Youth mental health impairments are a leading and growing cause of disability. Mental health deficits during childhood and adolescence often portend more serious illness later in life, and early intervention at time of symptom onset may be critical to ameliorating disease trajectories. However, despite their importance, the neural and developmental underpinnings of many psychiatric disorders are not well understood.
This dissertation aims to improve our understanding of pediatric psychiatric disorders by harnessing the power of machine learning, large sample sizes, and distinct training and replication subsamples to robustly examine functional magnetic resonance imaging data in two large samples of youth.
In Chapter 2, we review prior uses of machine learning in the psychiatric neuroimaging literature. We also develop a framework for evaluating machine learning applications in psychiatric neuroimaging, which we apply throughout this dissertation.
In Chapter 3 (Study 1), we use several supervised and unsupervised machine learning techniques to probe functional neural correlates of obsessive-compulsive symptoms in a large, multi-site community sample of youth. We find that patterns of individual obsessive-compulsive symptoms are fairly stable across subsamples. Granular resting state functional connectivity patterns associated with those symptom dimensions are not reliable, but broader large-scale network patterns appear to be more stable across subsamples.
In Chapter 4 (Study 2), we use a different large sample of youth to assess clinical, cognitive, and demographic factors associated with head motion during fMRI. Head motion is a known source of artifact in fMRI data, especially data collected from youth. Our findings suggest that head motion may be systematically associated with neuropsychiatric symptom severity, thus potentially confounding neuroimaging studies involving patient populations.
Across studies, this dissertation highlights the need for reproducibility and replicability, with a focus on research transparency, code sharing, and pre-registration of analyses. We hope herein to provide a solid methodological foundation from which to build our understanding of the neural basis of pediatric psychiatric symptoms.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/m11e-2m62 |
| Date | January 2024 |
| Creators | Reznik, Tracey Chen Shi |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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