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.

1. 1. Anderson TM
   2. Garcia AJ
   3. Baertsch NA
   4. Pollak J
   5. Bloom JC
   6. Wei AD
   7. Rai KG
   8. Ramirez JM

   (2016) A novel excitatory network for the control of breathing

   Nature 536:76–80.

   https://doi.org/10.1038/nature18944

   • PubMed
   • Google Scholar
2. 1. Baertsch NA
   2. Baertsch HC
   3. Ramirez JM

   (2018) The interdependence of excitation and inhibition for the control of dynamic breathing rhythms

   Nature Communications 9:843.

   https://doi.org/10.1038/s41467-018-03223-x

   • PubMed
   • Google Scholar
3. 1. Baertsch NA
   2. Severs LJ
   3. Anderson TM
   4. Ramirez JM

   (2019) A spatially dynamic network underlies the generation of inspiratory behaviors

   PNAS 116:7493–7502.

   https://doi.org/10.1073/pnas.1900523116

   • PubMed
   • Google Scholar
4. 1. Barnes BJ
   2. Tuong CM
   3. Mellen NM

   (2007) Functional imaging reveals respiratory network activity during hypoxic and opioid challenge in the neonate rat tilted sagittal slab preparation

   Journal of Neurophysiology 97:2283–2292.

   https://doi.org/10.1152/jn.01056.2006

   • PubMed
   • Google Scholar
5. 1. Başar E
   2. Düzgün A

   (2016) Links of consciousness, perception, and memory by means of Delta oscillations of brain

   Frontiers in Psychology 7:275.

   https://doi.org/10.3389/fpsyg.2016.00275

   • PubMed
   • Google Scholar
6. 1. Başar E
   2. Güntekin B

   (2008) A review of brain oscillations in cognitive disorders and the role of neurotransmitters

   Brain Research 1235:172–193.

   https://doi.org/10.1016/j.brainres.2008.06.103

   • PubMed
   • Google Scholar
7. 1. Ben-Mabrouk F
   2. Tryba AK

   (2010) Substance P modulation of TRPC3/7 channels improves respiratory rhythm regularity and ICAN-dependent pacemaker activity

   European Journal of Neuroscience 31:1219–1232.

   https://doi.org/10.1111/j.1460-9568.2010.07156.x

   • PubMed
   • Google Scholar
8. 1. Bouvier J
   2. Thoby-Brisson M
   3. Renier N
   4. Dubreuil V
   5. Ericson J
   6. Champagnat J
   7. Pierani A
   8. Chédotal A
   9. Fortin G

   (2010) Hindbrain interneurons and axon guidance signaling critical for breathing

   Nature Neuroscience 13:1066–1074.

   https://doi.org/10.1038/nn.2622

   • PubMed
   • Google Scholar
9. 1. Bright FM
   2. Vink R
   3. Byard RW

   (2018) The potential role of substance P in brainstem homeostatic control in the pathogenesis of sudden infant death syndrome (SIDS)

   Neuropeptides 70:1–8.

   https://doi.org/10.1016/j.npep.2018.02.006

   • PubMed
   • Google Scholar
10. 1. Brittain JS
    2. Sharott A
    3. Brown P

    (2014) The highs and lows of beta activity in cortico-basal ganglia loops

    European Journal of Neuroscience 39:1951–1959.

    https://doi.org/10.1111/ejn.12574

    • PubMed
    • Google Scholar
11. 1. Butera RJ
    2. Rinzel J
    3. Smith JC

    (1999) Models of respiratory rhythm generation in the pre-Bötzinger complex. II. populations of coupled pacemaker neurons

    Journal of Neurophysiology 82:398–415.

    https://doi.org/10.1152/jn.1999.82.1.398

    • PubMed
    • Google Scholar
12. 1. Carroll MS
    2. Viemari JC
    3. Ramirez JM

    (2013) Patterns of inspiratory phase-dependent activity in the in vitro respiratory network

    Journal of Neurophysiology 109:285–295.

    https://doi.org/10.1152/jn.00619.2012

    • PubMed
    • Google Scholar
13. 1. Carroll MS
    2. Ramirez JM

    (2013) Cycle-by-cycle assembly of respiratory network activity is dynamic and stochastic

    Journal of Neurophysiology 109:296–305.

    https://doi.org/10.1152/jn.00830.2011

    • PubMed
    • Google Scholar
14. 1. Chen Z
    2. Hedner J
    3. Hedner T

    (1990) Substance P in the ventrolateral medulla oblongata regulates ventilatory responses

    Journal of Applied Physiology 68:2631–2639.

    https://doi.org/10.1152/jappl.1990.68.6.2631

    • PubMed
    • Google Scholar
15. 1. Cohen MI

    (1981) Central determinants of respiratory rhythm

    Annual Review of Physiology 43:91–104.

    https://doi.org/10.1146/annurev.ph.43.030181.000515

    • PubMed
    • Google Scholar
16. 1. Colgin LL

    (2016) Rhythms of the hippocampal network

    Nature Reviews Neuroscience 17:239–249.

    https://doi.org/10.1038/nrn.2016.21

    • PubMed
    • Google Scholar
17. 1. Del Negro CA
    2. Hayes JA
    3. Pace RW
    4. Brush BR
    5. Teruyama R
    6. Feldman JL

    (2010) Synaptically activated burst-generating conductances may underlie a group-pacemaker mechanism for respiratory rhythm generation in mammals

    Progress in Brain Research 187:111–136.

    https://doi.org/10.1016/B978-0-444-53613-6.00008-3

    • PubMed
    • Google Scholar
18. 1. Del Negro CA
    2. Funk GD
    3. Feldman JL

    (2018) Breathing matters

    Nature Reviews Neuroscience 19:351–367.

    https://doi.org/10.1038/s41583-018-0003-6

    • PubMed
    • Google Scholar
19. 1. Del Negro CA
    2. Hayes JA

    (2008) A 'group pacemaker' mechanism for respiratory rhythm generation

    The Journal of Physiology 586:2245–2246.

    https://doi.org/10.1113/jphysiol.2008.153627

    • PubMed
    • Google Scholar
20. 1. Dick TE
    2. Dutschmann M
    3. Feldman JL
    4. Fong AY
    5. Hülsmann S
    6. Morris KM
    7. Ramirez JM
    8. Smith JC
    9. Respiratory Neurobiology Consortium

    (2018) Facts and challenges in respiratory neurobiology

    Respiratory Physiology & Neurobiology 258:104–107.

    https://doi.org/10.1016/j.resp.2015.01.014

    • PubMed
    • Google Scholar
21. 1. Doi A
    2. Ramirez JM

    (2008) Neuromodulation and the orchestration of the respiratory rhythm

    Respiratory Physiology & Neurobiology 164:96–104.

    https://doi.org/10.1016/j.resp.2008.06.007

    • PubMed
    • Google Scholar
22. 1. Doi A
    2. Ramirez JM

    (2010) State-dependent interactions between excitatory neuromodulators in the neuronal control of breathing

    Journal of Neuroscience 30:8251–8262.

    https://doi.org/10.1523/JNEUROSCI.5361-09.2010

    • PubMed
    • Google Scholar
23. 1. Elson RC
    2. Selverston AI

    (1992) Mechanisms of gastric rhythm generation in the isolated stomatogastric ganglion of spiny lobsters: bursting pacemaker potentials, synaptic interactions, and muscarinic modulation

    Journal of Neurophysiology 68:890–907.

    https://doi.org/10.1152/jn.1992.68.3.890

    • PubMed
    • Google Scholar
24. 1. Ezure K

    (1990) Synaptic connections between medullary respiratory neurons and considerations on the genesis of respiratory rhythm

    Progress in Neurobiology 35:429–450.

    https://doi.org/10.1016/0301-0082(90)90030-K

    • PubMed
    • Google Scholar
25. 1. Feldman JL
    2. Del Negro CA
    3. Gray PA

    (2013) Understanding the rhythm of breathing: so near, yet so far

    Annual Review of Physiology 75:423–452.

    https://doi.org/10.1146/annurev-physiol-040510-130049

    • PubMed
    • Google Scholar
26. 1. Feldman JL
    2. Kam K

    (2015) Facing the challenge of mammalian neural microcircuits: taking a few breaths may help

    The Journal of Physiology 593:3–23.

    https://doi.org/10.1113/jphysiol.2014.277632

    • PubMed
    • Google Scholar
27. 1. Flamm RE
    2. Harris-Warrick RM

    (1986) Aminergic modulation in lobster stomatogastric ganglion. II. target neurons of dopamine, octopamine, and serotonin within the pyloric circuit

    Journal of Neurophysiology 55:866–881.

    https://doi.org/10.1152/jn.1986.55.5.866

    • PubMed
    • Google Scholar
28. 1. Fu C
    2. Shi L
    3. Wei Z
    4. Yu H
    5. Hao Y
    6. Tian Y
    7. Liu Y
    8. Zhang Y
    9. Zhang X
    10. Yuan F
    11. Wang S

    (2019) Activation of Phox2b-Expressing neurons in the nucleus tractus solitarii drives breathing in mice

    The Journal of Neuroscience 39:2837–2846.

    https://doi.org/10.1523/JNEUROSCI.2048-18.2018

    • PubMed
    • Google Scholar
29. 1. Ge Q
    2. Feldman JL

    (1998) AMPA receptor activation and phosphatase inhibition affect neonatal rat respiratory rhythm generation

    The Journal of Physiology 509:255–266.

    https://doi.org/10.1111/j.1469-7793.1998.255bo.x

    • PubMed
    • Google Scholar
30. 1. Golombek DA
    2. Bussi IL
    3. Agostino PV

    (2014) Minutes, days and years: molecular interactions among different scales of biological timing

    Philosophical Transactions of the Royal Society B: Biological Sciences 369:20120465.

    https://doi.org/10.1098/rstb.2012.0465

    • Google Scholar
31. 1. Gray PA
    2. Rekling JC
    3. Bocchiaro CM
    4. Feldman JL

    (1999) Modulation of respiratory frequency by peptidergic input to rhythmogenic neurons in the preBötzinger complex

    Science 286:1566–1568.

    https://doi.org/10.1126/science.286.5444.1566

    • PubMed
    • Google Scholar
32. 1. Gray PA
    2. Janczewski WA
    3. Mellen N
    4. McCrimmon DR
    5. Feldman JL

    (2001) Normal breathing requires preBötzinger complex neurokinin-1 receptor-expressing neurons

    Nature Neuroscience 4:927–930.

    https://doi.org/10.1038/nn0901-927

    • PubMed
    • Google Scholar
33. 1. Gray PA
    2. Hayes JA
    3. Ling GY
    4. Llona I
    5. Tupal S
    6. Picardo MC
    7. Ross SE
    8. Hirata T
    9. Corbin JG
    10. Eugenín J
    11. Del Negro CA

    (2010) Developmental origin of preBötzinger complex respiratory neurons

    Journal of Neuroscience 30:14883–14895.

    https://doi.org/10.1523/JNEUROSCI.4031-10.2010

    • PubMed
    • Google Scholar
34. 1. Grillner S
    2. El Manira A

    (2015) The intrinsic operation of the networks that make us locomote

    Current Opinion in Neurobiology 31:244–249.

    https://doi.org/10.1016/j.conb.2015.01.003

    • PubMed
    • Google Scholar
35. 1. Guyenet PG
    2. Wang H

    (2001) Pre-Bötzinger neurons with preinspiratory discharges "in vivo" express NK1 receptors in the rat

    Journal of Neurophysiology 86:438–446.

    https://doi.org/10.1152/jn.2001.86.1.438

    • PubMed
    • Google Scholar
36. 1. Hanslmayr S
    2. Staresina BP
    3. Bowman H

    (2016) Oscillations and episodic memory: addressing the synchronization/Desynchronization conundrum

    Trends in Neurosciences 39:16–25.

    https://doi.org/10.1016/j.tins.2015.11.004

    • PubMed
    • Google Scholar
37. 1. Harris KD
    2. Dashevskiy T
    3. Mendoza J
    4. Garcia AJ
    5. Ramirez JM
    6. Shea-Brown E

    (2017) Different roles for inhibition in the rhythm-generating respiratory network

    Journal of Neurophysiology 118:2070–2088.

    https://doi.org/10.1152/jn.00174.2017

    • PubMed
    • Google Scholar
38. 1. Harris-Warrick RM
    2. Johnson BR
    3. Peck JH
    4. Kloppenburg P
    5. Ayali A
    6. Skarbinski J

    (1998) Distributed effects of dopamine modulation in the crustacean pyloric network

    Annals of the New York Academy of Sciences 860:155–167.

    https://doi.org/10.1111/j.1749-6632.1998.tb09046.x

    • PubMed
    • Google Scholar
39. 1. Hayes JA
    2. Del Negro CA

    (2007) Neurokinin receptor-expressing pre-botzinger complex neurons in neonatal mice studied in vitro

    Journal of Neurophysiology 97:4215–4224.

    https://doi.org/10.1152/jn.00228.2007

    • PubMed
    • Google Scholar
40. 1. Huckstepp RTR
    2. Cardoza KP
    3. Henderson LE
    4. Feldman JL

    (2018) Distinct parafacial regions in control of breathing in adult rats

    PLOS ONE 13:e0201485.

    https://doi.org/10.1371/journal.pone.0201485

    • PubMed
    • Google Scholar
41. 1. Jenkin SE
    2. Milsom WK

    (2014) Expiration: breathing's other face

    Progress in Brain Research 212:131–147.

    https://doi.org/10.1016/B978-0-444-63488-7.00008-2

    • PubMed
    • Google Scholar
42. 1. Kam K
    2. Worrell JW
    3. Janczewski WA
    4. Cui Y
    5. Feldman JL

    (2013a) Distinct inspiratory rhythm and pattern generating mechanisms in the preBötzinger complex

    Journal of Neuroscience 33:9235–9245.

    https://doi.org/10.1523/JNEUROSCI.4143-12.2013

    • PubMed
    • Google Scholar
43. 1. Kam K
    2. Worrell JW
    3. Ventalon C
    4. Emiliani V
    5. Feldman JL

    (2013b) Emergence of population bursts from simultaneous activation of small subsets of preBötzinger complex inspiratory neurons

    Journal of Neuroscience 33:3332–3338.

    https://doi.org/10.1523/JNEUROSCI.4574-12.2013

    • PubMed
    • Google Scholar
44. 1. Katz DM
    2. Dutschmann M
    3. Ramirez JM
    4. Hilaire G

    (2009) Breathing disorders in rett syndrome: progressive neurochemical dysfunction in the respiratory network after birth

    Respiratory Physiology & Neurobiology 168:101–108.

    https://doi.org/10.1016/j.resp.2009.04.017

    • PubMed
    • Google Scholar
45. 1. Kiehn O

    (2016) Decoding the organization of spinal circuits that control locomotion

    Nature Reviews Neuroscience 17:224–238.

    https://doi.org/10.1038/nrn.2016.9

    • PubMed
    • Google Scholar
46. 1. Kottick A
    2. Martin CA
    3. Del Negro CA

    (2017) Fate mapping neurons and Glia derived from Dbx1-expressing progenitors in mouse preBötzinger complex

    Physiological Reports 5:e13300.

    https://doi.org/10.14814/phy2.13300

    • PubMed
    • Google Scholar
47. 1. Kottick A
    2. Del Negro CA

    (2015) Synaptic depression influences Inspiratory-Expiratory phase transition in Dbx1 interneurons of the preBötzinger complex in neonatal mice

    The Journal of Neuroscience 35:11606–11611.

    https://doi.org/10.1523/JNEUROSCI.0351-15.2015

    • PubMed
    • Google Scholar
48. 1. Krey RA
    2. Goodreau AM
    3. Arnold TB
    4. Del Negro CA

    (2010) Outward currents contributing to inspiratory burst termination in preBötzinger complex neurons of neonatal mice studied in vitro

    Frontiers in Neural Circuits 4:124.

    https://doi.org/10.3389/fncir.2010.00124

    • PubMed
    • Google Scholar
49. 1. Long S
    2. Duffin J

    (1986) The neuronal determinants of respiratory rhythm

    Progress in Neurobiology 27:101–182.

    https://doi.org/10.1016/0301-0082(86)90007-9

    • PubMed
    • Google Scholar
50. 1. Mantyh PW

    (2002)

    Neurobiology of substance P and the NK1 receptor

    The Journal of Clinical Psychiatry 63:6–10.

    • PubMed
    • Google Scholar
51. 1. Marder E
    2. O'Leary T
    3. Shruti S

    (2014) Neuromodulation of circuits with variable parameters: single neurons and small circuits reveal principles of state-dependent and robust neuromodulation

    Annual Review of Neuroscience 37:329–346.

    https://doi.org/10.1146/annurev-neuro-071013-013958

    • PubMed
    • Google Scholar
52. 1. Milsom WK

    (1991) Intermittent breathing in vertebrates

    Annual Review of Physiology 53:87–105.

    https://doi.org/10.1146/annurev.ph.53.030191.000511

    • PubMed
    • Google Scholar
53. 1. Moore JD
    2. Deschênes M
    3. Furuta T
    4. Huber D
    5. Smear MC
    6. Demers M
    7. Kleinfeld D

    (2013) Hierarchy of orofacial rhythms revealed through whisking and breathing

    Nature 497:205–210.

    https://doi.org/10.1038/nature12076

    • PubMed
    • Google Scholar
54. 1. Moreira TS
    2. Takakura AC
    3. Czeisler C
    4. Otero JJ

    (2016) Respiratory and autonomic dysfunction in congenital central hypoventilation syndrome

    Journal of Neurophysiology 116:742–752.

    https://doi.org/10.1152/jn.00026.2016

    • PubMed
    • Google Scholar
55. 1. Morgado-Valle C
    2. Baca SM
    3. Feldman JL

    (2010) Glycinergic pacemaker neurons in preBötzinger complex of neonatal mouse

    Journal of Neuroscience 30:3634–3639.

    https://doi.org/10.1523/JNEUROSCI.3040-09.2010

    • PubMed
    • Google Scholar
56. 1. Nakamura Y
    2. Katakura N
    3. Nakajima M
    4. Liu J

    (2004) Rhythm generation for food-ingestive movements

    Progress in Brain Research 143:97–103.

    https://doi.org/10.1016/S0079-6123(03)43009-4

    • PubMed
    • Google Scholar
57. 1. Narayanan NS
    2. DiLeone RJ

    (2017) Lip sync: gamma rhythms orchestrate Top-Down control of feeding circuits

    Cell Metabolism 25:497–498.

    https://doi.org/10.1016/j.cmet.2017.02.017

    • PubMed
    • Google Scholar
58. 1. Neske GT

    (2015) The slow oscillation in cortical and thalamic networks: mechanisms and functions

    Frontiers in Neural Circuits 9:88.

    https://doi.org/10.3389/fncir.2015.00088

    • PubMed
    • Google Scholar
59. 1. Nusbaum MP
    2. Blitz DM
    3. Swensen AM
    4. Wood D
    5. Marder E

    (2001) The roles of co-transmission in neural network modulation

    Trends in Neurosciences 24:146–154.

    https://doi.org/10.1016/S0166-2236(00)01723-9

    • PubMed
    • Google Scholar
60. 1. Oke Y
    2. Miwakeichi F
    3. Oku Y
    4. Hirrlinger J
    5. Hülsmann S

    (2018) Cell Type-Dependent activation sequence during rhythmic bursting in the PreBötzinger complex in respiratory rhythmic slices from mice

    Frontiers in Physiology 9:1219.

    https://doi.org/10.3389/fphys.2018.01219

    • PubMed
    • Google Scholar
61. 1. Oliveira LM
    2. Oliveira MA
    3. Moriya HT
    4. Moreira TS
    5. Takakura AC

    (2019) Respiratory disturbances in a mouse model of Parkinson's disease

    Experimental Physiology 104:729–739.

    https://doi.org/10.1113/EP087507

    • PubMed
    • Google Scholar
62. 1. Palva S
    2. Palva JM

    (2018) Roles of brain criticality and multiscale oscillations in temporal predictions for sensorimotor processing

    Trends in Neurosciences 41:729–743.

    https://doi.org/10.1016/j.tins.2018.08.008

    • PubMed
    • Google Scholar
63. 1. Paton JJ
    2. Buonomano DV

    (2018) The neural basis of timing: distributed mechanisms for diverse functions

    Neuron 98:687–705.

    https://doi.org/10.1016/j.neuron.2018.03.045

    • PubMed
    • Google Scholar
64. 1. Peña F
    2. Parkis MA
    3. Tryba AK
    4. Ramirez JM

    (2004a) Differential contribution of pacemaker properties to the generation of respiratory rhythms during normoxia and hypoxia

    Neuron 43:105–117.

    https://doi.org/10.1016/j.neuron.2004.06.023

    • PubMed
    • Google Scholar
65. 1. Peña F
    2. Ramirez JM

    (2004b) Substance P-mediated modulation of pacemaker properties in the mammalian respiratory network

    Journal of Neuroscience 24:7549–7556.

    https://doi.org/10.1523/JNEUROSCI.1871-04.2004

    • PubMed
    • Google Scholar
66. 1. Picardo MC
    2. Weragalaarachchi KT
    3. Akins VT
    4. Del Negro CA

    (2013) Physiological and morphological properties of Dbx1-derived respiratory neurons in the pre-Botzinger complex of neonatal mice

    The Journal of Physiology 591:2687–2703.

    https://doi.org/10.1113/jphysiol.2012.250118

    • PubMed
    • Google Scholar
67. 1. Ptak K
    2. Burnet H
    3. Blanchi B
    4. Sieweke M
    5. De Felipe C
    6. Hunt SP
    7. Monteau R
    8. Hilaire G

    (2002) The murine neurokinin NK1 receptor gene contributes to the adult hypoxic facilitation of ventilation

    European Journal of Neuroscience 16:2245–2252.

    https://doi.org/10.1046/j.1460-9568.2002.02305.x

    • PubMed
    • Google Scholar
68. 1. Ptak K
    2. Yamanishi T
    3. Aungst J
    4. Milescu LS
    5. Zhang R
    6. Richerson GB
    7. Smith JC

    (2009) Raphé neurons stimulate respiratory circuit activity by multiple mechanisms via endogenously released serotonin and substance P

    Journal of Neuroscience 29:3720–3737.

    https://doi.org/10.1523/JNEUROSCI.5271-08.2009

    • PubMed
    • Google Scholar
69. 1. Ramirez JM
    2. Tryba AK
    3. Peña F

    (2004) Pacemaker neurons and neuronal networks: an integrative view

    Current Opinion in Neurobiology 14:665–674.

    https://doi.org/10.1016/j.conb.2004.10.011

    • PubMed
    • Google Scholar
70. 1. Ramirez JM
    2. Dashevskiy T
    3. Marlin IA
    4. Baertsch N

    (2016) Microcircuits in respiratory rhythm generation: commonalities with other rhythm generating networks and evolutionary perspectives

    Current Opinion in Neurobiology 41:53–61.

    https://doi.org/10.1016/j.conb.2016.08.003

    • PubMed
    • Google Scholar
71. 1. Ramirez JM
    2. Ramirez SC
    3. Anderson TM

    (2018)

    SIDS Sudden Infant and Early Childhood Death: The Past, the Present and the Future

    Sudden Infant Death Syndrome, Sleep, and the Physiology and Pathophysiology of the Respiratory Network, SIDS Sudden Infant and Early Childhood Death: The Past, the Present and the Future, University of Adelaide Press.

    • Google Scholar
72. 1. Ramirez JM
    2. Baertsch N

    (2018a) Defining the rhythmogenic elements of mammalian breathing

    Physiology 33:302–316.

    https://doi.org/10.1152/physiol.00025.2018

    • PubMed
    • Google Scholar
73. 1. Ramirez JM
    2. Baertsch NA

    (2018b) The dynamic basis of respiratory rhythm generation: one breath at a time

    Annual Review of Neuroscience 41:475–499.

    https://doi.org/10.1146/annurev-neuro-080317-061756

    • PubMed
    • Google Scholar
74. 1. Richter DW
    2. Smith JC

    (2014) Respiratory rhythm generation in vivo

    Physiology 29:58–71.

    https://doi.org/10.1152/physiol.00035.2013

    • PubMed
    • Google Scholar
75. 1. Rubin JE
    2. Hayes JA
    3. Mendenhall JL
    4. Del Negro CA

    (2009) Calcium-activated nonspecific cation current and synaptic depression promote network-dependent burst oscillations

    PNAS 106:2939–2944.

    https://doi.org/10.1073/pnas.0808776106

    • PubMed
    • Google Scholar
76. 1. Rubin JE
    2. Smith JC

    (2019) Robustness of respiratory rhythm generation across dynamic regimes

    PLOS Computational Biology 15:e1006860.

    https://doi.org/10.1371/journal.pcbi.1006860

    • PubMed
    • Google Scholar
77. 1. Saito Y
    2. Ito M
    3. Ozawa Y
    4. Matsuishi T
    5. Hamano K
    6. Takashima S

    (2001) Reduced expression of neuropeptides can be related to respiratory disturbances in rett syndrome

    Brain and Development 23:S122–S126.

    https://doi.org/10.1016/S0387-7604(01)00358-8

    • PubMed
    • Google Scholar
78. 1. Schwab DJ
    2. Bruinsma RF
    3. Feldman JL
    4. Levine AJ

    (2010) Rhythmogenic neuronal networks, emergent leaders, and k-cores

    Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics 82:051911.

    https://doi.org/10.1103/PhysRevE.82.051911

    • PubMed
    • Google Scholar
79. 1. Schwarzacher SW
    2. Rüb U
    3. Deller T

    (2011) Neuroanatomical characteristics of the human pre-Bötzinger complex and its involvement in neurodegenerative brainstem diseases

    Brain 134:24–35.

    https://doi.org/10.1093/brain/awq327

    • PubMed
    • Google Scholar
80. 1. Smith JC
    2. Ellenberger HH
    3. Ballanyi K
    4. Richter DW
    5. Feldman JL

    (1991) Pre-Bötzinger complex: a brainstem region that may generate respiratory rhythm in mammals

    Science 254:726–729.

    https://doi.org/10.1126/science.1683005

    • PubMed
    • Google Scholar
81. 1. Smith JC
    2. Butera RJ
    3. Koshiya N
    4. Del Negro C
    5. Wilson CG
    6. Johnson SM

    (2000) Respiratory rhythm generation in neonatal and adult mammals: the hybrid pacemaker-network model

    Respiration Physiology 122:131–147.

    https://doi.org/10.1016/S0034-5687(00)00155-9

    • PubMed
    • Google Scholar
82. 1. Tan W
    2. Janczewski WA
    3. Yang P
    4. Shao XM
    5. Callaway EM
    6. Feldman JL

    (2008) Silencing preBötzinger complex somatostatin-expressing neurons induces persistent apnea in awake rat

    Nature Neuroscience 11:538–540.

    https://doi.org/10.1038/nn.2104

    • PubMed
    • Google Scholar
83. 1. Vann NC
    2. Pham FD
    3. Dorst KE
    4. Del Negro CA

    (2018) Dbx1 Pre-Bötzinger complex interneurons comprise the core inspiratory oscillator for breathing in unanesthetized adult mice

    Eneuro 5:ENEURO.0130-18.2018.

    https://doi.org/10.1523/ENEURO.0130-18.2018

    • PubMed
    • Google Scholar
84. 1. Viemari JC
    2. Roux JC
    3. Tryba AK
    4. Saywell V
    5. Burnet H
    6. Peña F
    7. Zanella S
    8. Bévengut M
    9. Barthelemy-Requin M
    10. Herzing LB
    11. Moncla A
    12. Mancini J
    13. Ramirez JM
    14. Villard L
    15. Hilaire G

    (2005) Mecp2 deficiency disrupts norepinephrine and respiratory systems in mice

    Journal of Neuroscience 25:11521–11530.

    https://doi.org/10.1523/JNEUROSCI.4373-05.2005

    • PubMed
    • Google Scholar
85. 1. Vong L
    2. Ye C
    3. Yang Z
    4. Choi B
    5. Chua S
    6. Lowell BB

    (2011) Leptin action on GABAergic neurons prevents obesity and reduces inhibitory tone to POMC neurons

    Neuron 71:142–154.

    https://doi.org/10.1016/j.neuron.2011.05.028

    • PubMed
    • Google Scholar
86. 1. Wang X
    2. Hayes JA
    3. Revill AL
    4. Song H
    5. Kottick A
    6. Vann NC
    7. LaMar MD
    8. Picardo MC
    9. Akins VT
    10. Funk GD
    11. Del Negro CA

    (2014) Laser ablation of Dbx1 neurons in the pre-Bötzinger complex stops inspiratory rhythm and impairs output in neonatal mice

    eLife 3:e03427.

    https://doi.org/10.7554/eLife.03427

    • PubMed
    • Google Scholar
87. 1. Winter SM
    2. Fresemann J
    3. Schnell C
    4. Oku Y
    5. Hirrlinger J
    6. Hülsmann S

    (2009) Glycinergic interneurons are functionally integrated into the inspiratory network of mouse medullary slices

    Pflügers Archiv - European Journal of Physiology 458:459–469.

    https://doi.org/10.1007/s00424-009-0647-1

    • Google Scholar
88. 1. Wu J
    2. Capelli P
    3. Bouvier J
    4. Goulding M
    5. Arber S
    6. Fortin G

    (2017) A V0 core neuronal circuit for inspiration

    Nature Communications 8:544.

    https://doi.org/10.1038/s41467-017-00589-2

    • PubMed
    • Google Scholar
89. 1. Wyart C

    (2018) Taking a big step towards understanding locomotion

    Trends in Neurosciences 41:869–870.

    https://doi.org/10.1016/j.tins.2018.09.010

    • PubMed
    • Google Scholar
90. 1. Wyman RJ

    (1977) Neural generation of the breathing rhythm

    Annual Review of Physiology 39:417–448.

    https://doi.org/10.1146/annurev.ph.39.030177.002221

    • PubMed
    • Google Scholar
91. 1. Yeh SY
    2. Huang WH
    3. Wang W
    4. Ward CS
    5. Chao ES
    6. Wu Z
    7. Tang B
    8. Tang J
    9. Sun JJ
    10. Esther van der Heijden M
    11. Gray PA
    12. Xue M
    13. Ray RS
    14. Ren D
    15. Zoghbi HY

    (2017) Respiratory network stability and modulatory response to substance P require nalcn

    Neuron 94:294–303.

    https://doi.org/10.1016/j.neuron.2017.03.024

    • PubMed
    • Google Scholar
92. 1. Zerlaut Y
    2. Destexhe A

    (2017) Enhanced responsiveness and Low-Level awareness in stochastic network states

    Neuron 94:1002–1009.

    https://doi.org/10.1016/j.neuron.2017.04.001

    • PubMed
    • Google Scholar
93. 1. Zhang B
    2. Harris-Warrick RM

    (1994)

    Multiple receptors mediate the modulatory effects of serotonergic neurons in a small neural network

    The Journal of Experimental Biology 190:55–77.

    • PubMed
    • Google Scholar
94. 1. Zuperku EJ
    2. Stucke AG
    3. Hopp FA
    4. Stuth EA

    (2017) Characteristics of breathing rate control mediated by a subregion within the pontine parabrachial complex

    Journal of Neurophysiology 117:1030–1042.

    https://doi.org/10.1152/jn.00591.2016

    • PubMed
    • Google Scholar
95. 1. Zuperku EJ
    2. Stucke AG
    3. Krolikowski JG
    4. Tomlinson J
    5. Hopp FA
    6. Stuth EA

    (2019) Inputs to medullary respiratory neurons from a pontine subregion that controls breathing frequency

    Respiratory Physiology & Neurobiology 265:127–140.

    https://doi.org/10.1016/j.resp.2018.06.011

    • PubMed
    • Google Scholar

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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