2026-09-19T20:27:27Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1392982022-01-15T03:35:16Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Bathe, Mark
Wu, Julia
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-01-14T15:02:22Z
2022-01-14T15:02:22Z
2021-06
2021-06-17T20:14:56.152Z
https://hdl.handle.net/1721.1/139298
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.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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http://rightsstatements.org/page/InC-EDU/1.0/
Characterizing Autism and Schizophrenia Using PRISM and Deep Learning
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<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.</Abstract>
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