2 research outputs found
Modeling the differentiation of A- and C-type baroreceptor firing patterns
The baroreceptor neurons serve as the primary transducers of blood pressure
for the autonomic nervous system and are thus critical in enabling the body to
respond effectively to changes in blood pressure. These neurons can be
separated into two types (A and C) based on the myelination of their axons and
their distinct firing patterns elicited in response to specific pressure
stimuli. This study has developed a comprehensive model of the afferent
baroreceptor discharge built on physiological knowledge of arterial wall
mechanics, firing rate responses to controlled pressure stimuli, and ion
channel dynamics within the baroreceptor neurons. With this model, we were able
to predict firing rates observed in previously published experiments in both A-
and C-type neurons. These results were obtained by adjusting model parameters
determining the maximal ion-channel conductances. The observed variation in the
model parameters are hypothesized to correspond to physiological differences
between A- and C-type neurons. In agreement with published experimental
observations, our simulations suggest that a twofold lower potassium
conductance in C-type neurons is responsible for the observed sustained basal
firing, whereas a tenfold higher mechanosensitive conductance is responsible
for the greater firing rate observed in A-type neurons. A better understanding
of the difference between the two neuron types can potentially be used to gain
more insight into the underlying pathophysiology facilitating development of
targeted interventions improving baroreflex function in diseased individuals,
e.g. in patients with autonomic failure, a syndrome that is difficult to
diagnose in terms of its pathophysiology.Comment: Keywords: Baroreflex model, mechanosensitivity, A- and C-type
afferent baroreceptors, biophysical model, computational mode
Simulations of Myenteric Neuron Dynamics in Response to Mechanical Stretch
Background. Intestinal sensitivity to mechanical stimuli has been studied intensively in visceral pain studies. The ability to sense different stimuli in the gut and translate these to physiological outcomes relies on the mechanosensory and transductive capacity of intrinsic intestinal nerves. However, the nature of the mechanosensitive channels and principal mechanical stimulus for mechanosensitive receptors are unknown. To be able to characterize intestinal mechanoelectrical transduction, that is, the molecular basis of mechanosensation, comprehensive mathematical models to predict responses of the sensory neurons to controlled mechanical stimuli are needed. This study aims to develop a biophysically based mathematical model of the myenteric neuron with the parameters constrained by learning from existing experimental data. Findings. The conductance-based single-compartment model was selected. The parameters in the model were optimized by using a combination of hand tuning and automated estimation. Using the optimized parameters, the model successfully predicted the electrophysiological features of the myenteric neurons with and without mechanical stimulation. Conclusions. The model provides a method to predict features and levels of detail of the underlying physiological system in generating myenteric neuron responses. The model could be used as building blocks in future large-scale network simulations of intrinsic primary afferent neurons and their network