Noise-induced properties of active dendrites

Dendrites play an essential role in the integration of highly fluctuating input in vivo into neurons across all nervous systems. Yet, they are often studied under conditions where inputs to dendrites are sparse. The dynamic properties of active dendrites facing in vivo–like fluctuating input thus remain elusive. In this paper, we uncover dynamics in a canonical model of a dendritic compartment with active calcium channels, receiving in vivo–like fluctuating input. In a single-compartment model of the active dendrite with fast calcium activation, we show noise-induced nonmonotonic behavior in the relationship of the membrane potential output, and mean input emerges. In contrast, noise can induce bistability in the input–output relation in the system with slowly activating calcium channels. Both phenomena are absent in a noiseless condition. Furthermore, we show that timescales of the emerging stochastic bistable dynamics extend far beyond a deterministic system due to stochastic switching between the solutions. A numerical simulation of a multicompartment model neuron shows that in the presence of in vivo–like synaptic input, the bistability uncovered in our analysis persists. Our results reveal that realistic synaptic input contributes to sustained dendritic nonlinearities, and synaptic noise is a significant component of dendritic input integration.

Dynamics Analysis of Firing Patterns in Pre-Bötzinger Complex Neurons Model

Pre-Bötzinger complex (PBC) neurons located in mammalian brain are the necessary conditions to produce respiratory rhythm, which has been widely verified experimentally and numerically. At present, one of the two different types of bursting mechanisms found in PBC mainly depends on the calcium-activated of non-specific cation current (ICaN). In order to study the influence of ICaN and stimulus current Iexc in PBC inspiratory neurons, a single compartment model was simplified, and firing patterns of the model was discussed by using stability theory, bifurcation analysis, fast, and slow decomposition technology combined with numerical simulation. Under the stimulation of different somatic applied currents, the firing behavior of neurons are studied and exhibit multiple mix bursting patterns, which is helpful to further understand the mechanism of respiratory rhythms of PBC neurons.

Excitation Properties of Computational Models of Unmyelinated Peripheral Axons

Biophysically-based computational models of nerve fibers are important tools for designing electrical stimulation therapies, investigating drugs that affect ion channels, and studying diseases that affect neurons. Although peripheral nerves are primarily composed of unmyelinated axons (i.e., C-fibers), most modeling efforts focused on myelinated axons. We implemented the single-compartment model of vagal afferents from Schild et al. 1994 and extended the model into a multi-compartment axon, presenting the first cable model of a C-fiber vagal afferent. We also implemented the updated parameters from Schild and Kunze 1997. We compared the responses of these novel models to three published models of unmyelinated axons (Rattay and Aberham 1993; Sundt et al. 2015; Tigerholm et al. 2014) and to experimental data from single fiber recordings. Comparing Schild et al. 1994 and 1997 revealed that differences in rest potential and action potential shape were driven by changes in maximum conductances rather than changes in sodium channel dynamics. Comparing the five model axons, the conduction speeds and strength-duration responses were largely within expected ranges, but none of the models captured the experimental threshold recovery cycle-including a complete absence of late subnormality in the models-and their action potential shapes varied dramatically. The Tigerholm et al. 2014 model best reproduced the experimental data, but these modeling efforts make clear that additional data are needed to parameterize and validate future models of autonomic C-fibers.

SAT-738 Sources of Error in Estimation of Cortisol Half-Life Using Conventional, Single-Compartment Model: Bias Due to Variation in CBG Concentration

Most reports of cortisol half-life in the literature report a range of 90-130 min, which results are based on descriptive model that assumes mono-exponential decay of a single, total cortisol compartment. Free cortisol half-life has been similarly assessed using a descriptive single compartment model (1). However, the descriptive model is not physiologic in view of the rapid exchange between protein-bound and free cortisol compartments and evidence that metabolic elimination is restricted to the free cortisol compartment. In the present study, we sought to explore potential limitations of the descriptive, single-compartment model for cortisol elimination by assessing the influence of CBG concentration ([CBG]) on cortisol half-life estimates obtained using the descriptive model. We studied the influence of [CBG] and other variables on descriptive cortisol half-life using a Monte Carlo simulation of cortisol concentration decay curves developed using data from healthy controls (1). Total cortisol concentration ([TF]) curves were generated on the basis of 4 predictor variables: (i) [CBG], (ii) albumin concentration, (iii) [TF] at time zero following iv bolus (total cortisol at time 0, y-intercept), and (iv) free cortisol half-life central to a mechanistic (dynamic, 3-compartment) model (2). Simulations used a multivariable normal distribution and selected means, SDs, and correlation structure among these 4 variables in healthy controls. After generation of a series of cortisol decay curves (n=1000), half-lives for total and free cortisol were solved using the conventional (descriptive, single-compartment) model. The influence of predictor variables on conventional half-life estimates were assessed using standardized beta (STB) coefficients, which represent change in the SD of the outcome (numerator, i.e. total or free cortisol half-life obtained by descriptive model) for each SD change in a predictor (denominator) in a multivariable context (3). For total cortisol half-life (descriptive model) STBs were 0.91 ([CBG]), 0.73 (free cortisol half-life), -0.37 (y-intercept), and 0.04 ([albumin]) (all P<0.001). For free cortisol half-life (descriptive model), STBs were 0.98 ([CBG]), 0.73 (free cortisol half-life), -0.78 (y-intercept), and 0.11 [albumin]) (all P<0.001). We conclude that the conventional descriptive model for estimation of cortisol has significant limitations, including inaccuracy and systematic bias related to the influence of CBG concentration on half-life estimates. By inference, a similar bias confounds interpretation of the half-life obtained using conventional single-compartment model of other hormones associated with high-affinity serum binding proteins. References: (1) Perogamvros et al. Clin Endo 2011;74:30-36, (2) Keenan et al. Am J Physiol Endocrinol Metab 2004;287:E652-E661 (3) Dorin et al., J Endocrinol Soc 2017 July;1(7):945-56.

Analysis of Species Characteristics of Laboratory Animals in Reaction to Hyperbaric Environmental Conditions

The aim of this work is to determine the dynamics of nitrogen saturation in small laboratory animals. Nitrogen was chosen as a model gas in this study because of its availability and characteristics, as it is not metabolised and is subject to passive diffusion. By subjecting different species of animals to hyperbaric exposures of increasing time and pressure, the study aimed to identify how rapid a decompression was possible to achieve an outcome that saw 50% of the animals surviving the ensuing acute decompression sickness. The basic parameters of hyperbaric exposure - pressure and time - made it possible to describe the saturation phenomena on the basis of partial saturation periods and to show whether a small animal organism can be considered as a single compartment model.

Using injury cost functions from a predictive single-compartment model to assess the severity of mechanical ventilator-induced lung injuries

Identifying safe ventilation patterns for patients with acute respiratory distress syndrome remains challenging because of the delicate balance between gas exchange and selection of ventilator settings to prevent further ventilator-induced lung injury (VILI). Accordingly, this work seeks to link ventilator settings to graded levels of VILI to identify injury cost functions that predict injury by using a computational model to process pressures and flows measured at the airway opening. Pressure-volume loops were acquired over the course of ~2 h of mechanical ventilation in four different groups of BALB/c mice. A cohort of these animals were subjected to an injurious bronchoalveolar lavage before ventilation. The data were analyzed with a single-compartment model that predicts recruitment/derecruitment and tissue distension at each time step in measured pressure-volume loops. We compared several injury cost functions to markers of VILI-induced blood-gas barrier disruption. Of the cost functions considered, we conclude that mechanical power dissipation and strain heterogeneity are the best at distinguishing between graded levels of injury and are good candidates for forecasting the development of VILI. NEW & NOTEWORTHY This work uses a predictive single-compartment model and injury cost functions to assess graded levels of mechanical ventilator-induced lung injury. The most promising measures include strain heterogeneity and mechanical power dissipation.