Abstract

Psychopathology arises from a combination of many contributing factors, which can include genetic predispositions, family history, environmental stressors, developmental processes, and psychosocial dynamics. Each of these contributing factors has been researched for decades, and their influences on mental health have been well documented. In recent years, there has been a growing enthusiasm for integrating these risk factors using machine learning. However, there are a wide array of data-driven frameworks that have been implemented, and the relative strengthens of each approach is not clear. Through of series of four research studies, we examined each data-driven approach in terms of its potential to derive accurate predictions and enhance our understanding about the developmental origins of psychopathology. Our work identified critical methodological challenges when using machine learning to predict atypical aging (linked to multiple forms of psychopathology). Specifically, there were multiple sources of bias that hindered generalizability and no clear systematic ways of improving the utility of brain age gaps. These issues were not mitigated by multimodal imaging, as the only improvements were to predictive accuracy, which did not impact the utility of brain age gaps. Next, we provided novel insights about feature selection for deriving brain-based models of psychopathology, and further insight into how age-related differences should be incorporated into the model. Lastly, we implemented an RDoC approach towards building risk calculators of psychopathology, which uncovered key insights about the importance of sex-differences and social relationships for mental health. Mental health is a complex psychological construct, and deriving accurate predictive models will require greater integration across multi-level predictors and more customization to better account for societal differences and cultural norms regarding mental health.

Committee Chair

Deanna Barch

Committee Members

Aristeidis Sotiras; Janine Bijsterbosch; Nicole Karcher; Ryan Bogdan; Scott Marek

Degree

Doctor of Philosophy (PhD)

Author's Department

Interdisciplinary Programs

Author's School

McKelvey School of Engineering

Document Type

Dissertation

Date of Award

8-18-2025

Language

English (en)

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