Stochastic Actor-Oriented Models of Structural and Functional Human Brain Networks In Aging

The human brain can be seen as a complex and dynamical system that undergoes structural and functional changes throughout the lifetime. Non-invasive imaging techniques including magnetic resonance imaging (MRI) allow us to measure large-scale functional and structural connectivity in the brain. These data have led to insights into the growing picture of the human brain, yet questions remain. Beyond trends in measurable phenomena, the dynamics in the brain across long timescales are still poorly understood. The physical and temporal complexities of the brain require approaches grounded in complexity research, including network theory and stochastic modeling. Thus, our research aims to address the question of these long-term dynamics in the brain using stochastic models of network change. Specifically, we have adapted and applied the Stochastic Actor-Oriented Model to brain networks derived from functional and structural MRI data specifically in an aging population. These models are able to provide a deeper understanding of the emergent phenomena arising from these changing network interactions by identifying the factors that are driving changes across the lifetime. Furthermore, this research has applications to the experiences and treatments of aging and neurodegenerative illness.