Thesis advisor: Stefano Anzellotti / Autism Spectrum Disorder (ASD) displays uniquely in every individual, creating disparities in symptom severity, genetics, and functional connectivity. Examining the relationship between genetic and functional connectivity variability could help to better understand individual differences in ASD. From this, improved diagnosis, treatment, and understanding of ASD can be developed. To resolve individual differences in symptom severity and presentation, I generated matrices of subject functional connectivity data and compared this to gene expression maps. Multivariate regression analysis was performed on the data to anticipate ASD symptoms from these correlation matrices and to establish which genes have the largest impact on these predictions. The ANOVAs ran on the data were not significant, but there were several genes implicated in specific aspects of ASD. STX1A, MVP, CDKL5, and RABEP2 were the only genes correlated across more than one subtype of ASD. These results pave the way for future research to investigate the roles of these genes in a larger size of ASD subjects. / Thesis (BS) — Boston College, 2023. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Psychology and Neuroscience.
| Identifer | oai:union.ndltd.org:BOSTON/oai:dlib.bc.edu:bc-ir_109731 |
| Date | January 2023 |
| Creators | Pijar, Julianna |
| Publisher | Boston College |
| Source Sets | Boston College |
| Language | English |
| Detected Language | English |
| Type | Text, thesis |
| Format | electronic, application/pdf |
| Rights | Copyright is held by the author, with all rights reserved, unless otherwise noted. |
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