This dissertation examines brain lateralization and interhemispheric asymmetry patterns found in youths with Attention-Deficit / Hyperactivity Disorder (ADHD). Prior research groups have found mixed findings with respect to left and right hemisphere alterations from ADHD subjects using structural magnetic resonance imaging. In these investigations, we propose the use of Asymmetry Index (AI), a subject-specific metric that quantifies the extent of brain asymmetry and allows each subject to serve as their own control, thus reducing variability when pooling across different sites. We compare AI metric with laterality across volumetric, surface area, thickness, morphology, and white matter measures in order to characterize the ADHD brain over the course of neurodevelopment, psychotropic therapy, and behavioral presentations. Linear mixed effects models were characterized to account for individual differences and maturation. We reproduce the findings across several regional and international data consortiums that contain both cross-sectional and longitudinal ADHD neuroimaging data. Structural asymmetry group differences were more significant than lateralized comparisons across a number of volumetric and white matter measures, confirming asymmetry is robust at detecting differences between healthy controls and ADHD brains. However, the effects of medication and behavioral phenotypes failed to reproduce significant alterations across symmetry measures. We discuss these implications in light of recent evidence of possible neuroprotective features of ADHD. Future work may investigate the extent to which these brain asymmetry differences are causal or compensatory. Although structural AI is unlikely to provide a useful biomarker for ADHD, a deeper understanding of these asymmetry patterns could lead to better profiling of the clinical diagnostics and to personalized treatments.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1496 |
| Date | 01 January 2021 |
| Creators | Dutta, Cintya |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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