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The traditional methods for detecting autism spectrum disorder (ASD) areexpensive, subjective, and time-consuming, often taking years for a diagnosis,with many children growing well into adolescence and even adulthood beforefinally confirming the disorder. Recently, graph-based learning techniques havedemonstrated impressive results on resting-state functional magnetic resonanceimaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). Weintroduce IMAGIN, a multI-granular, Multi-Atlas spatio-temporal attention GraphIsomorphism Network, which we use to learn graph representations of dynamicfunctional brain connectivity (chronnectome), as opposed to static connectivity(connectome). The experimental results demonstrate that IMAGIN achieves a5-fold cross-validation accuracy of 79.25%, which surpasses the currentstate-of-the-art by 1.5%. In addition, analysis of the spatial and temporalattention scores provides further validation for the neural basis of autism.

Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes for Detection of Autism Spectrum Disorder