Characterizing Autism and Schizophrenia Using PRISM and Deep Learning

Abstract

Schizophrenia and autism spectrum disorder (ASD) are two life-altering neurological diseases whose neurobiological bases are not yet well understood. This thesis explores the phenotypical expression of autism and schizophrenia at the synapse level by applying deep learning to multiplexed immunofluorescence data. Deep convolutional networks are developed and applied to analyze PRISM images of neurons treated with gene knockdown treatments corresponding to genes associated with autism and schizophrenia. Similarities and differences between normal-type and disease-type synapses are identified, and underlying synaptic phenotype groups are discovered and characterized. The results provide potential biologic insights into autism and schizophrenia that can serve as a starting point for further experimental analysis.