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. 2016 Jul 1;121(1):268-78.
doi: 10.1152/japplphysiol.00790.2015. Epub 2016 Jun 9.

Feed-forward and reciprocal inhibition for gain and phase timing control in a computational model of repetitive cough

Teresa Pitts  1 , Kendall F Morris  2 , Lauren S Segers  2 , Ivan Poliacek  3 , Melanie J Rose  4 , Bruce G Lindsey  2 , Paul W Davenport  4 , Dena R Howland  5 , Donald C Bolser  4
Affiliations

Feed-forward and reciprocal inhibition for gain and phase timing control in a computational model of repetitive cough

Teresa Pitts et al. J Appl Physiol (1985). .

Abstract

We investigated the hypothesis, motivated in part by a coordinated computational cough network model, that second-order neurons in the nucleus tractus solitarius (NTS) act as a filter and shape afferent input to the respiratory network during the production of cough. In vivo experiments were conducted on anesthetized spontaneously breathing cats. Cough was elicited by mechanical stimulation of the intrathoracic airways. Electromyograms of the parasternal (inspiratory) and rectus abdominis (expiratory) muscles and esophageal pressure were recorded. In vivo data revealed that expiratory motor drive during bouts of repetitive coughs is variable: peak expulsive amplitude increases from the first cough, peaks about the eighth or ninth cough, and then decreases through the remainder of the bout. Model simulations indicated that feed-forward inhibition of a single second-order neuron population is not sufficient to account for this dynamic feature of a repetitive cough bout. When a single second-order population was split into two subpopulations (inspiratory and expiratory), the resultant model produced simulated expiratory motor bursts that were comparable to in vivo data. However, expiratory phase durations during these simulations of repetitive coughing had less variance than those in vivo. Simulations in which reciprocal inhibitory processes between inspiratory-decrementing and expiratory-augmenting-late neurons were introduced exhibited increased variance in the expiratory phase durations. These results support the prediction that serial and parallel processing of airway afferent signals in the NTS play a role in generation of the motor pattern for cough.

Keywords: airway protection; computational modeling; cough; in vivo; inhibition.

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Figures

Fig. 1.
Fig. 1.
Schematic representation of version 2 of the pontomedullary respiratory network model. Cell populations are represented as rectangles and are labeled with the cells' respiratory discharge pattern; populations are vertically grouped according to anatomic location. Vertical lines to the right of each group represent the output(s) of each cell population; note that the lines and cell populations have the same names. Cell input is delineated at line intersections marked with circles containing a symbol to indicate the synapse type (excitatory or inhibitory; see Key); the horizontal lines point to the cell population receiving the input. The synapse type (excitatory or inhibitory) is indicated by small circles on cell population (see Key). For example, VRC IE neurons inhibit the VRC I-Dec and I-Aug cells and are excited by, among others, NTS Pump + cells. Nerve outputs are represented by the bottom four populations in the VRC column. Sensory receptors are shown at bottom left; they influence the cell populations via second-order populations (left column). Populations and connections present in the base model [as published by Poliaček et al. (48)] that were removed or transformed in version 2 are indicated by dashed lines. Added connections and populations are shown in white and highlighted in gray. NTS, nucleus tractus solitarius; VRC, ventral respiratory column; PRG, pontine respiratory group. Aug and Dec, neurons with augmenting or decrementing activity patterns, respectively, during the indicated phase (I, inspiratory; E, expiratory) of maximum firing rate. ELM, expiratory laryngeal motoneurons; EI and IE, neurons with a peak firing rate during the E-I and I-E phase transitions, respectively; ILM, inspiratory laryngeal motoneurons; Lumbar, lumbar nerve; NBM, nonbreathing modulated neurons; Phrenic, phrenic nerve; Pump cells, excitatory or inhibitory neurons excited by pulmonary stretch receptors and most active with lung inflation; BS, bulbospinal; FF, feed-forward; Exp., expiratory.
Fig. 2.
Fig. 2.
In vivo representative example demonstrating the dynamic expiratory drive over a cough epoch (dashed line) and the change in CTE2 (gray boxes). Note the change in the CTE2, with longer E2 durations occurring at the end of the cough bout.
Fig. 3.
Fig. 3.
Evolution of the computational model. A: base model of Poliaček et al. (48). Note the limited lumbar (expiratory) amplitude variance and the stable CTE2 during coughing. B: version 1 model changes. A feed-forward inhibitory population was added, which accommodated over the cough epoch. C: version 2 model changes. 1. The second-order population was split into inspiratory (INSP) and expiratory (EXP) populations, which received identical excitatory and feed-forward inhibitory (FF INHIB) inputs. 2. A second E-Aug late population was added, which increased the expiratory control over the initiation of inspiration, increasing the CTE2 at the end of the cough epoch (gray boxes). This figure does not indicate anatomical locations for the proposed neuronal populations, although E-Aug late neurons are subsets of E-Aug neurons located in the Botzinger complex in vivo (53, 54). Second-order neurons are located in the NTS (14, 15), and feed-forward populations are also proposed to be located in this area of the dorsal medulla.
Fig. 4.
Fig. 4.
Poincaré plots (n vs. n + 1; see methods) of cough phase duration over model evolutions and overlaid on in vivo experiments represented by gray symbols. Data for CTI are represented as triangles, CTE1 as squares, and CTE2 as circles. The base model from Poliaček et al. (48) is in blue, version 1 is in green, and version 2 is in red. Version 1 had instances of CTE2 equal to zero, which is not seen during in vivo repetitive coughing. Note the greater similarity of points generated from in vivo data and version 2 of the computational model. This and the absence of zero-duration E2 phases suggest an improved model.
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