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Serotonergic Axons as Fractional Brownian Motion Paths: Insights Into the Self-Organization of Regional Densities.
Janusonis, Skirmantas; Detering, Nils; Metzler, Ralf; Vojta, Thomas.
Afiliación
  • Janusonis S; Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, United States.
  • Detering N; Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA, United States.
  • Metzler R; Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany.
  • Vojta T; Department of Physics, Missouri University of Science and Technology, Rolla, MO, United States.
Front Comput Neurosci ; 14: 56, 2020.
Article en En | MEDLINE | ID: mdl-32670042
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.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Comput Neurosci Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Comput Neurosci Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos