Deep learning models are applied in clinical research in order to diagnose disease. However,
diagnosing autism spectrum disorders (ASD) remains challenging due to its complex psychiatric symptoms
as well as a generally insuf cient amount of neurobiological evidence. We investigated the structural and
strategic bases of ASD using 14 different types of models, including convolutional and recurrent neural
networks. Using an open source autism dataset consisting of more than 1000 MRI scan images and a
high-resolution structural MRI dataset, we demonstrated how deep neural networks could be used as tools for
diagnosing and analyzing psychiatric disorders. We trained 3D convolutional neural networks to visualize
combinations of brain regions, thus representing the most referred-to regions used by the model whilst
classifying the images. We also implemented recurrent neural networks to classify the sequence of brain
regions ef ciently. We found emphatic structural and strategic evidence on which the model heavily relies
during the classi cation process. For instance, we observed that the structural and strategic evidence tends to
be associated with subcortical structures, including the basal ganglia (BG). Our work identi es the distinct
brain structures that characterize a complex psychiatric disorder while streamlining the deductive reasoning
that clinicians can use to ensure an economical and time-ef cient diagnosis process.