Modeling the effects of extracellular potassium on bursting properties in pre-Bötzinger complex neurons

There are many types of neurons that intrinsically generate rhythmic bursting activity, even when isolated, and these neurons underlie several specific motor behaviors. Rhythmic neurons that drive the inspiratory phase of respiration are located in the medullary pre-Bötzinger Complex (pre-BötC). However, it is not known if their rhythmic bursting is the result of intrinsic mechanisms or synaptic interactions. In many cases, for bursting to occur, the excitability of these neurons needs to be elevated. This excitation is provided in vitro (e.g. in slices), by increasing extracellular potassium concentration (K[subscript out]) well beyond physiologic levels. Elevated K[subscript out] shifts the reversal potentials for all potassium currents including the potassium component of leakage to higher values. However, how an increase in K[subscript out], and the resultant changes in potassium currents, induce bursting activity, have yet to be established. Moreover, it is not known if the endogenous bursting induced in vitro is representative of neural behavior in vivo. Our modeling study examines the interplay between K[subscript out], excitability, and selected currents, as they relate to endogenous rhythmic bursting. Starting with a Hodgkin-Huxley formalization of a pre-BötC neuron, a potassium ion component was incorporated into the leakage current, and model behaviors were investigated at varying concentrations of K[subscript out]. Our simulations show that endogenous bursting activity, evoked in vitro by elevation of K[subscript out], is the result of a specific relationship between the leakage and voltage-dependent, delayed rectifier potassium currents, which may not be observed at physiological levels of extracellular potassium.National Institutes of Health (U.S.) (National Center for Complementary and Integrative Health (U.S). Grant R01 AT008632)National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.). Grant R01 NS069220

Analysis and Modeling of Ensemble Recordings from Respiratory Pre-Motor Neurons Indicate Changes in Functional Network Architecture after Acute Hypoxia

We have combined neurophysiologic recording, statistical analysis, and computational modeling to investigate the dynamics of the respiratory network in the brainstem. Using a multielectrode array, we recorded ensembles of respiratory neurons in perfused in situ rat preparations that produce spontaneous breathing patterns, focusing on inspiratory pre-motor neurons. We compared firing rates and neuronal synchronization among these neurons before and after a brief hypoxic stimulus. We observed a significant decrease in the number of spikes after stimulation, in part due to a transient slowing of the respiratory pattern. However, the median interspike interval did not change, suggesting that the firing threshold of the neurons was not affected but rather the synaptic input was. A bootstrap analysis of synchrony between spike trains revealed that both before and after brief hypoxia, up to 45% (but typically less than 5%) of coincident spikes across neuronal pairs was not explained by chance. Most likely, this synchrony resulted from common synaptic input to the pre-motor population, an example of stochastic synchronization. After brief hypoxia most pairs were less synchronized, although some were more, suggesting that the respiratory network was transiently “rewired” after the stimulus. To investigate this hypothesis, we created a simple computational model with feed-forward divergent connections along the inspiratory pathway. Assuming that (1) the number of divergent projections was not the same for all presynaptic cells, but rather spanned a wide range and (2) that the stimulus increased inhibition at the top of the network; this model reproduced the reduction in firing rate and bootstrap-corrected synchrony subsequent to hypoxic stimulation observed in our experimental data

Multi-Scale Modeling of the Neural Control of Respiration

The generation of respiration in mammals begins in the lower brainstem where groups of neurons, that together comprise the respiratory central pattern generator (CPG), interact to produce a motor output that controls breathing. The pre-Bötzinger complex (pre-BötC) in the ventrolateral respiratory column (VRC) is believed to be a major contributor to rhythmic inspiratory activity that interacts with other neural compartments within the VRC as well as with other brainstem areas, including the pons. Though there has been a substantial push to understand the exact cellular and network mechanisms operating within the pre-BötC, as well as the way it is incorporated into the larger respiratory network, there is still much to be resolved. The overarching goal of the work presented in this dissertation is to contribute to our understanding of the neural control of respiration at several hierarchical levels. It is my hope that better insight into the complexities of these multiscale neural control mechanisms will provide a more complete framework for understanding various respiratory pathologies, and ultimately guide the development of novel therapies that will improve patient outcomes. I applied techniques from the fields of mathematics and computer science to develop computational models that reproduced results from electrophysiological recordings (done by our collaborators) and generated verifiable predictions. The scale of my modeling work encompasses the interaction of neurons in a single population, several interconnected populations of neurons that encompass the core of the mammalian respiratory network, and an integration of the respiratory network into a larger control system that includes afferent feedback loops. At each level I address specific, but related, topics that add to the general understanding of the neural control of respiration. The aims of my thesis address specific issues at each of the scales mentioned above. These issues may be summarized as follows: (i) the characteristic rhythmic bursting behavior observed in the pre-BötC, which was studied at the cellular levels with a particular interest in how this behavior impacts respiratory rhythmogenesis; (ii) a respiratory network connectome that defines interactions between several populations of neurons that together form the VRC, which produces an alternating pattern of inspiration, post-inspiration and expiration, and, how such a pattern may be affected by changes in chemical environment, e.g. elevated carbon dioxide or diminished oxygen concentrations; and (iii) the role of afferent feedback to the VRC, from the pons and lungs, which was studied in the context of respiratory phase switching mechanisms.Ph.D., Biomedical Engineering -- Drexel University, 201

Synaptically activated burst-generating conductances may underlie a group-pacemaker mechanism for respiratory rhythm generation in mammals

Breathing, chewing, and walking are critical life-sustaining behaviors in mammals that consist essentially of simple rhythmic movements. Breathing movements in particular involve the diaphragm, thorax, and airways but emanate from a network in the lower brain stem. This network can be studied in reduced preparations in vitro and using simplified mathematical models that make testable predictions. An iterative approach that employs both in vitro and in silico models argues against canonical mechanisms for respiratory rhythm in neonatal rodents that involve reciprocal inhibition and pacemaker properties. We present an alternative model in which emergent network properties play a rhythmogenic role. Specifically, we show evidence that synaptically activated burst-generating conductances-which are only available in the context of network activity-engender robust periodic bursts in respiratory neurons. Because the cellular burst-generating mechanism is linked to network synaptic drive we dub this type of system a group pacemaker. © 2010 Elsevier B.V

Muscarinic Modulation of Morphologically Identified Glycinergic Neurons in the Mouse PreBötzinger Complex

The cholinergic system plays an essential role in central respiratory control, but the underlying mechanisms remain elusive. We used whole-cell recordings in brainstem slices from juvenile mice expressing enhanced green fluorescent protein (EGFP) under the control of the glycine transporter type 2 (GlyT2) promoter, to examine muscarinic modulation of morphologically identified glycinergic neurons in the preBötzinger complex (preBötC), an area critical for central inspiratory rhythm generation. Biocytin-filled reconstruction of glycinergic neurons revealed that the majority of them had few primary dendrites and had axons arborized within their own dendritic field. Few glycinergic neurons had axon collaterals extended towards the premotor/motor areas or ran towards the contralateral preBötC, and had more primary dendrites and more compact dendritic trees. Spontaneously active glycinergic neurons fired regular spikes, or less frequently in a “burst-like” pattern at physiological potassium concentration. Muscarine suppressed firing in the majority of regular spiking neurons via M2 receptor activation while enhancing the remaining neurons through M1 receptors. Interestingly, rhythmic bursting was augmented by muscarine in a small group of glycinergic neurons. In contrast to its heterogeneous modulation of glycinergic neuronal excitability, muscarine generally depressed inhibitory and excitatory synaptic inputs onto both glycinergic and non-glycinergic preBötC neurons, with a stronger effect on inhibitory input. Notably, presynaptic muscarinic attenuation of excitatory synaptic input was dependent on M1 receptors in glycinergic neurons and on M2 receptors in non-glycinergic neurons. Additional field potential recordings of excitatory synaptic potentials in the M2 receptor knockout mice indicate that glycinergic and non-glycinergic neurons contribute equally to the general suppression by muscarine of excitatory activity in preBötC circuits. In conclusion, our data show that preBötC glycinergic neurons are morphologically heterogeneous, and differ in the properties of synaptic transmission and muscarinic modulation in comparison to non-glycinergic neurons. The dominant and cell-type-specific muscarinic inhibition of synaptic neurotransmission and spiking may contribute to central respiratory disturbances in high cholinergic states

Extending firing rate models to include ionic effects

Spiking models have been widely used to describe single neuron oscillation behaviors. However, these models can be quite complex so that in order to incorporate them in networks, one approach is to use so-called firing rate models where the dynamics of the neuron are reduced to the rate at which it fires when presented with a constant stimulus. In pathological conditions such as epilepsy or when the neurons are driven too strongly, they can stop firing due to a phenomenon known as depolarization block, which can come about due to the accumulation of potassium ions in the intracellular space. In the project, we used a well-known Wang-Buzsaki (WB) spiking model but also included an additional equation considering extracellular potassium effects. Given that the extracellular potassium effects is slow and synapses can be reasonably assumed as slow, we applied a slow-fast technique on the WB model and derived a firing rate model describing the synapse-potassium system qualitatively. The bifurcation of the reduced model suggests that the depolarization block threshold can be viewed as the homoclinic bifurcation in the synapse-potassium system, which would be depended upon the potassium sensitivity and the drift rate. In addition, we implement our firing rate model into a two-nearest neighbor spatial model. The spatial-temporal plots suggest our model behavior is consistent with experimental results. The synaptic connectivity has positive effects on seizure propagation and somewhat negative effects on synchronization. On the other hand, the potassium diffusion has a positive influence on synchronization. However, the influence of potassium sensitivity might be more complex. While the neurons under normal physiological conditions can be driven into the seizure-like oscillations in the network, the neurons under depolarization block seem to be a little bit more complicated and require further explanation