Theses and Dissertations
Date of Award
5-1-2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Haoteng Tang
Second Advisor
Marzieh Ayati
Third Advisor
Li Zhang
Abstract
Functional brain connectivity provides critical insight into neural mechanisms underlying neurodegenerative and affective disorders. Traditional neuroimaging studies rely on undirected or region-specific connectivity measures developed predominantly using racially homogeneous cohorts, limiting fairness and generalizability across diverse populations.
We propose a population-aware framework modeling directed causal interactions across large-scale brain functional subnetworks. Resting-state fMRI data from the HABS-HD cohort were used to construct subject-level causal connectivity matrices via ICA-LiNGAM, aggregated into interpretable system-level hyper-connectomes representing interactions among eleven canonical brain subsystems. These features trained nonlinear models for Alzheimer’s disease stage classification and trait worry prediction.
Results demonstrate that MLP models outperform traditional approaches, and race-specific modeling improves performance over pooled models. SHAP analysis reveals shared vulnerability pathways, including frontoparietal-to-default mode interactions, and population-specific regulatory circuits. These findings underscore the necessity of incorporating population context into neuroimaging-based predictive modeling for equitable, precision-focused neuroscience.
Recommended Citation
Moreno, A. (2026). Modeling Causal Interactions Across Brain Functional Systems for Population-Specific Disease Analysis [Master's thesis, The University of Texas Rio Grande Valley]. ScholarWorks @ UTRGV. https://scholarworks.utrgv.edu/etd/1930
Comments
Copyright 2026 Alissen D. Moreno. All Rights Reserved. https://proquest.com/docview/3371130583