All brain regions maintain specific densities of serotonergic fibers. Changes in these densities have been associated with various mental disorders, psychiatric conditions, and drug-induced effects. This interdisciplinary project seeks to reconstruct the fundamental self-organizing process that builds and supports the serotonergic matrix in the brain. Specifically, we hypothesize that the behavior of single serotonergic fibers can be described by a three-dimensional stochastic process and that the controlling parameters of this process are sufficient to predict the resultant fiber density, as an emergent phenomenon. This understanding is essential for novel therapeutic approaches and is especially relevant to future applications of nanomaterials in correcting brain abnormalities. The Principal Investigator team brings together expertise in serotonin neurobiology, computer engineering, and stochastic mathematics. In the project, we will use mouse models to understand the principal process driving single serotonergic fibers in a set of brain regions representing different fiber densities. Single fibers will be visualized with immunohistochemistry (including tissue expansion) and imaged with confocal laser scanning microscopy. A custom-designed image analysis algorithm will be built to automatically detect and isolate individual fiber trajectories in the three-dimensional space. The digitized trajectories will be used to create an optimal stochastic model, using advanced methods employed in financial mathematics, polymer dynamics, and other fields. This model will then be applied to human brain specimens, with the goal of detecting specific and quantifiable changes in the behavior of single serotonergic fibers in several mental disorders. This approach will provide new, quantitative insights into the origin and homeostasis of region-specific serotonergic densities in the brain. Notably, it may also reveal the likely causes of altered single-fiber behavior in mental disorders (e.g., it may be associated with changes in the distribution of physical obstacles in the tissue microarchitecture) and therefore can suggest therapeutic countermeasures (e.g., using biodegradable nanomaterials to manipulate the density of such obstacles). Generally, the project introduces a novel approach to the structural dynamics of ?diffuse? neurotransmitter systems and advances predictive approaches to neurosignaling in mental disorders, including functional recovery.
A number of mental disorders and psychiatric abnormalities have been associated with altered densities of fibers that release serotonin in the brain. The project will test a novel hypothesis that these densities are the consequence of individual fibers performing random walks, with no top-down control. By using experimental analysis of mouse and human brains, computer image analysis, and stochastic mathematics, the project will build a predictive model with a potential for future therapeutic treatments.
