5070. Latent Neural Systems of Stress Responsivity: Implications for Health and Social Environments
Invited abstract in session OR and Health Informatics for Sustainable Societal Outcomes, stream OR for Development.
Authors (first author is the speaker)
| 1. | Miriam Dash
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| Urologic Surgery, Mayo Clinic | |
| 2. | Nina Kajiji
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| Computer Science and Statistics, University of Rhode Island, and The NKD Group, Inc. | |
| 3. | Gordon Dash
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| Finance and Decision Sciences, University of Rhode Island | |
| 4. | S Tiffany Donaldson
|
| Psychology, University of Massachusetts Boston |
Abstract
Addressing stress is central to the SDG goal of good health and well-being. Our study models latent stress dynamics in rats bred for trait anxiety and reared socially or in isolation using multivariate machine learning approaches. Results show that stress responsivity is organized into domain-specific neural systems, driven primarily by sex, with limited contributions from trait and environment. These findings inform a future multi-objective goal-programming framework for public housing that incorporates vulnerability and environmental stressors to support a health-sensitive allocation policy.
Keywords
- Healthcare Analytics
- Life Sciences
- Machine Learning
Status: accepted
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