  The traditional methods for detecting autism spectrum disorder (ASD) are
expensive, subjective, and time-consuming, often taking years for a diagnosis,
with many children growing well into adolescence and even adulthood before
finally confirming the disorder. Recently, graph-based learning techniques have
demonstrated impressive results on resting-state functional magnetic resonance
imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). We
introduce IMAGIN, a multI-granular, Multi-Atlas spatio-temporal attention Graph
Isomorphism Network, which we use to learn graph representations of dynamic
functional brain connectivity (chronnectome), as opposed to static connectivity
(connectome). The experimental results demonstrate that IMAGIN achieves a
5-fold cross-validation accuracy of 79.25%, which surpasses the current
state-of-the-art by 1.5%. In addition, analysis of the spatial and temporal
attention scores provides further validation for the neural basis of autism.
