Emergent Network Topology within the Respiratory Rhythm-Generating Kernel Evolved In Silico

Temporal variations in the pattern of breathing: techniques, sources, and applications to translational sciences

The breathing process possesses a complex variability caused in part by the respiratory central pattern generator in the brainstem; however, it also arises from chemical and mechanical feedback control loops, network reorganization and network sharing with nonrespiratory motor acts, as well as inputs from cortical and subcortical systems. The notion that respiratory fluctuations contain hidden information has prompted scientists to decipher respiratory signals to better understand the fundamental mechanisms of respiratory pattern generation, interactions with emotion, influences on the cortical neuronal networks associated with cognition, and changes in variability in healthy and disease-carrying individuals. Respiration can be used to express and control emotion. Furthermore, respiration appears to organize brain-wide network oscillations via cross-frequency coupling, optimizing cognitive performance. With the aid of information theory-based techniques and machine learning, the hidden information can be translated into a form usable in clinical practice for diagnosis, emotion recognition, and mental conditioning.

Configuration and dynamics of dominant inspiratory multineuronal activity patterns during eupnea and gasping generation in vitro

The pre-Bötzinger complex (preBötC), located within the ventral respiratory column, produces inspiratory bursts in varying degrees of synchronization/amplitude. This wide range of population burst patterns reflects the flexibility of the preBötC neurons, which is expressed in variations in the onset/offset times of their activations and their activity during the population bursts, with respiratory neurons exhibiting a large cycle-to-cycle timing jitter both at the population activity onset and at the population activity peak, suggesting that respiratory neurons are stochastically activated before and during the inspiratory bursts. However, it is still unknown whether this stochasticity is maintained while evaluating the coactivity of respiratory neuronal ensembles. Moreover, the preBötC topology also remains unknown. In this study, by simultaneously recording tens of preBötC neurons and using coactivation analysis during the inspiratory periods, we found that the preBötC has a scale-free configuration (mixture of not many highly connected nodes, hubs, with abundant poorly connected elements) exhibiting the rich-club phenomenon (hubs more likely interconnected with each other). PreBötC neurons also produce multineuronal activity patterns (MAPs) that are highly stable and change during the hypoxia-induced reconfiguration. Moreover, preBötC contains a coactivating core network shared by all its MAPs. Finally, we found a distinctive pattern of sequential coactivation of core network neurons at the beginning of the inspiratory periods, indicating that, when evaluated at the multicellular level, the coactivation of respiratory neurons seems not to be stochastic.NEW & NOTEWORTHY By means of multielectrode recordings of preBötC neurons, we evaluated their configuration in normoxia and hypoxia, finding that the preBötC exhibits a scale-free configuration with a rich-club phenomenon. preBötC neurons produce multineuronal activity patterns that are highly stable but change during hypoxia. The preBötC contains a coactivating core network that exhibit a distinctive pattern of coactivation at the beginning of inspirations. These results reveal some network basis of inspiratory rhythm generation and its reconfiguration during hypoxia.

Neural network model of an amphibian ventilatory central pattern generator

The neuronal multiunit model presented here is a formal model of the central pattern generator (CPG) of the amphibian ventilatory neural network, inspired by experimental data from Pelophylax ridibundus. The kernel of the CPG consists of three pacemakers and two follower neurons (buccal and lung respectively). This kernel is connected to a chain of excitatory and inhibitory neurons organized in loops. Simulations are performed with Izhikevich-type neurons. When driven by the buccal follower, the excitatory neurons transmit and reorganize the follower activity pattern along the chain, and when driven by the lung follower, the excitatory and inhibitory neurons of the chain fire in synchrony. The additive effects of synaptic inputs from the pacemakers on the buccal follower account for (1) the low frequency buccal rhythm, (2) the intra-burst high frequency oscillations, and (3) the episodic lung activity. Chemosensitivity to acidosis is implemented by an increase in the firing frequency of one of the pacemakers. This frequency increase leads to both a decrease in the buccal burst frequency and an increase in the lung episode frequency. The rhythmogenic properties of the model are robust against synaptic noise and pacemaker jitter. To validate the rhythm and pattern genesis of this formal CPG, neurograms were built from simulated motoneuron activity, and compared with experimental neurograms. The basic principles of our model account for several experimental observations, and we suggest that these principles may be generic for amphibian ventilation.

Cell Type-Dependent Activation Sequence During Rhythmic Bursting in the PreBötzinger Complex in Respiratory Rhythmic Slices From Mice

Spontaneous respiratory rhythmic burst activity can be preserved in the preBötzinger Complex (preBötC) of rodent medullary transverse slices. It is known, that the activation sequence of inspiratory neurons in the preBötC stochastically varies from cycle to cycle. To test whether the activation timing of an inspiratory neuron depends on its neurotransmitter, we performed calcium imaging of preBötC neurons using double-transgenic mice expressing EGFP in GlyT2⁺ neurons and tdTomato in GAD65⁺ neurons. Five types of inspiratory neurons were identified using the fluorescence protein expression and the maximum cross-correlation coefficient between neuronal calcium fluctuation and field potential. Regarding the activation sequence, irregular type putative excitatory (GlyT2^-/GAD65^-) neurons and irregular type glycinergic (GlyT2⁺/GAD65^-) neurons tended to be activated early, while regular type putative excitatory neurons, regular type glycinergic neurons tended to be activated later. In conclusion, the different cell types define a general framework for the stochastically changing activation sequence of inspiratory neurons in the preBötC.

A computational model of the respiratory network challenged and optimized by data from optogenetic manipulation of glycinergic neurons

The topology of the respiratory network in the brainstem has been addressed using different computational models, which help to understand the functional properties of the system. We tested a neural mass model by comparing the result of activation and inhibition of inhibitory neurons in silico with recently published results of optogenetic manipulation of glycinergic neurons [Sherman, et al. (2015) Nat Neurosci 18:408]. The comparison revealed that a five-cell type model consisting of three classes of inhibitory neurons [I-DEC, E-AUG, E-DEC (PI)] and two excitatory populations (pre-I/I) and (I-AUG) neurons can be applied to explain experimental observations made by stimulating or inhibiting inhibitory neurons by light sensitive ion channels.