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Dissertation
Spatial and temporal brain and biological aging trajectories and their impact on physical and mental health outcomes
Doctor of Philosophy (Ph.D.), Drexel University
May 2026
DOI:
https://doi.org/10.17918/00011490
Abstract
Aging is a heterogeneous process that unfolds differently across brain regions and biological systems. Traditional biomarkers of aging, whether neuroimaging-derived brain age or biomarker-derived biological age, provide useful but limited perspectives because they are studied by calculating a single index to represent either brain or biological age. Importantly, while variability can be quantified at the population level, most existing approaches lack methods for quantifying uncertainty at the individual level, leaving researchers and clinicians uncertain about how confident to be in any single person's estimated brain or biological age. This dissertation develops a multidimensional aging framework that integrates brain and biological age estimates while utilizing CVAE-based spatiotemporal clustering approaches. Using UK Biobank as the primary dataset and MIDUS as an external validation cohort, we employed a deep neural network (Conditional Variational Autoencoders) to cluster brain ROI's and biological systems by both spatial and temporal patterns simultaneously to identify aging-relevant feature clusters across neuroimaging modalities and biological systems. For each identified aging biomarker cluster, stacked ensemble (SVR/GPR/DNN) and conformal inference methods will be used to quantify brain and biological age with individual-level prediction uncertainty. Results demonstrated that multidimensional clustering approaches identified distinct aging-related brain and biological domains with heterogeneous aging trajectories. Cluster-specific age estimates showed meaningful associations with physical, cognitive, and mental health outcomes, including cardiovascular disease, diabetes, dementia-related outcomes, depression, anxiety, and cognitive performance measures. In several analyses, cluster-specific aging measures provided more informative and localized associations than conventional single-index age-gap approaches. Conformal prediction analyses further demonstrated that uncertainty-aware aging measures could identify individuals with high-confidence accelerated aging profiles through positive out-of-bounds (OOB) classification. External validation analyses in MIDUS supported the generalizability of the developed framework across independent cohorts. By incorporating simultaneous spatial and temporal clustering, individual prediction intervals, and external replication, this work demonstrates a novel, rigorous, and interpretable framework for characterizing aging heterogeneity.
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Details
- Title
- Spatial and temporal brain and biological aging trajectories and their impact on physical and mental health outcomes
- Creators
- Hansoo Chang
- Contributors
- Fengqing Zhang (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University
- Number of pages
- 146 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology); College of Arts and Sciences; Drexel University
- Other Identifier
- 991022193297004721