Abstract
All vertebrate brains contain a dense matrix of thin fibers that release serotonin (5-hydroxytryptamine), a neurotransmitter that modulates a wide range of neural, glial, and vascular processes. Perturbations in the density of this matrix have been associated with a number of mental disorders, including autism and depression, but its self-organization and plasticity remain poorly understood. We introduce a model based on reflected Fractional Brownian Motion (FBM), a rigorously defined stochastic process, and show that it recapitulates some key features of regional serotonergic fiber densities. Specifically, we use supercomputing simulations to model fibers as FBM-paths in two-dimensional brain-like domains and demonstrate that the resultant steady state distributions approximate the fiber distributions in physical brain sections immunostained for the serotonin transporter (a marker for serotonergic axons in the adult brain). We suggest that this framework can support predictive descriptions and manipulations of the serotonergic matrix and that it can be further extended to incorporate the detailed physical properties of the fibers and their environment.
Recommended Citation
S. Janusonis et al., "Serotonergic Axons as Fractional Brownian Motion Paths: Insights into the Self-Organization of Regional Densities," Frontiers in Computational Neuroscience, vol. 14, Frontiers Media, Jun 2020.
The definitive version is available at https://doi.org/10.3389/fncom.2020.00056
Department(s)
Physics
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
5-hydroxytryptamine; anomalous diffusion; brain; density; fibers; fractional Brownian motion; serotonin; stochastic process
International Standard Serial Number (ISSN)
1662-5188
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2020 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
24 Jun 2020
Comments
This research was supported by the National Science Foundation (grants #1822517 and #1921515 to SJ and ND), the National Institute of Mental Health (grant #MH117488 to SJ and ND), the California NanoSystems Institute (Challenge grants to SJ and ND), the Research Corporation for Science Advancement (a Cottrell SEED Award to TV), and the German Research Foundation (DFG grant #ME 1535/7-1 to RM).