  Resting-state fMRI is commonly used for diagnosing Autism Spectrum Disorder
(ASD) by using network-based functional connectivity. It has been shown that
ASD is associated with brain regions and their inter-connections. However,
discriminating based on connectivity patterns among imaging data of the control
population and that of ASD patients' brains is a non-trivial task. In order to
tackle said classification task, we propose a novel deep learning architecture
(MHATC) consisting of multi-head attention and temporal consolidation modules
for classifying an individual as a patient of ASD. The devised architecture
results from an in-depth analysis of the limitations of current deep neural
network solutions for similar applications. Our approach is not only robust but
computationally efficient, which can allow its adoption in a variety of other
research and clinical settings.
