Rhythmicity is ubiquitous in the brain, important for many high-order functions such as consciousness, attention, perception, and memory (Başar and Düzgün, 2016; Başar and Güntekin, 2008; Colgin, 2016; Hanslmayr et al., 2016; Kiehn, 2016; Neske, 2015; Palva and Palva, 2018; Paton and Buonomano, 2018), as well as vital rhythmic motor behaviors including chewing, locomotion, and breathing (Grillner and El Manira, 2015; Kiehn, 2016; Narayanan and DiLeone, 2017; Ramirez and Baertsch, 2018a; Wyart, 2018; Nakamura et al., 2004). Rhythms generated by the brain are diverse, as are the underlying rhythm generating network- and cellular-level mechanisms (Paton and Buonomano, 2018). Indeed, rhythmicity can occur on time scales ranging from milliseconds to days (Golombek et al., 2014). However, most neuronal rhythms must be flexible, able to increase or decrease the rate of oscillation or the degree of synchronization to match changes in physiological or cognitive demands (Brittain et al., 2014; Ramirez and Baertsch, 2018b). Thus, understanding principles that allow dynamic control of rhythm generating networks may provide important insights into the regulation of diverse brain functions.

For half a century, investigations of the networks and cellular mechanisms that generate breathing have provided valuable, generalizable insights into the origins and control of neural rhythmicity (Del Negro et al., 2018; Feldman and Kam, 2015; Ramirez and Baertsch, 2018b; Wyman, 1977; Cohen, 1981; Ezure, 1990; Long and Duffin, 1986; Milsom, 1991). Inspiration, the dominant phase of breathing in mammals (Jenkin and Milsom, 2014), is generated by a spatially dynamic network located bilaterally along the ventrolateral medulla (Baertsch et al., 2019). A region that is both necessary and sufficient for inspiration, the pre-Bötzinger Complex (preBötC), is autorhythmic and forms the core of this network (Smith et al., 1991; Tan et al., 2008; Vann et al., 2018). Like many rhythmic networks, a critical characteristic of the preBötC is that the frequency of its output is dynamic - the rate of breathing changes during for example sleep/wake states, exercise, environmental challenges, and orofacial behaviors such as feeding and vocalization (Moore et al., 2013; Ramirez et al., 2016). Excitatory and inhibitory inputs from other brain regions, as well as neuromodulation, can potently facilitate or depress the frequency of breathing (Doi and Ramirez, 2008; Zuperku et al., 2017). Yet, elucidating how these influences alter the network- and cellular-level rhythm generating mechanisms within the preBötC remains a challenge (Dick et al., 2018).

Glutamatergic synaptic interactions among preBötC interneurons allow this sparsely connected network (Carroll and Ramirez, 2013; Schwab et al., 2010) to periodically synchronize and are therefore obligatory for rhythmogenesis (Ge and Feldman, 1998). However, if left unrestrained, feed-forward excitation in the network leads to hyper synchronization during inspiratory bursts, which subsequently causes a prolonged period of reduced network excitability (Baertsch et al., 2018; Kottick and Del Negro, 2015). This refractory phase delays the onset of the next inspiratory burst and, as a result, the frequency of breathing becomes very slow. Inhibitory interactions within the preBötC are critical for limiting synchronization during bursts (Harris et al., 2017) and reducing the subsequent refractoriness of the network (Baertsch et al., 2018). Indeed, roughly 40% of inspiratory preBötC neurons are inhibitory (GABAergic and/or glycinergic) (Oke et al., 2018; Winter et al., 2009; Morgado-Valle et al., 2010) and by regulating the excitability of glutamatergic neurons during inspiratory bursts, these neurons play an important role in controlling breathing frequency.

However, controlling the inspiratory burst itself is not the only mechanism that regulates the inspiratory rhythm. During the time between bursts, referred to as the inter-burst interval (IBI), recurrent excitatory synaptic connections (Del Negro et al., 2018) and ectopic bursting of individual preBötC neurons (Ramirez et al., 2004) are thought to give rise to a gradual increase in network excitability that drives the onset of the next burst. In this model, spontaneous spiking activity in a small subset of excitatory preBötC neurons begins to percolate stochastically through the network, gradually recruiting more spiking activity among interconnected excitatory neurons (Kam et al., 2013b). During this percolation phase, activation of membrane voltage- and calcium-dependent conductances in an increasing number of neurons causes the excitation to become exponential, culminating in an inspiratory burst (Del Negro et al., 2010; Ramirez et al., 2016). The gradual increase in spiking activity during this phase, or ‘pre-inspiratory ramp’, is thought to be primarily mediated by a subset of glutamatergic preBötC interneurons that have enhanced excitability. Derived from V0-lineage precursors, these neurons express the transcription factor developing brain homeobox one protein (Dbx1) during development (referred to here as ‘Dbx1 neurons’) (Bouvier et al., 2010; Gray et al., 2010; Picardo et al., 2013; Wu et al., 2017). How this process of recurrent excitation may contribute to the dynamic regulation of inspiratory frequency is not well understood.

Here, we examine network- and cellular-level changes in the inspiratory rhythm generator that underlie dynamic frequency responses to the excitatory neuromodulator substance P (SP). A member of the tachykinin neuropeptide family, SP is a key mediator of many physiological and neurobiological processes (Mantyh, 2002). For breathing, SP regulates the stability of the respiratory rhythm (Ben-Mabrouk and Tryba, 2010; Yeh et al., 2017) as well as respiratory responses to hypoxia (Chen et al., 1990; Ptak et al., 2002). The endogenous receptor for SP, neurokinin one receptor (NK₁R), is expressed on only ~5–7% of CNS neurons (Mantyh, 2002), but is enriched in the preBötC (Gray et al., 1999; Schwarzacher et al., 2011). SP binding to NK₁R causes excitation of preBötC neurons through coupling with voltage-independent cation channels (Hayes and Del Negro, 2007; Ptak et al., 2009), including sodium leak channel, non-selective (Nalcn). Disruption of this ion channel causes pathological respiratory instability (Yeh et al., 2017). SP also promotes robust facilitation of inspiratory frequency (Gray et al., 1999; Peña and Ramirez, 2004b). Therefore, SP is an ideal tool to explore the changes in network activity that regulate breathing stability and underlie the dynamic control of breathing frequency.

By combining electrophysiological, optogenetic, and pharmacological techniques, we find that SP differentially influences the refractory and recurrent excitation phases of the inspiratory rhythm through cell-type-specific effects. We conclude that phase-specific and differential modulation of excitatory and inhibitory network interactions is a key mechanism that allows the frequency of this vital rhythmogenic network to be dynamically controlled.

The rhythm generating network that produces breathing movements must constantly adjust to changing metabolic demands and also adapt to overlapping volitional and reflexive behaviors (Feldman et al., 2013; Ramirez and Baertsch, 2018b). Unravelling mechanisms that support this dynamic control may improve our understanding of disorders of the nervous system that destabilize breathing, such as Parkinson’s disease, Rett syndrome, sudden infant death syndrome, congenital central hypoventilation syndrome, multiple-systems atrophy, and amyotrophic lateral sclerosis (Oliveira et al., 2019; Ramirez et al., 2018; Schwarzacher et al., 2011; Katz et al., 2009; Moreira et al., 2016). Changes in neuromodulatory systems within the brainstem have been linked to many of these and other respiratory control disorders (Doi and Ramirez, 2008; Viemari et al., 2005). Here, we introduce the concept that distinct phases of the rhythmogenic process can be differentially regulated by neuromodulation to dynamically control the frequency and stability of breathing (Figure 9).

Figure 9

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During inspiration, each burst is assembled stochastically via heterogeneous interactions among a combination of intertwined synaptic and intrinsic properties (Ramirez and Baertsch, 2018b). Although exclusively excitatory synaptic interactions (Kam et al., 2013a) or intrinsic bursting mechanisms (Peña et al., 2004a) may be able to produce rhythm in isolation, this is unlikely to occur under normal conditions since these properties interact strongly. To the contrary, the combination of excitatory and inhibitory synaptic interactions with intrinsic bursting properties, known as the ‘rhythmogenic triangle’ (Ramirez and Baertsch, 2018b), is critical for the flexibility of this dynamic network (Ramirez et al., 2004; Rubin and Smith, 2019; Smith et al., 2000; Richter and Smith, 2014). Neuromodulators play important roles in regulating both synaptic and intrinsic bursting properties, perhaps best demonstrated in invertebrate model systems. In these networks, neuromodulators can inhibit or strengthen synaptic interactions (Harris-Warrick et al., 1998; Marder et al., 2014; Nusbaum et al., 2001), as well as induce or suppress intrinsic bursting properties (Elson and Selverston, 1992; Flamm and Harris-Warrick, 1986; Zhang and Harris-Warrick, 1994). These important principles also apply to the aminergic and peptidergic modulation of the mammalian preBötC network (Doi and Ramirez, 2010). For example, in the absence of synaptic interactions, the activity of both I_NaP and I_CAN-dependent autonomous bursting neurons in the preBötC is modulated by SP (Ben-Mabrouk and Tryba, 2010; Peña and Ramirez, 2004b). Although it is known that NK₁R expressing preBötC neurons are important for rhythmogenesis (Gray et al., 2001; Guyenet and Wang, 2001), it is unclear how these modulatory effects regulate the frequency and stability of the inspiratory rhythm. Evidence suggests that NK₁R is primarily expressed on excitatory neurons (Gray et al., 1999), including those with pre-I activity (Guyenet and Wang, 2001). However, if SP activates excitatory neurons during inspiratory bursts and facilitates their intrinsic bursting properties, one would expect a prolongation of the refractory phase, which would limit rather than promote a frequency increase. Instead, we found that SP does not affect the spiking frequency of either excitatory or inhibitory preBötC neurons during the inspiratory burst phase (Figures 3, 4 and 5). Because local synaptic interactions among interconnected excitatory and inhibitory neurons is a fundamental characteristic of the preBötC network (Ramirez and Baertsch, 2018b), we infer that the changes in neuronal spiking, or lack thereof, that we observed under current clamp conditions reflect local synaptic interactions within the preBötC. Accordingly, we suggest that the balance of excitatory and inhibitory inputs to preBötC neurons is not changed by SP, although voltage-clamp experiments under visual control would be warranted to more directly assess this balance. Nonetheless, an important question is: How is the activity of inspiratory neurons, and presumably the balance of excitation and inhibition, maintained during bursts despite the excitatory effects of SP?

One possibility is that excitatory inputs originating from outside the preBötC could be reduced to prevent SP from causing hyperexcitation during inspiratory bursts. Inspiratory neurons located rostral to the preBötC contribute to the dynamic regulation of breathing frequency. Indeed, transverse brainstem slice preparations that lack rostral inspiratory neurons generate less robust rhythms with shorter refractory periods and faster frequencies than horizontal slices (Baertsch et al., 2019). On the other hand, recruitment of these neurons is associated with increased excitation during inspiratory bursts, a prolonged refractory phase, and consequently slower breathing frequencies (Baertsch et al., 2019). However, under normal conditions, inhibition restrains the rhythm generating ability of these rostral neurons allowing faster, more dynamic breathing. Interestingly, we found that many inhibitory neurons rostral of the preBötC exhibit tonic spiking activity during the IBI, which was moderately increased by SP. In contrast, rostral excitatory neurons did not spike during the IBI, and spiking was only slightly suppressed during inspiratory bursts (Figure 8). Thus, unlike their role in modulating other dynamic breathing responses such as gasping (Baertsch et al., 2019), the contribution of rostral inspiratory neurons to SP-induced frequency increase seems to be relatively modest. Indeed, NK₁R is preferentially expressed in the preBötC compared to other regions of the ventral respiratory column (Gray et al., 1999), which may explain the relatively heterogeneous effects of SP on the activity of rostral neurons (Figure 7).

Another possibility is that synchronization of excitatory neurons is reduced during inspiration, thereby preventing hyperexcitation during the burst phase in response to SP. The assembly of inspiratory bursts is known to be stochastic, which is observed as significant variability in the cycle-to-cycle onset time of inspiratory neuron bursts relative to the preBötC population (Carroll and Ramirez, 2013). This variability in activation order, or onset jitter, is related, at least in part, to the sparse functional connectivity within the preBötC network (Carroll and Ramirez, 2013). We found that SP increases the variability of burst onset times among inspiratory neurons located within and rostral to the preBötC. This increase in timing variability was most pronounced among excitatory neurons, specifically those that lack pre-I activity (Figure 6). Thus, although other mechanisms such as depletion of excitatory synaptic vesicles (Rubin et al., 2009) may also contribute, we propose that increased jitter among excitatory neurons impairs synchronization of the network and helps maintain the balance of excitation and inhibition during inspiratory bursts in the presence of SP. Future computational modeling studies will be important to directly test this hypothesis.

The inspiratory burst phase is followed by a period of reduced excitability in the preBötC network. This refractory period is thought to arise from a combination of presynaptic depression (Kottick and Del Negro, 2015) and activation of slow hyperpolarizing current(s) (Baertsch et al., 2018; Krey et al., 2010) in glutamatergic Dbx1 neurons during inspiratory bursts. Indeed, refractoriness is maximal immediately following the inspiratory burst followed by a gradual recovery of excitability (Figure 1), likely as vesicles are recycled and hyperpolarizing conductances are inactivated. This refractory phase manifests experimentally as a period during which the probability of evoking an ectopic inspiratory burst via optogenetic stimulation of Dbx1 neurons is reduced (Figure 1A). However, the refractory period is not absolute as it can be overcome if the stimulus is of sufficient strength (Vann et al., 2018). Using a stimulus procedure consistent with previous reports (Baertsch et al., 2018; Baertsch et al., 2019; Kottick and Del Negro, 2015), we found that SP does not change the duration of the refractory period. This finding is consistent with our demonstration that excitatory neurons do not show an increased activation during the inspiratory burst phase (Figures 3B and 4B). Importantly, the minimum duration of spontaneous inter-burst intervals continues to be restrained by the refractory period in SP (Figure 1). Thus, refractory mechanisms remain an important determinant of breathing frequency in the presence of this neuromodulator. This may be functionally important to prevent excitatory neuromodulators such as SP from driving the respiratory network out of its physiological frequency range. Indeed, neuronal networks must not only be capable of dynamically regulating their frequency, but they must also be able to maintain stability in spite of heterogeneous, intrinsically variable cellular components that receive converging inputs from numerous excitatory neuromodulators (Marder et al., 2014).

As the inspiratory network transitions out of the refractory phase, some excitatory neurons, presumably with more depolarized basal membrane potentials (Butera et al., 1999; Smith et al., 2000), begin to fire action potentials prior to the onset of the next synchronized population burst (Figure 3). The associated synaptic interactions, which may particularly involve Dbx1 neurons (Wang et al., 2014), are thought to stochastically percolate through a network of recurrently connected excitatory neurons (Del Negro et al., 2018). This network-based process, sometimes referred to as the ‘group pacemaker’ hypothesis (Del Negro and Hayes, 2008), likely occurs simultaneously with changes in intrinsic membrane properties such as I_NaP (Butera et al., 1999; Ramirez et al., 2016; Smith et al., 2000), that together lead to a gradual build-up of excitability within the network. Consequently, the spiking activity of excitatory pre-I neurons between inspiratory bursts exhibits a ramping pattern. We found that the magnitude and slope of this pre-inspiratory ramp, as well as the number of excitatory neurons that have pre-inspiratory activity, is increased by SP. These recruited excitatory neurons also exhibit a pronounced pre-inspiratory ramp, suggesting that they participate in the recurrent excitation process. Together, these effects likely increase the rate of recurrent excitation during this percolation phase to accelerate the onset of the next burst (Figure 9), underling the increase in breathing frequency induced by SP. Moreover, we found that, on a cycle-to-cycle basis, the slope of pre-inspiratory ramp activity is inversely related to the inter-burst interval. Thus, variations in the stochastic percolation of excitation during this phase seem to predict variability in the duration between inspiratory bursts. Our data suggest that, by increasing the rate of recurrent excitation, this process becomes more consistent and breathing irregularity is reduced. We conclude that the dual effects of SP on breathing frequency and stability are primarily a consequence of its effects on the percolation phase of the inspiratory rhythm.

Based on our collective results, we propose a conceptual framework for inspiratory rhythm generation in which three distinct phases, the burst phase, refractory phase, and percolation phase, can be differentially modulated to influence breathing dynamics and stability (Figure 9). Although these three phases seem to be intrinsic to the inspiratory rhythm generator, we anticipate that non-inspiratory neurons and/or respiratory related brain regions outside the preBötC (e.g. Anderson et al., 2016; Fu et al., 2019; Huckstepp et al., 2018; Richter and Smith, 2014) can provide important regulatory control over each phase of the inspiratory cycle, leading to predictable phase-dependent effects on rhythm dynamics. This concept may provide a foundation for understanding breathing in the context of many other physiological and pathological conditions (Bright et al., 2018; Saito et al., 2001), and it may also serve as a guide for understanding the dynamic control rhythm generating networks in general.

All data generated during this study are included in the manuscript and supporting files. Source data files have been provided for all figures.

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Author details

1. Nathan A Baertsch

   Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology

   For correspondence

   nathan.baertsch@seattlechildrens.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1589-5575

2. Jan-Marino Ramirez

   1. Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, United States
   2. Department of Neurological Surgery, University of Washington School of Medicine, Seattle, United States

   Contribution

   Supervision, Funding acquisition

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-5626-3999

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