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Mechanisms Leading to Rhythm Cessation in the Respiratory PreBötzinger Complex Due to Piecewise Cumulative Neuronal Deletions(1,2,3).

Song H, Hayes JA, Vann NC, Drew LaMar M, Del Negro CA - eNeuro (2015)

Bottom Line: When the recruitment rate drops below 1 neuron/ms the network stops spontaneous rhythmic activity.Neurons that play pre-eminent roles in rhythmogenesis include those that commence spiking during the quiescent phase between respiratory bursts and those with a high number of incoming synapses, which both play key roles in recruitment, i.e., recurrent excitation leading to network bursts.This study provides a theoretical framework for the operating mechanism of mammalian central pattern generator networks and their susceptibility to loss-of-function in the case of disease or neurodegeneration.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Applied Science, The College of William & Mary , Williamsburg, Virginia 23187-8795.

ABSTRACT
The mammalian breathing rhythm putatively originates from Dbx1-derived interneurons in the preBötzinger complex (preBötC) of the ventral medulla. Cumulative deletion of ∼15% of Dbx1 preBötC neurons in an in vitro breathing model stops rhythmic bursts of respiratory-related motor output. Here we assemble in silico models of preBötC networks using random graphs for structure, and ordinary differential equations for dynamics, to examine the mechanisms responsible for the loss of spontaneous respiratory rhythm and motor output measured experimentally in vitro. Model networks subjected to cellular ablations similarly discontinue functionality. However, our analyses indicate that model preBötC networks remain topologically intact even after rhythm cessation, suggesting that dynamics coupled with structural properties of the underlying network are responsible for rhythm cessation. Simulations show that cumulative cellular ablations diminish the number of neurons that can be recruited to spike per unit time. When the recruitment rate drops below 1 neuron/ms the network stops spontaneous rhythmic activity. Neurons that play pre-eminent roles in rhythmogenesis include those that commence spiking during the quiescent phase between respiratory bursts and those with a high number of incoming synapses, which both play key roles in recruitment, i.e., recurrent excitation leading to network bursts. Selectively ablating neurons with many incoming synapses impairs recurrent excitation and stops spontaneous rhythmic activity and motor output with lower ablation tallies compared with random deletions. This study provides a theoretical framework for the operating mechanism of mammalian central pattern generator networks and their susceptibility to loss-of-function in the case of disease or neurodegeneration.

No MeSH data available.


Latency rank order of all constituent neurons in the network at four specific time points in a simulation. Top (red), The running time spike histogram for a random neuron deletion simulation. a, b, and c indicate three cycles leading to network-wide bursts (at time points 16, 557, and 814 s, respectively). d, The time after the last burst. The middle shows the latency rank order (defined in Results) for cycles a, b, and c, and after rhythm termination (d) plotted versus cycle time (in seconds). The dotted line indicates latency rank order (unitless) of the neuron after which the curve inflects upward, leading to a network-wide burst. The lower panel shows the same data as the middle, but where cycle time is limited to 0–4 s, emphasizing the similarity of b, c, and d.
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Figure 8: Latency rank order of all constituent neurons in the network at four specific time points in a simulation. Top (red), The running time spike histogram for a random neuron deletion simulation. a, b, and c indicate three cycles leading to network-wide bursts (at time points 16, 557, and 814 s, respectively). d, The time after the last burst. The middle shows the latency rank order (defined in Results) for cycles a, b, and c, and after rhythm termination (d) plotted versus cycle time (in seconds). The dotted line indicates latency rank order (unitless) of the neuron after which the curve inflects upward, leading to a network-wide burst. The lower panel shows the same data as the middle, but where cycle time is limited to 0–4 s, emphasizing the similarity of b, c, and d.

Mentions: Figure 8 shows constituent neurons sorted by pre-inspiratory latency for the same network realization as in Figures 3–5. Latency rank order (earliest activating neurons obtain lower rank) is plotted versus activation cycle time, i.e., when the first action potential occurs during the inter-burst interval. Latency for constituent neurons 1 to ∼210 remains relatively fixed (Fig. 8a,b–d, bottom). Individual network cycles are not identical (Carroll and Ramirez, 2013; Carroll et al., 2013; Kam et al., 2013b) but pre-inspiratory latency for a neuron generally remains within a few tens of milliseconds from cycle to cycle. An early-spiking neuron does not convert to a late-spiking one, and vice versa.


Mechanisms Leading to Rhythm Cessation in the Respiratory PreBötzinger Complex Due to Piecewise Cumulative Neuronal Deletions(1,2,3).

Song H, Hayes JA, Vann NC, Drew LaMar M, Del Negro CA - eNeuro (2015)

Latency rank order of all constituent neurons in the network at four specific time points in a simulation. Top (red), The running time spike histogram for a random neuron deletion simulation. a, b, and c indicate three cycles leading to network-wide bursts (at time points 16, 557, and 814 s, respectively). d, The time after the last burst. The middle shows the latency rank order (defined in Results) for cycles a, b, and c, and after rhythm termination (d) plotted versus cycle time (in seconds). The dotted line indicates latency rank order (unitless) of the neuron after which the curve inflects upward, leading to a network-wide burst. The lower panel shows the same data as the middle, but where cycle time is limited to 0–4 s, emphasizing the similarity of b, c, and d.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596029&req=5

Figure 8: Latency rank order of all constituent neurons in the network at four specific time points in a simulation. Top (red), The running time spike histogram for a random neuron deletion simulation. a, b, and c indicate three cycles leading to network-wide bursts (at time points 16, 557, and 814 s, respectively). d, The time after the last burst. The middle shows the latency rank order (defined in Results) for cycles a, b, and c, and after rhythm termination (d) plotted versus cycle time (in seconds). The dotted line indicates latency rank order (unitless) of the neuron after which the curve inflects upward, leading to a network-wide burst. The lower panel shows the same data as the middle, but where cycle time is limited to 0–4 s, emphasizing the similarity of b, c, and d.
Mentions: Figure 8 shows constituent neurons sorted by pre-inspiratory latency for the same network realization as in Figures 3–5. Latency rank order (earliest activating neurons obtain lower rank) is plotted versus activation cycle time, i.e., when the first action potential occurs during the inter-burst interval. Latency for constituent neurons 1 to ∼210 remains relatively fixed (Fig. 8a,b–d, bottom). Individual network cycles are not identical (Carroll and Ramirez, 2013; Carroll et al., 2013; Kam et al., 2013b) but pre-inspiratory latency for a neuron generally remains within a few tens of milliseconds from cycle to cycle. An early-spiking neuron does not convert to a late-spiking one, and vice versa.

Bottom Line: When the recruitment rate drops below 1 neuron/ms the network stops spontaneous rhythmic activity.Neurons that play pre-eminent roles in rhythmogenesis include those that commence spiking during the quiescent phase between respiratory bursts and those with a high number of incoming synapses, which both play key roles in recruitment, i.e., recurrent excitation leading to network bursts.This study provides a theoretical framework for the operating mechanism of mammalian central pattern generator networks and their susceptibility to loss-of-function in the case of disease or neurodegeneration.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Applied Science, The College of William & Mary , Williamsburg, Virginia 23187-8795.

ABSTRACT
The mammalian breathing rhythm putatively originates from Dbx1-derived interneurons in the preBötzinger complex (preBötC) of the ventral medulla. Cumulative deletion of ∼15% of Dbx1 preBötC neurons in an in vitro breathing model stops rhythmic bursts of respiratory-related motor output. Here we assemble in silico models of preBötC networks using random graphs for structure, and ordinary differential equations for dynamics, to examine the mechanisms responsible for the loss of spontaneous respiratory rhythm and motor output measured experimentally in vitro. Model networks subjected to cellular ablations similarly discontinue functionality. However, our analyses indicate that model preBötC networks remain topologically intact even after rhythm cessation, suggesting that dynamics coupled with structural properties of the underlying network are responsible for rhythm cessation. Simulations show that cumulative cellular ablations diminish the number of neurons that can be recruited to spike per unit time. When the recruitment rate drops below 1 neuron/ms the network stops spontaneous rhythmic activity. Neurons that play pre-eminent roles in rhythmogenesis include those that commence spiking during the quiescent phase between respiratory bursts and those with a high number of incoming synapses, which both play key roles in recruitment, i.e., recurrent excitation leading to network bursts. Selectively ablating neurons with many incoming synapses impairs recurrent excitation and stops spontaneous rhythmic activity and motor output with lower ablation tallies compared with random deletions. This study provides a theoretical framework for the operating mechanism of mammalian central pattern generator networks and their susceptibility to loss-of-function in the case of disease or neurodegeneration.

No MeSH data available.