Characterizing Autism and Schizophrenia Using PRISM and Deep Learning
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 immunofluor...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139298 |
| _version_ | 1826201927781711872 |
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| author | Wu, Julia |
| author2 | Bathe, Mark |
| author_facet | Bathe, Mark Wu, Julia |
| author_sort | Wu, Julia |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:59:04Z |
| format | Thesis |
| id | mit-1721.1/139298 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:59:04Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1392982022-01-15T03:35:16Z Characterizing Autism and Schizophrenia Using PRISM and Deep Learning Wu, Julia Bathe, Mark Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. 2022-01-14T15:02:22Z 2022-01-14T15:02:22Z 2021-06 2021-06-17T20:14:56.152Z Thesis https://hdl.handle.net/1721.1/139298 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Wu, Julia Characterizing Autism and Schizophrenia Using PRISM and Deep Learning |
| title | Characterizing Autism and Schizophrenia Using PRISM and Deep Learning |
| title_full | Characterizing Autism and Schizophrenia Using PRISM and Deep Learning |
| title_fullStr | Characterizing Autism and Schizophrenia Using PRISM and Deep Learning |
| title_full_unstemmed | Characterizing Autism and Schizophrenia Using PRISM and Deep Learning |
| title_short | Characterizing Autism and Schizophrenia Using PRISM and Deep Learning |
| title_sort | characterizing autism and schizophrenia using prism and deep learning |
| url | https://hdl.handle.net/1721.1/139298 |
| work_keys_str_mv | AT wujulia characterizingautismandschizophreniausingprismanddeeplearning |