforms them in predictive accuracy in a brain imaging
application. Our model learns the autoregressive structure of individual time
series and estimates directed connectivity graphs between the learned
representations via a self-attention mechanism in an end-to-end fashion. The
supervised training of the model as a classifier between patients and controls
results in a model that generates directed connectivity graphs and highlights
the components of the time-series that are predictive for each subject. We
demonstrate our results on a functional neuroimaging dataset classifying
schizophrenia patients and controls.
