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Sunday, July 19 • 7:00pm - 8:00pm
P209: A Predictive Model of Serotonergic Fiber Densities Based on Reflected Fractional Brownian Motion

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Skirmantas Janusonis, Ralf Metzler, Thomas Vojta

All vertebrate brains contain a dense matrix of thin axons (fibers) that release serotonin (5-hydroxytryptamine), a neurotransmitter that modulates a wide range of neural, glial, and vascular processes. Altered serotonergic fiber densities have been associated with a number of mental disorders and conditions, such as Autism Spectrum Disorder, Major Depressive Disorder, and exposure to 3,4-methylenedioxymethamphetamine (MDMA, "Ecstasy"). Also, serotonergic fibers can regrow in adulthood and therefore can support the functional recovery of the brain after injury. However, the processes that lead to the self-organization and plasticity of this fiber system remain poorly understood.

Our previous research has shown that the trajectories of serotonergic fibers in terminal fields can be modeled as random walks [1, 2]. We now introduce a computational model that is based on Fractional Brownian Motion (FBM), a continuous stochastic process that generalizes normal Brownian Motion and allows correlations between non-overlapping increments. The model capitalizes on the recently discovered properties of the reflected FBM (rFBM) in one- dimensional domains [3, 4].

FBM is parametrized by the Hurst index ( _H_ ) that allows subdiffusion ( _H_ < ½) and superdiffusion ( _H_ > ½). We show that in the superdiffusive regime rFBM-walks recapitulate some key features of regional serotonergic fiber densities, on the whole-brain scale. Specifically, by using supercomputing simulations of fibers as FBM-paths in two-dimensional brain-like domains, we demonstrate that the resultant steady-state distributions approximate the fiber distributions in mouse brain sections immunostained for the serotonin transporter (a marker for serotonergic fibers in the adult brain). These results do not sensitively depend on the _H_ -value (for _H_ > ½), precise estimates of which are currently difficult to obtain experimentally.

This novel framework can support predictive descriptions and manipulations of the serotonergic matrix and it can be further extended to incorporate the detailed physical properties of the fibers and their environment. We also show that this neuroscience-motivated approach can stimulate theoretical investigations of rFBM in two- and three-dimensional domains, with potential applications in other fields of science.

Acknowledgements This research is funded by the National Science Foundation (grants #1822517 and #1921515 to SJ), the National Institute of Mental Health (grant #MH117488 to SJ), the California NanoSystems Institute (Challenge grants to SJ), 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), and the Foundation of Polish Science (an Alexander von Humboldt Polish Honorary Research Scholarship to RM).

References [1] Janušonis S., Detering N. A stochastic approach to serotonergic fibers in mental disorders. Biochimie. 2019, 161, 15-22. [2] Janušonis S., Mays K.C., Hingorani M.T. Serotonergic fibers as 3D-walks. ACS Chem Neurosci. 2019, 10, 3064-3067. [3] Wada A.H.O., Vojta, T. Fractional Brownian motion with a reflecting wall. Phys. Rev. E. 2018, 97, 020102. [4] Guggenberger T., Pagnini G., Vojta T., Metzler R. Fractional Brownian motion in a finite interval: correlations effect depletion or accretion zones of particles near boundaries. New J. Phys. 2019, 21, 022002.

avatar for Skirmantas Janusonis

Skirmantas Janusonis

Associate Professor, University of California, Santa Barbara
My laboratory investigates the self-organization of the brain serotonergic matrix. Our research is inherently interdisciplinary and spans molecular neurobiology, advanced microscopy (including live imaging with holotomography, super-resolution microscopy), midbrain neuronal cell cultures... Read More →

Sunday July 19, 2020 7:00pm - 8:00pm CEST
Slot 16