A simple integrative electrophysiological model of bursting GnRH neurons

In this paper a modular model of the GnRH neuron is presented. For the aim of simplicity, the currents corresponding to fast time scales and action potential generation are described by an impulsive system, while the slower currents and calcium dynamics are described by usual ordinary differential equations (ODEs). The model is able to reproduce the depolarizing afterpotentials, afterhyperpolarization, periodic bursting behavior and the corresponding calcium transients observed in the case of GnRH neurons

Endogén glutamát jelentősége neuroendokrin rendszerek szabályozásában = Role of endogenous glutamate in the regulation of neurosecretory systems

A kutatási támogatás segítségével a kutatócsoport tanulmányozta és feltérképezte az átvivőanyagként glutamátot használó (vezikuláris glutamát transzportereket tartalmazó) idegsejtek pontos anatómiai megoszlását rágcsálók hipotalamuszában. Leírta ezen glutamáterg idegsejtek részvételét az emberi hipotalamusz, és ezen belül, az emberi szaporodást irányító GnRH idegsejtek beidegzésében. Igazolta glutamáterg idegi fenotípus jegyeinek meglétét a szuprakiazmatikus mag egy elszórt neuron populációjában, továbbá korábban peptidergként megismert olyan neuronrendszerekben, melyek az agyalapi mirigy mellső és hátsó lebenyének működését szabályozzák. Tanulmányozta és endokrin fenotípus szerint azonosította a mellső hipofízis hámsejtjeiben is a glutamáterg neuronokra jellemző VGLUT1 és VGLUT2 enzim izoformákat és vizsgálta azok termelődésének szabályozását endokrin állatmodelleken. Elektronmikroszkópos vizsgálatokkal megállapította, hogy míg a neuroendokrin rendszerekben a VGLUT2 marker enzim mikrovezikulákhoz asszociált, addig az agyalapi mirigy mellső lebenyének hámsejtjeiben szekretoros granulumokban fordul elő. In situ hibridizáció használatával részletesen feltérképezte az egér hipotalamuszában az endokannabinoid érzékeny glutamáterg és GABAerg idegsejtek megoszlását. A projekthez kapcsolódó egyéb tanulmányokban új eredményeket szolgáltatott a reprodukció és az ösztrogén szignalizáció hipotalamikus és agykérgi mechanizmusainak jobb megértéséhez is. | Using this grant support, the research group described the topographic distribution of neurons that use glutamate (and contain one of the two major isoforms of vesicular glutamate transporter enzymes, VGLUT1 and VGLUT2) as synaptic transmitter in the rodent hypothalamus. They described the contribution of glutamatergic neurons to the innervation of the human hypothalamus and specifically, its GnRH neurons. They provided evidence for the occurrence of scattered glutamatergic neurons in the suprachiasmatic nucleus and in parvi- and magnocellular neurons known to regulate the anterior and posterior pituitary lobes solely via peptidergic mechanisms. They characterized epithelial cells in the anterior pituitary that express the VGLUT1 and VGLUT2 enzyme isoforms and studied the regulation of these enzymes under different endocrine challenges. They used electron microscopy and established that the glutamatergic marker enzyme VGLUT2 is associated with small-clear synaptic vesicles in neuroendocrine neuronal cells of the hypothalamus and with dense-core vesicles in glutamatergic endothelial cells of the adenohypophysis. They provided a detalied in situ hybridization map on the distribution of endocannabinoid-sensitive (CB1 mRNA expressing) hypothalamic neurons that exhibit glutamatergic and GABAergic phenotypes. In other studies linked to the project they provided new data about the regulation of reproduction and about estrogen signaling in the hypothalamus and the cerebral cortex

Action potential propagation and synchronisation in myelinated axons

With the advent of advanced MRI techniques it has become possible to study axonal white matter non-invasively and in great detail. Measuring the various parameters of the longrange connections of the brain opens up the possibility to build and refine detailed models of large-scale neuronal activity. One particular challenge is to find a mathematical description of action potential propagation that is sufficiently simple, yet still biologically plausible to model signal transmission across entire axonal fibre bundles. We develop a mathematical framework in which we replace the Hodgkin-Huxley dynamics by a spike-diffuse-spike model with passive sub-threshold dynamics and explicit, threshold-activated ion channel currents. This allows us to study in detail the influence of the various model parameters on the action potential velocity and on the entrainment of action potentials between ephaptically coupled fibres without having to recur to numerical simulations. Specifically, we recover known results regarding the influence of axon diameter, node of Ranvier length and internode length on the velocity of action potentials. Additionally, we find that the velocity depends more strongly on the thickness of the myelin sheath than was suggested by previous theoretical studies. We further explain the slowing down and synchronisation of action potentials in ephaptically coupled fibres by their dynamic interaction. In summary, this study presents a solution to incorporate detailed axonal parameters into a whole-brain modelling framework

Efficient implicit solvers for models of neuronal networks

We introduce economical versions of standard implicit ODE solvers that are specifically tailored for the efficient and accurate simulation of neural networks. The specific versions of the ODE solvers proposed here, allow to achieve a significant increase in the efficiency of network simulations, by reducing the size of the algebraic system being solved at each time step, a technique inspired by very successful semi-implicit approaches in computational fluid dynamics and structural mechanics. While we focus here specifically on Explicit first step, Diagonally Implicit Runge Kutta methods (ESDIRK), similar simplifications can also be applied to any implicit ODE solver. In order to demonstrate the capabilities of the proposed methods, we consider networks based on three different single cell models with slow-fast dynamics, including the classical FitzHugh-Nagumo model, a Intracellular Calcium Concentration model and the Hindmarsh-Rose model. Numerical experiments on the simulation of networks of increasing size based on these models demonstrate the increased efficiency of the proposed methods

Endokannabinoid szignalizáció szerepe a reprodukció hypothalamikus szintű szabályozásában = Endocannabinoid signaling in hypothalamic regulation of reproduction

A szaporodás idegrendszeri szabályozásában kulcs szerepet tölt be a gonadotropin-releasing hormone (GnRH) neuronrendszer. A rendszer működését perifériás hormonhatások és más agyi neuronhálózatok szabályozzák. Multidiszciplináris megközelítés alkalmazásával tanulmányoztuk a GnRH neuronrendszer kapcsolatait és szignál transzdukciós mechanizmusait, különös tekintettel a retrográd endokannabinoid szignalizáció szabályozó szerepére. Kísérleti eredményeinkről 24 tudományos közleményben adtunk számot, 96 összesített impakt értékkel. Feltártuk a hypothalamus kannabinoid receptor 1 (CB1) hírvivő RNS-t termelő idegsejtjeinek lokalizációját, valamint azok glutamáterg és GABA-erg fenotípusait. Igazoltuk, hogy a GnRH sejteken végződő GABA tartalmú idegvégződések CB1-t tartalmaznak, valamint bebizonyítottuk, hogy a GnRH idegsejtekből felszabaduló endokannabinoidok befolyásolják a GABA közvetítette információ átadást a GnRH neuronok felé. Feltártuk a ghrelin és endokannabinoid szignalizációs útvonalak kapcsolt jellegét a hypothalamusban. Igazoltuk a humán GnRH idegsejtek glutamát- és GABA-erg beidegzését. A GnRH neuronrendszer kisspeptinerg afferensei vonatkozásában új regulációs adatokat szolgáltattunk. Vizsgáltuk az ösztrogén szignalizáció szerepét a GnRH idegsejtek működésében, valamint az agykéregben. A GnRH idegsejtek működésének elmélyültebb tanulmányozására matematikai modellt alkottunk. Összegezve, eredményeink a reprodukció szabályozásának új mechanizmusait tárták fel. | Gonadotropin-releasing hormone (GnRH)-synthesizing neurons play a pivotal role in the central regulation of reproduction. Their operation depends on signaling by peripheral hormones and interactions with other neuronal circuits. By means of a multidisciplinary approach, the networking and signal transduction mechanisms of GnRH neurons were studied, with special reference to a putative retrograde endogenous cannabinoid signaling mechanism. The research results were published in 24 original papers representing a cumulative impact value of 96. Specifically, we mapped the hypothalamic distribution of cannabinoid receptor 1 (CB1) mRNA-expressing neurons and their GABA- and glutamatergic phenotypes, proved the presence of CB1 in neuronal afferents of GnRH neurons and characterized the impact of endocannabinoids liberated from GnRH neurons on the GABA-ergic signal transduction to GnRH cells. We provided evidence for the coupled nature of the ghrelin and the endocannabinoid signaling mechanisms. New GABA- and glutamatergic afferents of human GnRH neurons were also identified. In addition, novel regulatory mechanisms executed by kisspeptinergic circuits upon GnRH cells were revealed. We elucidated further characteristics of estradiol feedback effects to GnRH and cortical neurons. We established a mathematical model for the better understanding of GnRH cell performance. Collectively, our results shed light on novel mechanisms regulating reproduction at the hypothalamic level

Integrating Network and Intrinsic Changes in GnRH Neuron Control of Ovulation

Infertility affects 15-20% of couples; failure to ovulate is a common cause. Ovulation is triggered when estradiol switches from negative feedback action on the pituitary and hypothalamus to positive feedback, initiating a surge of gonadotropin-releasing hormone (GnRH) secretion that causes a surge of luteinizing hormone (LH) release, which triggers ovulation. Our understanding of the neurobiological changes underlying the switch from negative to positive feedback is incomplete. High levels of estradiol are essential, and in rodents, the LH surge tends to occur at a specific time-of-day. GnRH neurons, however, do not express the estrogen receptor required for feedback, thus estradiol-sensitive afferents likely convey estradiol information to GnRH neurons. We hypothesized that GnRH neurons switch from negative to positive feedback by integrating multiple changes to their synaptic inputs and intrinsic properties. To investigate the neurobiological mechanisms that underlie surge generation, daily GnRH/LH surges can be induced by ovariectomy and estradiol replacement (OVX+E) in rodents. GnRH neuron activity and release are increased in the afternoon (positive feedback) and decreased in the morning (negative feedback). No time-of-day changes are observed in OVX mice that do not receive an estradiol implant. Previous studies using the daily surge model have elucidated multiple GnRH neuron intrinsic and fast-synaptic changes during the switch from negative to positive feedback. It is unclear which if any of these changes are necessary for increasing GnRH firing rate during positive feedback. We hypothesized that changes to GnRH neuron intrinsic properties culminate in an increase in excitability to current steps during positive feedback and a decrease in excitability during negative feedback. To our surprise, changes to GnRH neuron ionic conductances rendered GnRH neurons more excitable during positive feedback relative to all other groups, but changes to ionic conductances between OVX and negative feedback animals had no net effect on GnRH neuron excitability. A mathematical model using a novel application of a rigorous parameter estimation method predicted that multiple, redundant combinations of changes to GnRH intrinsic conductances can produce the firing response in positive feedback. Changes to two interdependent parameters that determine the kinetics of voltage-gated potassium channels accounted for the similar neural responses during negative feedback and in OVX mice. Although enhancing GnRH neuron excitability is expected to increase firing rate during positive feedback, it is unclear if this change is necessary or if the concomitant increase is fast-synaptic transmission is sufficient for increasing GnRH neural activity during positive feedback. To test this, we used dynamic clamp to inject positive feedback, negative feedback, and OVX postsynaptic conductance trains into cells from positive feedback, negative feedback, and OVX mice. Positive feedback conductance trains were more effective in initiating spiking in cells from all three animal models relative to negative feedback and OVX trains. However, the positive feedback train elicited twice the number of action potentials from positive feedback mice relative to those from all other groups. Lastly, we extended our previous work to measure changes to GnRH neuron excitability and GABAergic inputs during the estrous cycle. We demonstrated that GABA postsynaptic current frequency and GnRH neuron excitability are both increased during positive feedback (proestrus) relative to negative feedback (diestrus) and strikingly similar to changes observed in the daily surge model. Collectively, these studies demonstrate that GnRH neurons act to integrate and amplify multiple signals to increase firing rate during the preovulatory surge.PHDMol & Integrtv Physiology PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155290/1/adamsce_1.pd

Action potential propagation and synchronisation in myelinated axons

With the advent of advanced MRI techniques it has become possible to study axonal white matter non-invasively and in great detail. Measuring the various parameters of the long-range connections of the brain opens up the possibility to build and refine detailed models of large-scale neuronal activity. One particular challenge is to find a mathematical description of action potential propagation that is sufficiently simple, yet still biologically plausible to model signal transmission across entire axonal fibre bundles. We develop a mathematical framework in which we replace the Hodgkin-Huxley dynamics by a spike-diffuse-spike model with passive sub-threshold dynamics and explicit, threshold-activated ion channel currents. This allows us to study in detail the influence of the various model parameters on the action potential velocity and on the entrainment of action potentials between ephaptically coupled fibres without having to recur to numerical simulations. Specifically, we recover known results regarding the influence of axon diameter, node of Ranvier length and internode length on the velocity of action potentials. Additionally, we find that the velocity depends more strongly on the thickness of the myelin sheath than was suggested by previous theoretical studies. We further explain the slowing down and synchronisation of action potentials in ephaptically coupled fibres by their dynamic interaction. In summary, this study presents a solution to incorporate detailed axonal parameters into a whole-brain modelling framework

Calibration of ionic and cellular cardiac electrophysiology models

© 2020 The Authors. WIREs Systems Biology and Medicine published by Wiley Periodicals, Inc. Cardiac electrophysiology models are among the most mature and well-studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods > Computational Methods Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models