Multi-branch deep learning model integrated with multi-modal MRI for differential diagnosis of autism spectrum disorders

This study proposes a multi-branch deep learning model fused with multi-modal MRI for the differential diagnosis of ASD. We solve the problem that traditional ASD classification algorithms based on static functional network connections ignore the time-varying characteristics of brain functional connections. Study the spatiotemporal characteristics of ASD brain imaging, and mine the information of functional connectivity between brain regions over time.