A network model of respiratory rhythmogenesis

Ogilvie, Malcolm D.; Gottschalk, Allan; Anders, K; Richter, D. W.; Pack, Allan I.

doi:10.1152/ajpregu.1992.263.4.r962

Cited by 61 publications

(56 citation statements)

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“…The network interactions among rVRG and BötC populations (ramp-I, early-I, and late-I) and between these populations and BötC populations define the basic circuitry for IOS mechanism. The late-I population plays the key role in the initiation of inspiratory offswitching (Cohen, 1979;Ogilvie et al, 1986;Cohen et al, 1993;Richter, 1996;Rybak et al, 1997b) by providing inhibition of the early-I population. The latter disinhibits the post-I population that completes switching to expiration and makes it irreversible.…”

Section: Computational Modelingmentioning

confidence: 99%

Respiratory Rhythm Entrainment by Somatic Afferent Stimulation

Potts¹,

Rybak²,

Paton³

2005

J. Neurosci.

118

131

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Section: Computational Modelingmentioning

confidence: 99%

Respiratory Rhythm Entrainment by Somatic Afferent Stimulation

Potts¹,

Rybak²,

Paton³

2005

J. Neurosci.

118

131

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“…Inhibitory synaptic interactions are vital, as they both impose the voltage changes required for initiation of endogenous respiratory bursting and control the burst pattern. Another indispensable function of these interactions is to terminate respiratory bursts during transitions among the distinct phases of the respiratory cycle that are determined by activity of antagonistic neurons (8)(9)(10). This critical process of burst termination is largely controlled by glycine receptors (GlyRs), since dysfunction or deletion of inhibitory glycinergic transmission abolishes regular breathing (11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

Serotonin receptor 1A–modulated phosphorylation of glycine receptor α3 controls breathing in mice

Manzke¹,

Niebert²,

Koch³

et al. 2010

J. Clin. Invest.

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Rhythmic breathing movements originate from a dispersed neuronal network in the medulla and pons. Here, we demonstrate that rhythmic activity of this respiratory network is affected by the phosphorylation status of the inhibitory glycine receptor α3 subtype (GlyRα3), which controls glutamatergic and glycinergic neuronal discharges, subject to serotonergic modulation. Serotonin receptor type 1A-specific (5-HTR 1A -specific) modulation directly induced dephosphorylation of GlyRα3 receptors, which augmented inhibitory glycine-activated chloride currents in HEK293 cells coexpressing 5-HTR 1A and GlyRα3. The 5-HTR 1A -GlyRα3 signaling pathway was distinct from opioid receptor signaling and efficiently counteracted opioid-induced depression of breathing and consequential apnea in mice. Paradoxically, this rescue of breathing originated from enhanced glycinergic synaptic inhibition of glutamatergic and glycinergic neurons and caused disinhibition of their target neurons. Together, these effects changed respiratory phase alternations and ensured rhythmic breathing in vivo. GlyRα3-deficient mice had an irregular respiratory rhythm under baseline conditions, and systemic 5-HTR 1A activation failed to remedy opioid-induced respiratory depression in these mice. Delineation of this 5-HTR 1A -GlyRα3 signaling pathway offers a mechanistic basis for pharmacological treatment of opioid-induced apnea and other breathing disturbances caused by disorders of inhibitory synaptic transmission, such as hyperekplexia, hypoxia/ischemia, and brainstem infarction.

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“…In the respiratory and locomotor networks, catalogs of 5-20 different firing patterns and their hypothesized connections have been incorporated into increasingly complex models of rhythm generation with the idea that they can potentially explain how these networks generate rhythmicity (Molkov et al 2010;Ogilvie et al 1992;Rubin et al 2009;Rybak et al 1997aRybak et al , 1997bRybak et al , 1997cRybak et al , 2007Rybak et al , 2008Sherwood et al 2011;Smith et al 2007). Yet, despite the reliance of current models on the concept of different physiological categories, the quantitative basis for these categories has not been rigorously established, nor have the criteria that specify inclusion into one or another cell class been described in detail.…”

mentioning

confidence: 99%

Patterns of inspiratory phase-dependent activity in the in vitro respiratory network

Carroll¹,

Viemari

Ramirez

2013

Journal of Neurophysiology

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Carroll MS, Viemari JC, Ramirez JM. Patterns of inspiratory phase-dependent activity in the in vitro respiratory network. J Neurophysiol 109: 285-295, 2013. First published October 17, 2012 doi:10.1152/jn.00619.2012.-Mechanistic descriptions of rhythmogenic neural networks have often relied on ball-and-stick diagrams, which define interactions between functional classes of cells assumed to be reasonably homogenous. Application of this formalism to networks underlying respiratory rhythm generation in mammals has produced increasingly intricate models that have generated significant insight, but the underlying assumption that individual cells within these network fall into distinct functional classes has not been rigorously tested. In the present study we used multiunit extracellular recording in the in vitro pre-Bötzinger complex to identify and characterize the rhythmic activity of 951 cells. Inspiratory phasedependent activity was estimated for all cells, and the data set as a whole was analyzed with principal component analysis, nonlinear dimensionality reduction, and hierarchical clustering techniques. None of these techniques revealed categorically distinct functional cell classes, indicating instead that the behavior of these cells within the network falls along several continua of spiking behavior. neuronal networks; pre-Bötzinger complex; respiration; cell types RHYTHMIC ACTIVITY IS UBIQUITOUS in neural systems, from the slow rhythm generated by the suprachiasmatic nuclei that synchronizes mammalian circadian cycles (Dibner et al. 2010), to the broad frequency spectra covered by various oscillations generated within cortical networks (Buzsaki and Draguhn 2004;Tort et al. 2010;Wang 2010). Many of the underlying rules of rhythm generation have been established in neural circuits that generate rhythmic motor behaviors (Goulding 2009;Grillner 2006;Marder and Calabrese 1996). These motor activities are typically reciprocal in nature (e.g., expiration and inspiration, extension and flexion, protraction and retraction) and are often described by ball-and-stick 1 schematics that assume specific connectivity (sticks) between different types of cells or cell populations (balls). In these models each cell possesses functionally discrete properties firing in distinct phases with respect to a given global activity pattern (Guertin 2009; Selverston 2010). Ball-and-stick representations have been particularly powerful in invertebrate neuronal networks (Antonsen and Edwards 2003), because the firing and connectivity properties of physiologically and anatomically identified individual neurons could be reproducibly characterized across different individuals of the same species (Jing et al. 2010;Katz et al. 2010;Marder and Calabrese 1996;Newcomb et al. 2012; Ramirez, 1998;White and Nusbaum 2011). One of the many important lessons learned from these small invertebrate networks is that even in the case of a truly identified neuron, electrophysiological measures of single-cell properties do not suffice to uniquely specify a ...

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A network model of respiratory rhythmogenesis

Cited by 61 publications

References 0 publications

Respiratory Rhythm Entrainment by Somatic Afferent Stimulation

Respiratory Rhythm Entrainment by Somatic Afferent Stimulation

Serotonin receptor 1A–modulated phosphorylation of glycine receptor α3 controls breathing in mice

Patterns of inspiratory phase-dependent activity in the in vitro respiratory network

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