Blood pressure drives multispectral tuning of inspiration via a linked-loop neural network

Lauren S Segers; Sarah C Nuding; Mackenzie M Ott; Russell O’Connor; Kendall F Morris; Bruce G Lindsey

doi:10.1152/jn.00442.2020

. 2020 Sep 23;124(6):1676–1697. doi: 10.1152/jn.00442.2020

Blood pressure drives multispectral tuning of inspiration via a linked-loop neural network

Lauren S Segers ¹, Sarah C Nuding ¹, Mackenzie M Ott ¹, Russell O’Connor ¹, Kendall F Morris ^1,^✉, Bruce G Lindsey ¹

PMCID: PMC7814902 PMID: 32965158

Abstract

The respiratory motor pattern is coordinated with cardiovascular system regulation. Inspiratory drive and respiratory phase durations are tuned by blood pressure and baroreceptor reflexes. We hypothesized that perturbations of systemic arterial blood pressure modulate inspiratory drive through a raphe-pontomedullary network. In 15 adult decerebrate vagotomized neuromuscular-blocked cats, we used multielectrode arrays to record the activities of 704 neurons within the medullary ventral respiratory column, pons, and raphe areas during baroreceptor-evoked perturbations of breathing, as measured by altered peak activity in integrated efferent phrenic nerve activity and changes in respiratory phase durations. Blood pressure was transiently (30 s) elevated or reduced by inflations of an embolectomy catheter in the descending aorta or inferior vena cava. S-transform time-frequency representations were calculated for multiunit phrenic nerve activity and some spike trains to identify changes in rhythmic activity during perturbations. Altered firing rates in response to either or both conditions were detected for 474 of 704 tested cells. Spike trains of 17,805 neuron pairs were evaluated for short-time scale correlational signatures indicative of functional connectivity with standard cross-correlation analysis, supplemented with gravitational clustering; ∼70% of tested (498 of 704) and responding neurons (333 of 474) were involved in a functional correlation with at least one other cell. Changes in high-frequency oscillations in the spiking of inspiratory neurons and the evocation or resetting of slow quasi-periodic fluctuations in the respiratory motor pattern associated with oscillations of arterial pressure were observed. The results support a linked-loop pontomedullary network architecture for multispectral tuning of inspiration.

NEW & NOTEWORTHY The brain network that supports cardiorespiratory coupling remains poorly understood. Using multielectrode arrays, we tested the hypothesis that blood pressure and baroreceptor reflexes “tune” the breathing motor pattern via a raphe-pontomedullary network. Neuron responses to changes in arterial pressure and identified functional connectivity, together with altered high frequency and slow Lundberg B-wave oscillations, support a model with linked recurrent inhibitory loops that stabilize the respiratory network and provide a path for transmission of baroreceptor signals.

Keywords: blood pressure, brain stem network, breathing, cardiorespiratory coupling, oscillations

INTRODUCTION

Breathing drives ventilation and supports voluntary actions and cognitive functions of the brain, including memory formation, attentional modulation, and the sense of corporeal awareness (Arshamian et al. 2018; Heck et al. 2019; Herrero et al. 2018; Monti et al. 2020; Park et al. 2020; Tort et al. 2018). The respiratory motor pattern is tightly coordinated with cardiovascular system regulation (Barman 2020; Convertino 2019; Guyenet 2014; Menuet et al. 2020; Taylor et al. 1999). Inspiratory drive and respiratory phase durations are tuned by blood pressure and baroreceptor reflexes (Arata et al. 2000; Baekey et al. 2010; Bishop 1974; Gabriel and Seller 1969; Grunstein et al. 1975; Heymans and Bouckaert 1930; Lindsey et al. 1998; McMullan et al. 2009; McMullan and Pilowsky 2010; Nishino and Honda 1982; Richter and Seller 1975). Cardiac pulse-pressure sensitive receptors, possibly including muscle spindles (Birznieks et al. 2012), influence the respiratory motor output on a beat-by-beat basis (Dick et al. 2005; Ford and Kirkwood 2018; Kirkwood et al. 2019). Coughing, an airway defensive behavior that transiently appropriates brain stem circuits for breathing (Shannon et al. 2000), is also influenced by changes in blood pressure (Poliaček et al. 2011).

One series of network models for the regulation of breathing by blood pressure includes homeostatic recurrent inhibitory raphe circuit loops linked to the ventral respiratory column (VRC), a brain region essential for respiratory rhythm and pattern generation (Lindsey et al. 1991, 1998, 2012). These loops, composed of excitatory raphe neurons constrained via recurrent inhibition from local and distant sites, are proposed to help stabilize the network while providing a path for transmission of baroreceptor signals. More generally, such loop circuits can operate as filters and generate synchrony-driven gain modulation (Hennequin et al. 2017), a hypothesis supported by repeated patterns of spike synchrony in the respiratory brain stem evoked by baroreceptor stimulation (Arata et al. 2000; Chang et al. 2000; Lindsey et al. 1997). In the model, raphe circuits mediate push-pull tuning of inspiratory drive through coordinated clusters of VRC tonic expiratory (t-E) neurons that operate as hubs where baroreceptor and chemoreceptor influences converge (Morris et al. 2018; Segers et al. 2012, 2015).

The VRC is embedded in a distributed pontomedullary network (Segers et al. 2008), and perturbation of pontine circuits can disrupt cardiorespiratory coupling (Dutschmann and Dick 2012). Raphe neurons have been proposed to serve as routes for pontine-VRC communications (Nuding et al. 2009a, 2009b, 2015), but the mechanisms that normally contribute to the coordination of breathing and blood pressure remain incompletely understood. Motivated by this gap in knowledge, we addressed the hypothesis that perturbations of systemic arterial blood pressure modulate inspiratory drive through a raphe-pontomedullary network. We monitored neuronal spike trains at multiple sites within the brain stem to detect changes in firing rates during transient elevations and reductions in blood pressure and to acquire evidence of functional connectivity that would inform further model development. Perturbation of inspiratory drive and respiratory cycle frequency included changes in high-frequency oscillations (HFOs, 50–120 Hz) commonly observed in the spiking of inspiratory neurons (Funk and Parkis 2002; Ghali and Marchenko 2013) and the evocation or resetting of slow quasi-periodic fluctuations in the respiratory motor pattern (∼0.04 Hz) associated with oscillations of arterial pressure (Cherniack et al. 1969; Preiss et al. 1975). The results support a linked-loop pontomedullary network architecture for this multispectral tuning of inspiration.

Preliminary accounts of this work have been reported (Lindsey et al. 2019; O’Connor et al. 2013).

METHODS

All experiments were performed according to protocols approved by the University of South Florida’s Institutional Animal Care and Use Committee with strict adherence to all American Association for Accreditation of Laboratory Animal Care International, National Institutes of Health, and National Research Council guidelines. Data were acquired from 15 adult cats (3.0–5.25 kg) of either sex. Animals were initially anesthetized with 5% isoflurane mixed with air or with ketamine hydrochloride (5.5 mg/kg im) followed by isoflurane, and maintained with 0.5%–3.0% isoflurane until decerebration (Kirsten and St. John 1978). Arterial blood pressure, end-tidal CO₂, and tracheal pressure were monitored continuously; arterial PO₂, PCO₂, and pH were measured periodically. Animals were neuromuscularly blocked (pancuronium bromide or vecuronium bromide, initial bolus 0.1 mg/kg followed by 0.2 mg·kg⁻¹·h⁻¹ iv) and artificially ventilated with either air (n = 4) or 100% O₂ (n = 11). A bilateral cervical vagotomy was performed to remove vagal afferent feedback from pulmonary stretch receptors and to permit comparisons with prior work. At the end of each experiment, animals were euthanized (Beuthanasia [0.97 mg/kg; Schering-Plough Animal Health] or sodium pentobarbital [28 mg/kg] followed by a saturated solution of KCl in water).

Efferent phrenic nerve activity was monitored continuously; lumbar, splanchnic and/or vagus nerves were also recorded in some experiments. Neuronal spike trains were recorded with arrays of 16–32 individual depth-adjustable high-impedance tungsten microelectrodes (1 μm tip diameter; 10–12 MΩ). Spikes were isolated and converted to digital time series using interactive spike-sorting software packages (Datawave Technologies, O’Connor et al. 2005). Recording sites derived from stereotaxic coordinates were mapped into the three-dimensional space of a computer-based brain stem atlas derived from The Brain Stem of the Cat: A Cytoarchitectonic Atlas with Stereotaxic Coordinates (Berman 1968) (also see Nuding et al. 2015; Segers et al. 2008, 2015).

Phase-normalized respiratory cycle-triggered histograms (CTHs) were calculated. Each neuron was evaluated for a respiratory-modulated firing rate and classified as inspiratory (I) or expiratory (E) according to the part of the cycle during which the cell was most active and further classified as decrementing (Dec) or augmenting (Aug) if the peak rate occurred during the first or second half of the phase, respectively (Cohen 1968; Segers et al. 2015). Neurons were additionally designated as phasic (P), if their firing probability was zero during any part of the respiratory cycle, as estimated by the CTH, or tonic (T) otherwise. Cells without a preferred phase of maximum activity were designated nonrespiratory modulated (NRM). Cardiac pulse CTHs were also calculated and evaluated for significant cardiac modulation (Dick and Morris 2004; Dick et al. 2005).

Mean arterial blood pressure was transiently (∼30 s) elevated or reduced by inflation of an embolectomy catheter inserted in the descending aorta or inferior vena cava, respectively (Fig. 1A; Table 1). Trials were separated by ∼1.5-min intervals to allow the mean systemic arterial pressure to return to prestimulus levels. Effective perturbations of breathing were identified by changes greater than or equal to two standard deviations in the peak integrated phrenic nerve amplitude from the mean of prestimulus values (Nuding et al. 2009b); a minimum of three effective trials were required for inclusion in this study. Altered respiratory phase durations were also observed (e.g., Fig. 1, B and C, right). Significant responses for each neuron for each stimulus protocol were identified as a change in neuronal firing rate assessed with a bootstrap-based statistical method with the false discovery rate controlled to be less than 5% (Nuding et al. 2009b). Responses were classified as an increase (INC), a decrease (DEC), or no change in rate. A significant change in the depth of breathing modulation, or “rate ratio,” provided an indication of cross-phase modulation (Segers et al. 2015) and was reported as a neuron’s response if a change in rate ratio was not accompanied by any other significant change in firing rate.

Fig. 1. — Changes in blood pressure evoke changes in the breathing motor pattern. A: schematic of ventral surface of brain stem and baroreceptors illustrates procedures used to transiently elevate or reduce mean arterial blood pressure by inflation of embolectomy catheters in the descending aorta and inferior vena cava, respectively. Red X indicates that vagus nerves were sectioned bilaterally, eliminating influences of pulmonary receptors and aortic baroreceptors normally signaled via those nerves. B: perturbations of the respiratory motor pattern during trials of elevated blood pressure were assessed by (*left*) the s-transform time-frequency representation of multiunit efferent phrenic nerve activity, displayed as a heat map with a luminance proportional to the s-transform magnitude with frequency represented by a log scale (2 of 5 trials are shown), and by (*right*) changes in peak integrated phrenic nerve amplitude and respiratory phase durations (I, inspiratory; E, expiratory, including post-inspiration) during the stimulus (green shading) greater than 2 standard deviations (gray horizontal lines) from the mean of pre-stimulus values as shown in associated phase graphs. Color shading within the s-transform corresponds to relative magnitude; in this example, magnitude values range from 0 to 57.3 (darkest to lightest shade, respectively). Arrowhead indicates the frequency of respiratory cycling. C: S-transform (*left*) and phase graphs (*right*) show changes in the motor pattern during a transient reduction in blood pressure (highlighted in pink) and post-stimulus changes in motor output indicated by asterisks marking opposite directions of change in amplitude and phase durations that occurred when the stimulus was removed.

Table 1.

The data set reported in this study consists of 704 brain stem neurons evaluated for changes in firing rate when mean arterial blood pressure was transiently elevated or reduced

Animal	Recording	Sex	Ventilation Gas	Number of Simultaneously Recorded Neurons Tested for Response to Elevated and/or Reduced BP			Change in MAP When BP Was Elevated, mmHg; means ± SD	Change in MAP When BP Was Lowered, mmHg; means ± SD	Previously Reported Responses to Chemoreceptor Stimuli
Animal	Recording	Sex	Ventilation Gas	VRC	Raphe	Pons	Change in MAP When BP Was Elevated, mmHg; means ± SD	Change in MAP When BP Was Lowered, mmHg; means ± SD	Previously Reported Responses to Chemoreceptor Stimuli
1	1	F	Air	30	18	19	42.7 ± 3.4	−19.15 ± 1.8	• Hypocapnic apnea (Nuding et al. 2015, Animal 1, Figs. 1–4)
2	1	F	Air	42			48.0 ± 6.2	n/a	• Central chemoreceptor (CR) stimulation (Ott et al. 2011; Ott et al. 2012, Figs. 1 and 2) • Peripheral CR stimulation (Segers et al. 2015)
3	1	F	100% O₂	5	11	12	29.0 ± 3.4	−12.98 ± 2.5
4	1	F	100% O₂	32	27	17	24.7 ± 3.0	−22.4 ± 2.7	• Peripheral CR stimulation (Segers et al. 2015) • Peripheral CR stimulation (Nuding et al. 2009b) • Central CR stimulation (Nuding et al. 2009b) • Hypocapnic apnea (Nuding et al. 2015, Animal 4, Fig. 9)
4	2		100% O₂	40	19	15	24.7 ± 5.2	−28.3 ± 4.5
5	1	F	100% O₂	23	29	10	27.7 ± 6.2	−42 ± 6.3	• Peripheral CR stimulation (Segers et al. 2015) • Hypocapnic apnea (Nuding et al. 2015)
6	1	F	Air	30			51 ± 5.1	n/a	• Central CR stimulation (Ott et al. 2011) • Peripheral CR stimulation (Segers et al. 2015)
7	1	F	Air	65			71.3 ± 1.6	n/a	• Central CR stimulation (Ott et al. 2011) • Peripheral CR stimulation (Segers et al. 2015)
8	1	F	100% O₂		40		26.7 ± 5.3	−21.7 ± 3.9
9	1	M	100% O₂		19		45.0 ± 5.6	−34.3 ± 6.7
10	1	M	100% O₂	12	11	12	45.8 ± 6.1	−45.6 ± 9.3
11	1	F	100% O₂		23	13	44.0 ± 6.0	−37.0 ± 8.1
12	1	F	100% O₂		16		30.0 ± 3.8	−29.34 ± 3.9
13	1	M	100% O₂		17	19	26.6 ± 1.4	−24.58 ± 1.4
14	2	F	100% O₂		21	21	36.7 ± 0.04	−30.4 ± 1.8
15	4	F	100% O₂		22	14	51.3 ± 5.0	−17.9 ± 6.0

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For each of 16 recordings, this table reports the numbers of neurons tested in each brain stem region, the mean (± SD) change in mean arterial blood pressure (MAP), and whether data from an animal have been reported previously. References to previously assessed responses to other stimuli. BP, blood pressure.

The s-transform joint time-frequency representation (Stockwell et al. 1996) was calculated for multi-neuron phrenic nerve signals and some spike train records to identify changes in rhythmic activity with experimental perturbations of systemic arterial blood pressure (Nuding et al. 2015). A constant relative time resolution of either 2 or 4 cycles was used. The null hypothesis was that the observed s-transform magnitude in a window was generated by a Bernoulli process with a probability equal to the mean rate per tick of the whole observed spike train. In significance, displays of the s-transform, time-frequency points with magnitudes large enough to cross the P value threshold of 0.005 and with a false discovery rate (FDR) of less than 5% were considered significant.

Spike trains were evaluated for short time scale correlational signatures indicative of functional connectivity by standard cross-correlation analysis (Perkel et al. 1967), supplemented by gravitational clustering (Gerstein 2010). Peak or trough features in the correlograms and significant aggregation in the gravitational representations were identified with Monte Carlo tests (Morris et al. 2018) using surrogate spike trains (Pauluis and Baker 2000) with gamma-distributed inter-spike intervals (Miura et al. 2006). The false discovery rate for significant correlogram bins was controlled to be less than 5% and all reported correlogram features had a detectability index >3 (Aertsen and Gerstein 1985; Melssen and Epping 1987). Unless otherwise noted, the cross-correlograms presented were calculated using all spike events in a recording. Correlation linkage maps graphically represented sets of detected cross-correlogram features in groups of simultaneously monitored neurons (Segers et al. 2008). Significant features in spike-triggered averages were identified using a two-sided Wilcoxon signed-rank test with Bonferroni correction; P < 0.05 (Ott et al. 2012).

RESULTS

Transient increases and decreases in mean systemic arterial blood pressure caused by inflation of embolectomy catheters in the descending aorta and inferior vena cava (Fig. 1A), respectively, evoked changes in the respiratory motor pattern as assessed by integrated phrenic nerve activity. An elevated blood pressure was commonly associated with a reduction in inspiratory drive and a slower cycle frequency due to a prolongation of both inspiration and expiration, as seen in the firing-rate histogram and s-transform of the integrated phrenic nerve activity (Fig. 1B). The s-transform is shown as a heat map, with luminance proportional to the s-transform magnitude value; frequency is displayed as a log scale along the y-axis. These examples were computed using frequencies between 0.05 and 5.0 Hz; the lowest, brightest band (∼0.5 Hz; arrow) denotes respiratory cycling, with parallel harmonic bands at higher frequencies. The range of s-transform magnitudes is represented by the colored scale to the right of the plots; each s-transform is scaled individually from 0 to its maximum magnitude value. The response during an interval of reduced blood pressure was an increase in inspiratory drive sometimes accompanied by an increase in respiratory frequency (Fig. 1C). Following deflation of the catheter balloons, changes in the motor pattern were sometimes noted, for example, as in Fig. 1C, in the slowing of the rhythm and a decline in integrated phrenic amplitude during the poststimulus period. Additional examples of these and other less common changes in the motor pattern associated with the perturbations are shown in subsequent figures.

Parallel recordings of neuronal spike trains from the raphe-pontomedullary respiratory network of Animal 1 (Fig. 2A) included the signal from pontine neuron 533, whose impulse frequency increased during the lowering of blood pressure (Fig. 2B). Such responses were identified by comparing rates by respiratory cycle measured before and following the onset of successive elevations or reductions in blood pressure (Fig. 2C). Rate changes in response to the perturbations of blood pressure were identified in most neurons as indicated by color-coded symbols next to corresponding rate histograms, grouped by brain stem region and plotted together with control respiratory-modulated discharge profiles (Fig. 2D). The s-transform time-frequency representation of the spike train for inspiratory neuron 815 documented changes both in respiratory cycle frequency and in inspiratory high-frequency oscillations of ∼90 Hz. The corresponding spike-triggered average showed these time-locked oscillations in phrenic motor output (Fig. 2E). The responses of these neurons during hypocapnic apnea were reported in a previous study (Table 1).

The spiking activities of 704 neurons were recorded with multielectrode arrays and tested with elevated and/or reduced blood pressure: 279 in the VRC, 152 in the pons (inclusive of the Kölliker–Fuse/parabrachial complex of the dorsolateral pons), and 273 cells in the brain stem midline (including the medullary raphe nuclei). The ranges of the anteroposterior, medial-lateral, and depth coordinates of recording locations in the VRC were from −3.0 to 9.0 mm (with respect to the obex), 1.92–5.23 mm to the right of midline, and 2.51–6.81 mm deep; in the raphe region, 0–11.23 mm rostral to obex, 0.2 mm on either side of midline, and 0.1–6.01 mm deep; and in the pons, from −2.0 to 0 (with respect to the caudal border of the inferior colliculus), 3.15–6.22 to the right of midline, and 1.0–5.61 mm deep. Table 1 provides, for each recording, the mean changes in mean arterial pressure and the number of neurons within each brain region tested for response to changes in blood pressure. Overall, 474 (67%) of the 704 neurons tested with either or both direction of change in blood pressure responded with a significant change in firing rate; results are presented according to brain stem area and respiratory type in Table 2. A majority of the neurons recorded in each of the three brain stem areas, respiratory (VRC, 72%; raphe, 69%; pons, 60%) as well as nonrespiratory (53%; 73%; 71%, respectively) modulated, were responsive to experimental manipulation of arterial pressure. Table 3 lists the results of perturbations in blood pressure grouped by direction of change in blood pressure, brain stem area, and response category. Approximately 39% of cells tested with trials of elevated blood pressure responded with a significant change in firing rate or rate ratio, mostly with a decreased firing rate; this relationship was present within each of the three brain stem areas. A greater percentage of cells tested with lowered blood pressure responded (63%), most of them with an increased firing rate, again within each region. Twenty-six percent of cells tested with both elevated and reduced blood pressure responded to both perturbations (Table 4); 33 neurons responded with a similar direction of change in firing rate, whereas 107 cells responded differently. Some cells (n = 154) tested with both stimulus conditions did not respond to either.

Table 2.

Location and respiratory modulation of neurons that responded to experimental manipulation of arterial blood pressure

Respiratory Modulation	Brain Stem Area						Total Percentage of Cells That Respond, %
	VRC		Raphe		Pons
	No. of Cells Tested	Percentage of Cells that Respond, %	No. of Cells Tested	Percentage of Cells that Respond, %	No. of Cells Tested	Percentage of Cells that Respond, %
Respiratory	186	72	172	69	83	60	68
I-phasic	53	94	8	50	8	63	86
I-tonic	39	64	65	63	28	54	61
E-phasic	33	79	8	88	7	71	79
E-tonic	55	55	90	72	40	63	65
IE	4	25	1	100	0		20
EI	2	100	0		0		100
NRM	93	53	101	73	69	71	65
Total	279	66	273	70	152	65	67

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Neurons with significant changes in firing rate during either stimulus condition are grouped by respiratory type and brain stem area. E, expiratory neuron; EI, neuron with greatest firing rate during the E-to-I phase transition; I, inspiratory neuron; IE, neuron with greatest firing rate during the I-to-E phase transition; NRM, neuron without respiratory modulation; phasic, on average, firing probability is zero during part of the respiratory cycle; tonic, on average, neuron is active throughout the respiratory cycle; VRC, ventral respiratory column.

Table 3.

Location and response category of neurons that responded to experimental manipulation of arterial blood pressure

Stimulus Condition	Brain Stem Area	No. of Neurons Tested	Change in Cell Activity			Total No. of Responsive Neurons
Stimulus Condition	Brain Stem Area	No. of Neurons Tested	INC	DEC	Rate ratio	Total No. of Responsive Neurons
Elevated blood pressure	VRC	273	42	79	12	133
	Raphe	263	20	60	2	82
	Pons	149	7	38	5	50
	Total	685	69 (26%)	177 (67%)	19 (7%)	265
Reduced blood pressure	VRC	141	75	24	1	100
	Raphe	268	137	21	2	160
	Pons	146	70	14	5	89
	Total	555	282 (81%)	59 (17%)	8 (2%)	349

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Neurons with significant changes in firing rate grouped by direction of change in blood pressure, brain stem area, and response category. Numbers in parentheses indicate percentage of responsive neurons within a category. DEC, decreased firing rate; INC, increased firing rate; rate ratio, change in the depth of breathing modulation; VRC, ventral respiratory column.

Table 4.

Summary of response analysis of 536 neurons tested with both elevated and lowered blood pressure

Brain Stem Area	Response to Elevated Blood Pressure	Response to Reduced Blood Pressure				No. of Neurons Tested with Both Conditions
Brain Stem Area	Response to Elevated Blood Pressure	INC	DEC	Rate ratio	None	No. of Neurons Tested with Both Conditions
VRC	INC	8	3	0	2	135
	DEC	24	11	0	2
	Rate ratio	4	0	0	0
	None	37	10	1	33
Raphe	INC	4	7	0	9	258
	DEC	35	3	0	22
	Rate ratio	0	1	0	1
	None	90	10	2	74
Pons	INC	4	2	0	1	143
	DEC	26	2	2	6
	Rate ratio	3	0	1	1
	None	36	10	2	47
Total						536

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140 cells (26%) responded to both stimulus conditions with a significant change in firing rate or rate ratio. DEC, decreased firing rate; INC, increased firing rate; none, no significant change in firing rate; rate ratio, change in the depth of breathing modulation; VRC, ventral respiratory column.

Intra- and interregional functional connectivity among baroresponsive neurons support circuit operations for tuning of inspiratory drive by blood pressure.

Cross-correlogram features included central peaks indicative of shared influences of like sign and offset peaks and troughs commonly interpreted as signs of excitatory and inhibitory processes, respectively (Moore et al. 1970; Perkel et al. 1967). The feature set derived from the multisite recording in Animal 1 (shown in Fig. 3A and summarized in the correlation feature map in Fig. 3B) revealed evidence for intra- and interregional relationships among neurons distributed in the pons, raphe, and VRC that support neuron circuit operations for tuning of inspiratory drive by blood pressure. Circles representing the neurons include an identification code, respiratory modulation pattern, and responses to elevated and lowered blood pressure. Each neuron in the correlated pairs shown had a change in firing rate in response to one or both series of blood pressure perturbations. Neurons represented in the map are grouped by brain region to aid description of inferred circuit operations.

Fig. 3. — Responses and cross-correlograms features support pontine neuron circuit operations for tuning of an inspiratory drive by blood pressure. A: cross-correlograms for pairs of neurons in *Animal 1* represented in Fig. 2. Each correlogram was scaled to facilitate compact visual representation; minimum and maximum firing rates, normalized to spikes/s/trigger event, are indicated. Numbers in yellow circles correspond to represented cross-correlograms. Arrowheads in *correlograms 1* and 2 note a positive-lag offset peak superimposed upon a broader peak. The detectability index values for troughs or peaks in each histogram are as follows (O, offset; C, central; P, peak; T, trough): 1: OP, 8.5; 2: OP, 23.6; 3: OP, 10.4; 4: OP, 8.2; 5: OT, 5.1; 6: OT, 9.4; 7: OT, 8.2; 8: OT, 5.4; 9: OT, 3.5; 10: OT, 4.1; 11: CP, 47.4; 12: OT, 4.1; 13: OP, 6.9; 14: OT, 6.3; 15: OT, 6.9; 16: OP, 3.5; 17: OP, 5.9; 18: OP, 3.9; 19: OT, 3.6; 20: OT, 3.5; 21: OP, 4.2; 22: CP, 5.4; 23: OP, 4.0; 24: OT, 4.8; 25: OP, 4.3; and 26: OT, 3.0. Number of spikes for each neuron used to calculate cross-correlograms: *511*: 5,347; *524*: 19,393; *533*: 28,002; *534*: 38,613; *803*: 298,388; *805*: 462,009; *807*: 200,008; *813*: 44,427; *815*: 203,049; *837*: 248,893; *842*: 138,279; *852*: 40,619; *853*: 77,355; *881*: 71,915; *909*: 55,815; *913*: 6,577; *914*: 8,883; *915*: 24,060; *919*: 5,232; *931*: 37,612; and *933*: 18,051. B: correlation feature map represents simultaneously monitored pairs of neurons as circles linked by lines with symbols near target neurons that correspond to the direction of change in their firing probability (+, increased firing probability is characterized by a peak in the CCH; –, decreased probability, trough), following spikes in the indicated reference or trigger neurons. Such maps and neuron responses permit inferences about intra and interregional relationships and directed functional excitation and inhibition. Red lines indicate putative inhibitory influences of pontine neurons on raphe and VRC cells. C: features in spike-triggered averages of rectified nerve recordings support links between responsive neurons and phrenic and lumbar motor neurons shown in B. VRC, ventral respiratory column.

Rostral VRC (pre-Bötzinger region) inspiratory neuron 805 triggered correlograms with target inspiratory neurons 815 and 837 that featured narrow offset peaks with positive time lags riding upon broader peaks (Fig. 3A, 2 and 3); the correlogram of 803 and one of these target neurons (815) had similar features (Fig. 3A1). These features suggested excitatory actions of the trigger neurons superimposed upon the more temporally dispersed influence of other coordinated, but unobserved, inputs. Bilateral peaks and troughs indicative of HFOs were common in correlograms for inspiratory neuron pairs in this recording (e.g., Fig. 3A, 2–7). Evidence for a VRC inspiratory neuron chain disinhibitory microcircuit for baroreceptor tuning of the drive to breathe was also identified (Fig. 3A, 6 and 7): inspiratory decrementing (I-Dec) neuron 881 triggered correlograms with neurons 803 and 805 that featured offset troughs (e.g., Fig. 3A6), as did correlograms triggered by 813 for target inspiratory neurons 815 and 852 and tonic expiratory (t-E) neuron 884 (Fig. 3A7).

A VRC-pontine loop circuit was supported by offset peaks in correlograms triggered by inspiratory neurons 837 and 815 with pontine target 534 (Fig. 3A4) and a small offset trough consistent with 534-to-803 inhibition (Fig. 3A5). Putative inhibition of VRC I-Dec neurons by pontine neuron 511 was also identified (Fig. 3A8). Other identified interactions were indicative of VRC-raphe loop operations modulated by the pons. Decrementing inspiratory neuron 842 triggered correlograms with offset troughs that suggested distributed inhibitory actions upon a cluster of raphe targets, including neurons 931 and 919 (Fig. 3A, 15 and 24). In turn, two of the raphe neurons in the target cluster, 915 and 931, triggered correlograms with VRC t-E neuron 807 that featured offset peaks (Fig. 3A16). Neurons 913, 915, and 919 in the cluster were putative targets of pontine neuron 511 (Fig. 3A, 13, 21, and 25), which also had a high probability of synchronous firing with raphe neuron 914 (Fig. 3A11). Two other pontine neurons, 524 and 544, had putative inhibitory actions on neuron 914 (e.g., Fig. 3A19), whereas both neurons 914 and 511 triggered offset trough correlograms with several VRC I-Dec neuron targets, including neurons 842, 852, and 853 (Fig. 3A, 8, 9, and 14). These results supported convergent inhibition of VRC I-Dec neurons and modulation of their local interactions (Fig. 3A10) by both pontine and raphe circuits. Other offset features in raphe neuron correlations were indicative of local circuit actions (Fig. 3A, 17, 20, 23, and 26), whereas central peaks corroborated the shared influences from the VRC and pons (Fig. 3A22). The positive-lag peaks in spike-triggered averages of rectified nerve activities (Fig. 3C) indicated excitatory influences of VRC I neuron 837 and E cell 854 upon inspiratory (phrenic n.) and expiratory (lumbar n.) motor outputs, respectively.

Overall, a total of 17,805 neuron pairs in which both cells were tested with elevated and/or reduced blood pressure were evaluated for short-time scale spike train correlations in this study. Cross-correlograms of 1,224 (7%) of such pairs contained at least one significant correlation feature; detected features included offset and central peaks and troughs and instances of multiple/repeating peaks and troughs; correlograms for 640 pairs had offset peak or trough features consistent with excitatory and inhibitory synaptic interactions, respectively. Neuron activities were correlated in 589 of 8,289 pairs (7%) in which both cells responded to at least one perturbation; the percentage of correlation was similar when considering only those pairs in which both neurons responded to both stimulus conditions (104 of 1,256 pairs; 8%). Short-time scale correlations of 302 neuron pairs were characterized by an offset peak or trough; 287 (95%) of these pairs were composed of cells which responded with an increased or decreased firing rate to a change in blood pressure and, together with their correlational features, exhibited at least one response-promoting and/or response-limiting relationship. The matrix of each neuron’s response to each category of perturbation and their correlational signature of interaction provides a set of putative network operations supported by the results. We identified neuron pairs with responses (altered firing rates) and offset peak or trough features supporting functional connectivity appropriate for eight categories of tuning of respiratory drive and rhythm during changes in systemic arterial blood pressure (Table 5). Results from each animal’s data set contributed to the inferred mechanisms presented in Table 5.

Table 5.

Inferred mechanisms of contribution of a trigger neuron to the response of its putative target cell

Trigger Neuron	Target Neuron	Offset Feature	Inferred Mechanism of Target Cell Response Promotion/Limitation	Number of Inferred Mechanisms Detected With Cells Located in: (Trigger Area → Target Area)
				Same brain stem area			Different brain stem areas
				V→V	R→R	P→P	V→R	R→V	V→P	P→V	R→P	P→R	Total
Response to elevated BP
INC	INC	Peak	Promote INC by excitation	8		1						1	10
DEC	INC	Trough	Promote INC by disinhibition	8				2					10
INC	INC	Trough	Limit INC by inhibition	6	1	1							8
DEC	INC	Peak	Limit INC by disfacilitation	9				2		1			12
INC	DEC	Trough	Promote DEC by inhibition	13								2	15
DEC	DEC	Peak	Promote DEC by disfacilitation	35	5	1		1	2		4	1	49
INC	DEC	Peak	Limit DEC by excitation	7					2				9
DEC	DEC	Trough	Limit DEC by disinhibition	59	3		2	1		2	1	2	70
			Total	145	9	3	2	6	4	3	5	6	183
				Total of intra-area mechanisms = 157			Total of inter-area mechanisms = 26
Response to reduced BP
INC	INC	Peak	Promote INC by excitation	7	19	5	4	3	4	4	5	11	62
DEC	INC	Trough	Promote INC by disinhibition	4			4		2			1	11
INC	INC	Trough	Limit INC by inhibition	6	9	1	7	5		7		5	40
DEC	INC	Peak	Limit INC by disfacilitation	4	4	2	3	1	4	1	2		21
INC	DEC	Trough	Promote DEC by inhibition	5		2				1	1		9
DEC	DEC	Peak	Promote DEC by disfacilitation	3					2				5
INC	DEC	Peak	Limit DEC by excitation	2		3		7	1	2	1		16
DEC	DEC	Trough	Limit DEC by disinhibition	6			1		2				9
			Total	37	32	13	19	16	15	15	9	17	173
				Total of intra-area mechanisms = 82			Total of inter-area mechanisms = 91

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DEC, decreased firing rate; INC, increased firing rate; P, pons; R, raphe; V, ventral respiratory column; arrows indicate directionality of inferred functional connectivity.

Many of the pairs included in Table 5 were elements of larger correlational neuronal assemblies. Approximately 71% (n = 498) of the 704 neurons tested for changes in firing rate as a result of alterations in blood pressure were involved in a functional correlation with at least one other cell; within this cell subset, a nonresponsive VRC E-tonic cell was correlated with the greatest number of neurons (n = 36), all of which were VRC cells, and with a variety of correlational features. A similar percentage of neurons responsive to at least one direction of change in blood pressure (70%; 333 of 474 cells) was involved in one or more short time-scale interactions with other cell(s); in this case, the most well-connected neuron (n = 26 VRC cells) was a tonic VRC cell whose firing rate increased throughout the E phase with a burst of activity at the E-to-I phase transition.

Inferences drawn from these sets of relationships support a number of network operations and inform model development. Several of these are considered in the following sections.

VRC inspiratory neuron chain has an embedded array of recurrent inhibitory loops.

Multisite VRC recordings in Animal 2 (Fig. 4, A and B) also revealed diminished inspiratory neuron chain activity during elevations of blood pressure (Fig. 4C). These recording sites were spread along 5 mm of the rostral-caudal extent of the VRC (Fig. 4A), with cells showing a variety of firing patterns in their respiratory cycle-triggered histograms (CTHs; Fig. 4B), including I-Dec, I-Aug, and tonic expiratory cells. The color of the CTH type is carried through in both the cell color in Fig. 4A and the cell groupings in Fig. 4C (left); a cell’s response to increased blood pressure is also denoted to the left of the cell ID. These responses show that most of the I-Aug and I-Dec cells had decreased firing rates in response to increased blood pressure. Concurrently, firing rates of pericolumnar expiratory neurons increased (plots for neurons 813, 815, 820, 844, and 847 in Fig. 4C). High-frequency oscillations and the respiratory cycle frequency were perturbed, as illustrated in the s-transform for neuron 808, and gasp-like events were evident at the end of some inspiratory neuron bursts and in the phrenic nerve record (asterisks, Fig. 4C).

Signatures of interaction identified within the column included offset troughs in correlograms triggered by several of the expiratory neurons, features indicative of inhibitory actions on a rostral cluster of inspiratory neurons (Fig. 5A, red connecting lines; Fig. 5B, 1–9). Evidence for expiratory neuron inhibition of the inspiratory chain extended to the appearance of offset troughs in averages of the phrenic nerve signal triggered by neurons 815 and 820 (Fig. 5G, e and f). The expiratory neurons were mutually correlated, forming a tightly coordinated assembly with a belt-like distribution around the inspiratory neuron chain (Fig. 5C, 10–18, light blue dash-dot line). Tonic expiratory neuron 815 targeted all four monitored rostral VRC inspiratory neurons, including pre-inspiratory neurons 801 and 831 and augmenting inspiratory neurons 802 and 808 (see Fig. 4B for respiratory cycle-triggered histograms). The correlogram for pair 801–808 had a central trough (Fig. 5D20), a feature commonly interpreted as indicating that the two neurons received coordinated inputs of opposite sign, a possible mechanism for the increased firing rate of 801 as the rate of 808 declined. The 802–808 correlogram had a central peak, presumably a consequence of shared excitatory and/or inhibitory influences of like sign, some unobserved (Fig. 5D22).

Fig. 5. — Correlational signatures of interaction support blood pressure evoked expiratory neuron modulation of inspiratory chain recurrent loops. A: correlation feature map for *Animal 2* gives a graphical summary of responses to blood pressure elevations and detected short-time scale changes in firing probability (peaks and troughs) that support recurrent loop operations within the inspiratory neuron chain and between the chain and a “belt” of expiratory neurons. See text for details. Numbers in small shaded circles correspond to represented cross-correlograms. *B–F*: cross-correlograms for pairs of neurons in *Animal 2* represented in Fig. 4. The detectability index value of each feature is as follows (O, offset; C, central; P, peak; T, trough): 1: OT, 6.6; 2: OT, 31.4; 3: OT, 7.7; 4: OT, 6.6; 5: OT, 5.4; 6: OT, 6.5; 7: OT, 6.9; 8: OT, 6.5; 9: OT, 4.7; 10: CP, 14.3; 11: CP, 5.3; 12: CP, 5.9; 13: CP, 7.0; 14: CP, 20.0; 15: OP, 53.9; OT, 7.3; 16: CP, 11.9; 17: CP, 4.8; 18: OP, 15.4; 19: OP, 7.5; 20: CT, 6.3; 21: OP, 4.4; 22: CP, 10.9; OT, 4.3; 23: OP, 5.7; 24: OP, 6.4; OT, 6.4; 25: OP, 6.6; OT, 7.8; 26: OP, 5.4; OT, 6.2; 27: CP, 7.3; 28: OP, 5.4; OT, 4.3; 29: OP, 27.9; OT, 5.8 (see Fig. 6); 30: CP, 6.8; 31: OP, 6.0; 32: CP, 5.2; 33: OP, 5.5; 34: OP, 7.0; 35: OP, 8.5; 36: OP, 19.0; OT, 17; 37: CP, 13.4; 38: CP, 24.1; 39: CP, 22.6; 40: CP, 16.7; OT, 8.0; 41: CT, 4.9; 42: CT, 4.1; 43: CT, 6.8; and 44: CT, 7.2. Number of spikes for each neuron represented in Fig. 4 used to calculate cross-correlograms: *801*: 92,503; *802*: 260,467; *808*: 254,595; *809*: 66,373; *812*: 105,528; *813*: 168,259; *814*: 53,900; *815*: 319,904; *818*: 85,646; *820*: 245,476; *822*: 48,453; *826*: 80,901; *831*: 69,215; *842*: 71,5673; *844*: 5,539; 847: 38,755; *851*: 103,820; and *869*: 35,481. G: averages of rectified left (contralateral) phrenic nerve signals are labeled in the feature map with encircled letters: a and b: triggered by rostral pre-I neurons 801 and 831; c and d: triggered by I-Aug neurons 802 and 808; e and f: triggered by expiratory neurons 815 and 820.

Cross-correlograms triggered by the four rostral inspiratory neurons revealed offset peak features indicative of excitatory I-Driver actions (Fig. 5D, 19, 21, and 23–35). We note here and consider further subsequently that several correlograms triggered by 802 had positive-lag offset peaks (Fig. 5D, 23, 27, and 28) and some also had negative-lag offset troughs (Fig. 5D28). In contrast, correlograms for the same targets triggered by 808 had offset peaks, but no negative-lag troughs were detected (Fig. 5D, 30–35). These differences extended to features in the spike-triggered averages of the rectified phrenic nerve motor output signal (Fig. 5G, c and d). The average triggered by 802 had a sharp positive-lag peak within a larger trough, whereas the average triggered by 808 featured a broader offset peak. The phrenic averages triggered by pre-I neurons 801 and 831 had broader central peaks in keeping with broader upstream excitatory influences on inspiratory drive (Fig. 5G, a and b). In this context we also note that correlograms for pairs of the target neurons common to both putative I-Drivers contained central peaks (Fig. 5E, 36–40), features consistent with the shared excitatory drives from 802 and 808. Correlograms triggered by spike trains of these putative inhibitory inspiratory neurons for expiratory neuron targets contained troughs consistent with inhibition mediated by the synchronously discharging trigger neurons (Fig. 5F, 41–44).

Perturbation of high-frequency oscillations and “low-drive” augmented burst events.

Three attributes that define inspiratory drive — namely, neuron firing rates, inspiratory burst duration, and HFO frequency and power — were altered by transient elevation of blood pressure and subsequent functional inhibition of the inspiratory chain. Cross-correlograms for I-Driver neuron 802 and several inspiratory inhibitory neurons located at sites along the length of the chain in Animal 2 were calculated. The interval between each positive-lag offset peak and the peak located before the negative-lag offset trough was measured for each correlogram and used to estimate the HFO frequency that would be generated by each recurrent inhibitory loop (Fig. 6A). Although the frequency estimates derived from this sample of correlograms triggered by a single putative I-Driver neuron were lower than the estimate of ∼60 Hz calculated from corresponding spike-triggered averages of the unrectified and rectified phrenic nerve signals (Fig. 6B), fluctuations in the s-transform plots for neurons 802 and 808 in the HFO frequency band were consistent with this range of estimates (Fig. 6C). The significance plots indicate the absence of significant HFOs during elevated blood pressure. Variations in the modulation of t-E neuron activity resulting from, for example, baroreceptor-evoked excitation (Fig. 6D) would alter thresholds along the inspiratory neuron chain, thereby changing the coherence of oscillations contributed by the different loop paths between the I-Drivers and their inhibitory targets, including other routes such as via the pons.

Fig. 6. — Inspiratory neuron correlation features support recurrent inhibitory loop contribution to HFO generation. A: cross-correlograms from *Animal 2* triggered by neuron 802 for multiple target neurons with positive-lag peak and negative-lag trough features used to estimate the HFO frequency that would be generated by each recurrent inhibitory loop. The detectability index values for positive-lag peaks and negative-lag troughs are (O, offset; C, central; P, peak; T, trough): *802–812*: OP, 5.4; OT, 6.3; *802–853*: OP, 5.7; OT, 4.7; *802–851*: OP, 28.0; OT, 5.9; *802–822*: OP, 9.9; OT, 6.4; *802–826*: OP, 6.6; OT, 7.8. Number of spikes for each neuron used to calculate cross-correlograms is as follows: *802*: 260,467; *812*: 105,528; *822*: 48,453; *826*: 80,901; *851*: 103,820; *853*: 56,853. B: averages of unrectified and rectified left phrenic nerve signals triggered by spikes of neuron 802. Empirical confidence bands (gray traces) show ± 3 SE of the mean difference between the average and shuffled controls. C: S-transforms for spike trains from neurons 802 (*top*) and 808 (*bottom*) around the third elevation of blood pressure; magnitude values range from 0 to 57.0 for neuron 802 and from 0 to 58.7 for neuron 808. Dashed lines highlight frequency band for HFOs (50–120 Hz). Points with significant s-transform magnitudes within this band were identified for each neuron (black areas in significance plots; P < 0.005; FDR = 0.03). Note lack of significant magnitude in the HFO frequency band during elevated blood pressure. D: correlation feature map with inferred functional recurrent inhibitory loops elements highlighted in red. See text for details. FDR, false discovery rate; HFO, high-frequency oscillation.

Discoordination of HFOs was seen in four trials of elevated blood pressure performed in Animal 2 (Fig. 7). These trials contained a period of augmented phrenic activity (highlighted with red asterisks in Fig. 7A and yellow bars in Fig. 7B) coincident with the “escape” of I-Driver neuron 808 from putative recurrent inhibition, suggesting a possible circuit mechanism for generation of augmenting burst events during intervals of low inspiratory drive, as during elevated blood pressure (Fig. 7A). A comparison of the parallel spike trains recorded during these burst events with immediately preceding respiratory cycles revealed “gaps” in the impulse generation of the inhibitory t-E neurons at moments when the discharge rates of 808 and its downstream followers increased (Fig. 7B).

Other baroresponsive circuit modules with pontine neurons linked to the raphe and VRC.

Perturbations of blood pressure in Animal 3 altered the respiratory rhythm and inspiratory drive, as clearly seen from the integrated phrenic nerve trace and altered cell activity shown in Fig. 8B. Pontine cell 515 and raphe unit 910 increased their firing rates in response to reduced blood pressure, whereas the firing rate of VRC neuron 604 declined. Offset peaks in the cross-correlograms for neurons 910 and 515 triggered by 604 (Fig. 8, C and D2), in addition to the neurons’ responses to reduced blood pressure, provide evidence for a disfacilitatory action of 604 on 515 and 910. The reduction in firing rate of cell 604 during periods of reduced blood pressure would result in less excitation of neurons 515 and 910, potentially restraining/shaping their enhanced activities. In agreement with this hypothesis, we note that the offset peak feature for the 604–910 pair, apparent in cross-correlograms calculated from the entire recording including the data sample during the blood pressure elevation trials, was absent during the subsequent trials with reduced pressure (Fig. 8C). The two target neurons were elements of a larger inferred circuit chain in which raphe neuron 910 was subject to gain modulation by both pontine and raphe neurons (Fig. 8, D2, D3, D7, and E). A putative chain of functional connectivity, beginning with VRC cell 604, linking pontine neurons to the VRC and raphe is highlighted in the correlation feature map (Fig. 8E, dashed purple arrows).

Fig. 8. — VRC neurons with reduced firing rates during transient declines in blood pressure have distributed functional links in the pons and medullary raphe. A: recording site coordinates from *Animals 3* (*top*) and 4 (*bottom*) mapped in a brain stem atlas. Recording sites and neuron ID codes ringed in yellow were sampled in the second recording in *Animal 4*; *, neurons (both with ID code 520) recorded from the same location and included in correlograms and feature maps shown for both recordings. Data from *Animal 4* have been previously published; see Table 1. B: firing rate modulation of simultaneously monitored pontine, raphe, and VRC neurons in *Animal 3* during sequential elevations and reductions of blood pressure. Tracheal pressure and end-tidal CO₂ traces show regular transient fluctuations due to periodic lung inflations to reduce atelectasis and its consequences. C: positive-lag peaks in cross-correlograms for VRC trigger neuron 604 and raphe target neuron 910. A peak was present in the correlogram calculated for the entire record (1a, detectability index = 5.8) and for samples that included elevated blood pressure perturbations (1b and 1c). No feature was detected from the data set confined to the stimulus series when blood pressure was lowered (1d). *D 2–7*: other correlograms with offset peak or trough features. Detectability index values are as follows (O, offset; C, central; P, peak; T, trough): 2: OP, 10.4; 3: OP, 7.9; 4: OP, 3.5; 5: OP, 3.3; 6: OT, 3.0; 7: OP, 3.4. Numbers of spikes for neurons represented in B that were used to calculate cross-correlograms: *508*: 67,089; *515*: 1,739; *512*: 3,648; *904*: 2,816; *906*: 40,064; *910*: 22,216; *604*: 98,601. E: feature map shows correlational linkages and responses of the monitored group in *Animal 3*. This map supports a circuit in which raphe neuron 910 was modulated by both pontine and raphe neurons. Dashed purple arrows indicate a putative chain of functional connectivity, beginning with cell 604, with pontine neurons linked to the raphe and VRC. See text for details. F: correlograms for neurons monitored in *recording 1* from *Animal 4*. Detectability index values for offset troughs or peaks: 1: OP, 7.2; 2: OP, 6.1; 3: OT, 4.8; 4: OT, 5.5; OP, 3.7; 5: OT, 5.4; OP, 4.7. Number of spikes for each neuron used to calculate cross-correlograms: *520*: 81,062; *808*: 27,821; *810*: 15,217; *820*: 82,521; *823*: 196,592; *919*: 9,912. G: correlation feature map for the first recording from *Animal 4*. See text for details. VRC, ventral respiratory column.

Groups of responsive VRC, raphe, and pontine neurons linked by functional associations were also identified in two recordings from Animal 4. In the first data set, neuron 810, like VRC neuron 604 shown in Fig. 8B, had a reduced discharge rate during intervals with lowered blood pressure. Notably, the peak and trough features in correlograms triggered by 810 were consistent with excitatory actions on raphe neuron 919 and VRC neuron 823, and with functional inhibition of pontine neuron 520 and VRC cells 808 and 820 (Fig. 8, F and G).

A neural assembly identified in the second recording from Animal 4 (Fig. 9A) included pontine neuron 517 with widespread heterogeneous correlational linkage features in correlograms with other pontine neurons and with target cells in the raphe and VRC (Fig. 9, B1–B8 and C). Gravitational clustering analysis of a subset of the spike train data recorded during the blood pressure elevations identified dynamics in coordinated spiking; note that peak features in cross-correlograms triggered by pontine cell 517 for VRC neurons 906, 914, and 925 (Fig. 9B, 6–8) were reflected in the “aggregation” of gravity particles corresponding to these cells (Fig. 9D). The projection of gravity particle trajectories from n-space to a plane together with plots of the distances between particle pairs (Fig. 9, D–F) provided further evidence for spike synchrony detected in the cross-correlograms. The firing synchrony of cells 517 and 925 is illustrated by the red portions of the bold line in the plot of particle pair-wise distance as a function of time (PDFT, Fig. 9E, top), which correspond to times when the distance between particles for neurons 517 and 925 was less than that of all particle pairs within surrogate data (confidence limits are gray lines; see legend of Fig. 9 for more detail). Similar information for this and the remaining 14 particle pairs is illustrated in a plot of significant aggregation (Fig. 9E, bottom) where vertical lines indicate time steps during which particles of an indicated pair were significantly close to one another. A comparison with similar measures made during the lowering of blood pressure revealed continuing interactions between pontine neuron 517 and raphe cell 925 and other pairs (Fig. 9F), although several other previously detected linkages (indicated by red rectangles around the pair labels in Fig. 9F, bottom and by green dashed lines in Fig. 9G) were diminished or no longer detected. The firing rates of synchronously discharging inspiratory neurons 340 and 314 (Fig. 9B10) were also altered during the blood pressure perturbations.

Disruption and resetting of slow oscillations in the inspiratory motor pattern.

Superimposed on the stimulus-evoked modulations of inspiratory drive in the phrenic motor discharge in Animal 4 (recording 2) were slower oscillations with a frequency of ∼0.02 Hz also apparent in the blood pressure trace (Fig. 9A inset, red arrowheads). The hypothesis that blood pressure perturbations alter slow wave frequency modulation of inspiratory drive was evaluated in three animals with similar oscillations.

Experimental alterations of blood pressure in Animal 5 changed the inspiratory motor pattern as noted previously (Fig. 1) and altered the discharge patterns of neurons recorded at distributed sites within the brain stem (Fig. 10, A and B). Following deflation of the balloon in the inferior vena cava to remove the stimulus to reduce blood pressure, the respiratory cycle frequency declined (integrated phrenic nerve trace, Fig. 10B). During this poststimulus period, higher spike frequencies (∼40 Hz) within the inspiratory neuron bursts (e.g., s-transform of neuron 815 spike train, Fig. 10B) were disrupted, as were slower oscillations (∼0.1 Hz) in the rate modulations of some neurons. As the respiratory cycle returned to control values, the slower oscillations were again present; yellow boxes highlight these oscillations in the firing rate histograms of several neurons and the s-transform of neuron 908 (Fig. 10B). Cross-correlogram features included evidence for interactions involving neurons with the slow oscillations. Pontine t-E neuron 514 discharged asynchronously with VRC neuron 828 (Fig. 10C7) and synchronously with raphe neurons 908 (Fig. 10C6) and 916 and VRC cell 851, all of which had correlational signatures of divergent and convergent actions upon raphe t-E neurons 923 and 924 (e.g., Fig. 10C, 5 and E). Other correlogram features included evidence for recurrent inhibitory loop operations between VRC NRM neuron 828 and two t-E raphe neurons (924 and 926) as well as VRC I-Aug neuron 844 (Fig. 10C, 1 and E). Signatures of inspiratory HFOs were also evident in the peak and bilateral trough features of averages of the phrenic nerve signal triggered by inspiratory neurons 815 and 819 (Fig. 10D).

Fig. 10. — Primary and secondary multispectral responses to transient reductions in blood pressure. A: recording site coordinates for *Animal 5* mapped in brain stem atlas. B: firing rate histograms and s-transforms for pontine, raphe, and VRC neurons recorded simultaneously during a series of blood pressure reductions. Responses to blood pressure elevations and reductions are indicated to the left of each trace. Several intervals with slower oscillations (∼0.1 Hz) are highlighted in yellow. Lower traces show integrated phrenic nerve signal, blood pressure (BP), tracheal pressure (TP), end-tidal CO₂ (ET-CO₂), and stimulus marker. Tracheal pressure and end-tidal CO₂ traces show regular transient fluctuations due to periodic lung inflations to reduce atelectasis and its consequences. C: cross correlograms; detectability index values for trough or peak features (O, offset; C, central; P, peak; T, trough): 1: OP, 5.8; OT, 5.0; 2: OP, 3.2; 3: OT, 4.2; 4: OP, 4.6; 5: OP, 6.8; 6: CP, 3.2; 7: CT, 5.3; 8: OT, 3.5. Number of spikes for each neuron used to calculate cross-correlograms: *514*: 8,416; *803*: 40,790; *828*: 187,161; *851*: 316,265; *901*: 5,418; *903*: 8,144; *906*: 32,882; *908*: 297,792; *916*: 26,204; *923*: 8,476; *924*: 7,639. D: spike-triggered averages of rectified phrenic nerve signal triggered by neurons 815 and 819. E: correlation feature map. See text for details. VRC, ventral respiratory column.

Spontaneous multispectral fluctuations in the breathing pattern of Animal 6 included slow quasi-periodic (∼0.02 Hz) changes in inspiratory drive characterized by bouts of tachypnea and altered HFOs coordinated with elevations of mean arterial pressure (Fig. 11A, left; red arrows mark the onset of episodes of increased inspiratory drive). Transient occlusions of the descending aorta caused elevations in blood pressure that significantly prolonged the interval between the episodes of tachypnea (one-tailed t test, P = 0.000017; Fig. 11A, horizontal arrows). This rhythm resetting was followed by a poststimulus response to the step decrease in blood pressure at the end of each perturbation that included an increase in respiratory cycle frequency and inspiratory drive along with alterations in rostral and caudal VRC neuron discharge patterns (Fig. 11, B and C).

Fig. 11. — Transient step elevations of blood pressure reset slow oscillations in inspiratory drive and respiratory frequency. A: multiunit and integrated phrenic nerve traces with s-transform during a recording in *Animal 6*. Red arrows mark onset of episodes of spontaneous increased inspiratory drive and respiratory cycle frequency; intervals between these tachypneic episodes were lengthened when blood pressure was deliberately elevated during periods between episodes (horizontal arrows). B: sagittal view of recording site coordinates for neurons with firing rate histograms shown in C. C: firing rate histograms for monitored neurons, multiunit and integrated phrenic nerve signals, and blood pressure during a period (outlined in red) around the end of the first elevated blood pressure trial in A. See text for details.

Animal 7 also exhibited spontaneous bouts of slow quasi-periodic oscillations in rostral ventrolateral medulla (RVLM) and intermediate ventrolateral medullary (IVLM) neurons and in inspiratory drive associated with elevations of mean arterial pressure (Fig. 12, A and B). During these waves, some neurons, including 438 and 820, had peak rates that occurred out of phase with respect to active intervals in other neurons, such as 410 and 412. Periods with more uniform respiratory cycles were interspersed between these episodes, during which arterial blood pressure declined. This recurring pattern suggested that baroreceptors intermittently evoked a slow wave oscillator that actively increased inspiration and blood pressure until a threshold was reached and the network switched to another operating mode, during which blood pressure gradually declined and inspiratory drive increased (Fig. 12C).

Fig. 12. — Slow wave oscillations in phrenic nerve and brain stem neurons are evoked following return to control blood pressure steps after elevated intervals; correlation features support a role for cardiac modulated recurrent loop circuits. A: dorsal view of recording site coordinates for neurons monitored in *Animal 7* and represented by firing rate histograms shown in B and D. The cells labeled in A and included in subsequent panels were located within the rostral or intermediate ventrolateral medulla as well as within the VRC. B: firing rate histograms and phrenic nerve traces show rate modulations during spontaneous slow oscillations associated with elevations of blood pressure. C: detail of highlighted record in B indicated by arrow. The yellow box highlights an interval between two periods of slow oscillations during which the phrenic drive increased as the blood pressure decreased. See text for details. D: firing rate histograms and s-transforms show multispectral tuning of inspiratory drive by elevations in blood pressure; s-transform magnitude values for neuron 818 range from 0 to 24.1, and for the multiunit phrenic activity from 0 to 107.2. FDR for significance plot < 0.03. Slow oscillations in the phrenic motor pattern are indicated in the s-transform of multiunit phrenic activity. E: cross-correlograms. Although the negative-lag troughs in E1 and E2 appear small compared with the positive-lag peaks, both features are significant. The red boxes along the x-axes indicate significant bins based upon comparison with 961 correlograms calculated using surrogate spike trains. The center gray line is the mean of the surrogate correlograms; *top* and *bottom* gray lines are the maximum and minimum values, respectively, of the surrogate correlograms for each bin. Detectability index values for troughs or peaks (O, offset; C, central; P, peak; T, trough): 1: OP, 5.7; OT, 6.2; 2: OP, 5.6; OT, 4.7; 3: CP, 9.1; 4: OP, 5.8; 5: CT, 3.4. Number of spikes for each neuron used to calculate cross-correlograms: *410*: 29,602; *412*: 59,418; *438*: 46,941; *802*: 5,502; *820*: 35,707. F: cardiac cycle-triggered histograms for neurons 438, 410, and 412; vertical gray lines aid comparison of the cells’ time-locked modulations in firing rate. G: autocorrelograms for neurons 438 and 820. H: correlation feature map for subset of neurons represented in firing rate histograms illustrates recurrent loop circuits suggested by cross-correlograms. See text for details. FDR, false discovery rate; IVLM, intermediate ventrolateral medulla; RVLM, rostral ventrolateral medulla; VRC, ventral respiratory column.

This hypothesis was supported by changes in the inspiratory motor pattern evoked by elevated blood pressure perturbations. Successive inflations of a catheter in the descending aorta raised blood pressure and caused reductions in inspiratory drive; each period of elevated blood pressure was followed by poststimulus activity with slow oscillations in the phrenic motor pattern (shown in the s-transform of phrenic nerve activity as a thickening/brightening of the time-frequency band between 0.03 and 0.06 Hz) synchronized with rate modulations and the presence of mid-frequency oscillations (∼30 Hz) in some of the monitored neurons (Fig. 12D, s-transform for cell 818). Cross-correlogram features for pairs 438–410 and 438–412 were consistent with convergent inhibition of 410 and 412 upon rostral neuron 438 (negative-lag troughs) and the latter neuron’s participation in a recurrent loop circuit via divergent excitation (positive-lag peaks; Fig. 12E, 1 and 2). Their respective rate modulations time-locked to the cardiac cycle supported this circuit organization (Fig. 12F), as did the central peak in the 410–412 correlogram indicating shared inputs (Fig. 12E3). Other correlogram features suggested an additional excitatory action of rostral trigger neuron 438 on neuron 820 (Fig. 12E4), an interpretation consistent with their distinct autocorrelograms (Moore et al. 1970) (Fig. 12G; oscillations in the firing rate of cell 438 were reflected on both sides of zero in the cross-correlogram, whereas the autocorrelogram of neuron 820 indicated a lack of oscillations in the firing rate of that cell) and their similar spontaneous and evoked rate modulations (Fig. 12, B and D). An offset trough feature (Fig. 12E5) was consistent with functional inhibitory actions of neuron 438 on VRC neuron 802, which had a tonic expiratory discharge profile. These linkages are summarized in Fig. 12H.

DISCUSSION

The results extend prior observations on functional connectivity between the pons and VRC circuit mechanisms for modulation of inspiratory drive and breathing frequency (Dutschmann and Dick 2012; Nuding et al. 2009a; Segers et al. 2008; Zuperku et al. 2019). As noted in Table 1, responses of some neurons to hypocapnic apnea or other stimulation of peripheral or central chemoreceptors have been reported previously (Nuding et al. 2009b, 2015; Ott et al. 2011, 2012; Segers et al. 2015). When considered with this prior work, the present results support brain stem chain and loop-circuit operations that modulate t-E neuron hubs to control breathing.

The results also support a model of the brain stem respiratory network that tunes breathing during changes in blood pressure (Lindsey et al. 2012). The model framework includes loop operations in the nucleus of the solitary tract (NTS; Fig. 13, square A), where neurons, including those modulated by recurrent inhibition, signal the rate and direction of blood pressure changes sensed by baroreceptors (Bailey et al. 2008; Kolpakova et al. 2017; Mifflin 2001; Rogers et al. 1996). These baroresponsive neurons drive raphe-pontomedullary circuit loops (Fig. 13B) that ultimately target VRC t-E neurons (Fig. 13C). Mechanisms leading to increased activity in VRC t-E neurons (e.g., excitation and disinhibition from raphe neurons work in a “push-pull” fashion to increase t-E activity) are labeled and highlighted in yellow. Blue arrows within cell circles indicate directions of change in firing rate leading to decreased inspiratory drive following excitation of baroreceptors. Red dashed ovals indicate recurrent inhibitory loops, which when imbalanced would cause disruptions in oscillations.

Fig. 13. — Summary of results and model framework for the brain stem respiratory network. The results extend prior work and support a model of a linked-loop brain stem respiratory network that tunes breathing during changes in blood pressure. Neurons in the nucleus of the solitary tract (NTS) receive input from baroreceptors (A) and drive raphe-pontomedullary circuit loops (B) that target VRC t-E neurons (C). Mechanisms leading to increased activity in VRC t-E neurons are labeled and highlighted in yellow; blue arrows within cell circles indicate directions of change in firing rate; red dashed ovals indicate recurrent inhibition. Gray dashed lines indicate detailed results that inform and support the model. Circuit modules inferred from results support participation of pontine neurons in multi-path operations for inspiratory drive modulation (*D1–5*; inferred connections, pontine sources, and raphe/VRC targets are colored to match points made in the correspondingly colored text balloon). E: model proposes that elevation of blood pressure enhances inhibition from VRC t-E neurons and leads to reduced inspiratory drive and HFO decoherence; features detected in cross-correlograms triggered by putative I-Driver neuron “1” are reflected in phrenic nerve activity averaged with respect to spikes in the same neuron. F: results support recurrent loop circuit modules within the RVLM that modulate VRC t-E neuron inhibition of inspiratory drive and produce slow oscillations in the breathing pattern. See text for further details. HFO, high-frequency oscillation; RVLM, rostral ventrolateral medulla; VRC, ventral respiratory column.

Circuit modules inferred from sets of neuronal responses and correlational signatures of interaction within these results support the hypothesis that pontine neurons participate in multipath operations for inspiratory drive modulation (Fig. 13D) and provide evidence for circuits that link raphe and VRC neurons (Fig. 13, D1–D3) and for efference copy loops within the inspiratory neuron chain that tune inspiratory drive (Fig. 13, D4 and D5). The respiratory motor pattern has rhythms in frequency bands above and below breathing, but details of the underlying network mechanisms for these quasi-periodic oscillations have remained obscure. High-frequency oscillations commonly observed in the spiking of inspiratory neurons (Funk and Parkis 2002; Ghali and Marchenko 2013) are known to depend upon inhibitory interactions within the brain stem (Sebe et al. 2006). During the present study, we noted that when elevations in blood pressure suppressed the HFOs, VRC inspiratory neuron chain operations were also disrupted, supporting the hypothesis that breathing is normally constrained by intrinsic recurrent inhibitory loop circuits (cf. Baertsch et al. 2018, 2019; Ott et al. 2012; Segers et al. 1987). The model proposes that when blood pressure is elevated, the activity of t-E neurons increases and their inhibition of I-Driver neurons is enhanced (Fig. 13E). A consequence of this reduction in inspiratory drive is reduced activity in inhibitory I-Dec neurons, leading to disinhibition of t-E neurons, which further amplifies the suppression of inspiration. These operations result in decreased recurrent inhibition within the inspiratory chain, and thereby disrupt the amplitude and coherence of HFOs and enhance the probability of augmented burst generation driven by a subset of disinhibited I-Driver neurons.

Experimental changes in blood pressure also evoked or reset slower quasi-periodic fluctuations in the respiratory motor pattern in the frequency range of Lundberg B-wave oscillations in arterial pressure (∼0.04 Hz). Neuronal responses, distinct profiles of cardiac pulse modulation of firing rates, and detected functional connectivity suggest that these slow oscillations reflect recurrent loop operations between neurons in the rostral ventrolateral medulla (RVLM) that modulate t-E neuron inhibition of inspiratory drive and breathing frequency (Fig. 13F). The “dual” excitatory and inhibitory actions of the RVLM neurons inferred from firing-rate histograms and correlogram features (Fig. 12) suggest distinct actions of multiple transmitter-receptor systems or intervening interneurons, as considered in other contexts elsewhere (e.g., Fig. 11 in Nuding et al. 2015). This circuit representation includes baroreceptor reflex circuit models based on a large body of research (Barman 2020; Guyenet et al. 2013). The slow oscillations have been described previously but were identified as Mayer waves (Preiss et al. 1975), which are commonly considered to have slightly higher frequencies of ∼0.1 Hz (Cherniack et al. 1969). Mayer waves have been observed in normal human subjects and can be evoked or enhanced by hypotension and chemoreceptor signaling, or by conditions that increase sympathetic drive. They have been attributed to a central rhythm-generating circuit and baroreceptor-mediated arterial pressure feedback control operations (Censi et al. 2000; Cherniack et al. 1969; Julien 2006; Lescot et al. 2005; Mautner-Huppert et al. 1989; Montano et al. 2001; Morris et al. 2010).

Consideration of methods and approach.

Advantages and limitations of the approaches used to identify responses and functional connectivity of simultaneously recorded neurons have been described (Grün and Rotter 2010; Nuding et al. 2015; Ott et al. 2012; Segers et al. 2008). Standard methods were used to perturb blood pressure (Lindsey et al. 1998; Poliaček et al. 2011). The transient increases or decreases in blood pressure by inflation of embolectomy catheters in the descending aorta and inferior vena cava, respectively, were sufficient to evoke distinct changes in inspiratory drive and respiratory cycle frequency, including alterations in the motor pattern with a frequency of slow Lundberg oscillations. Notably, these changes corresponded to the evoked modulation of firing rates in neurons with distinct rate profiles in averages triggered by cardiac pulses, providing further support for a contributing role for baroreceptors (Barman 2020).

Experiments were conducted in bilaterally vagotomized animals to remove the influence of pulmonary stretch receptors on the respiratory motor pattern. This procedure, commonly used in studies of brain stem mechanisms that control breathing, also removes the influence of aortic baroreceptors (Amorim et al. 2019) and alters the respiratory modulated discharge patterns of some brain stem neurons relative to vagal intact animals (Dick et al. 2008; Morris et al. 2010).

Relationship to other prior work.

The results confirmed and extended prior work on the effects of changes in blood pressure on the respiratory motor pattern, which had been viewed with some skepticism (cf. McMullan et al. 2009; McMullan and Pilowsky 2010). The use of 30-s step-and-hold perturbations that increased or decreased blood pressure revealed changes in inspiratory drive not apparent with short pulse increments of pressure applied only during an expiratory phase (Baekey et al. 2010). The results support the hypothesis that phasic and tonic decrementing expiratory or post-inspiratory neurons that modulate inspiratory drive via fluctuations of their inhibitory actions are widely distributed in the VRC (Morris et al. 2018; Segers et al. 2015) and not confined to the Bötzinger and pre-Bötzinger complexes (Ausborn et al. 2018). A complementary study of the neural control of blood pressure using optogenetics and neuronal tracing has recently been published (Menuet et al. 2020).

The results also suggest that HFOs, in addition to providing effective motor drive to inspiratory muscles (Funk and Parkis 2002), represent signatures of recurrent inhibitory circuit loop operations that constrain high inspiratory drive to suppress “inappropriate escape” of augmented breaths or gasps. Under this hypothesis, decoherence of HFOs during low respiratory drive enhances the probability of sighing, a motor pattern that would counter the consequences of atelectasis caused by attendant small tidal volumes. Previously observed augmented bursts generated under low chemoreceptor drive during the transition to hypocapnic apnea are consistent with this notion (e.g., Fig. 10 in Nuding et al. 2015).

Summary.

The evidence for recurrent loop circuits identified in the present study and earlier cited work supports a network architecture that incorporates opposite actions between groups of neurons that can operate to stabilize the activity of the entire assembly of neurons within a range of firing rates and patterns. Excitatory actions upon the inhibitory group would be countered by the resultant consequences on the recurrent inhibition, and vice versa. In the model, these internal equilibrium-seeking mechanisms work in parallel with negative feedback reflex controls to tune inspiratory drive and patterning. The recurrent inhibitory arm of the loops can limit the amplitude and duration of the excitatory arm’s actions on downstream targets, as is the case for feed-forward circuit motifs also embedded in the present model, and as considered elsewhere in the context of incremental memory induction by carotid chemoreceptors (Lindsey et al. 2018) and in other brain circuits (Hennequin et al. 2017). The recurrent loops can also tune spike synchrony, as in HFO generation and production of stochastic patterns of synchrony that increase the gain of signal pathways operating through coordinated parallel channels (Arata et al. 2000; Hennequin et al. 2017). Given the influence of the breathing pattern on other brain functions as noted in the introduction, the present results also suggest the hypothesis that fluctuations in blood pressure can indirectly, via this influence, also have an impact on memory formation, attentional modulation, and the sense of corporeal awareness.

GRANTS

This work was supported by National Institutes of Health (NIH) Grant R01/37 NS019814, Grant R01NS046062 as part of the National Science Foundation/NIH Collaborative Research in Computational Neuroscience (CRCNS) Program, and SPARC (Stimulating Peripheral Activity to Relieve Conditions) Common Fund OT2OD023854.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

AUTHOR CONTRIBUTIONS

L.S.S., S.C.N., M.M.O., K.F.M., and B.G.L. conceived and designed research; L.S.S., S.C.N., M.M.O., K.F.M., and B.G.L. performed experiments; L.S.S., S.C.N., M.M.O., R.O., K.F.M., and B.G.L. analyzed data; L.S.S., S.C.N., M.M.O., R.O., K.F.M., and B.G.L. interpreted results of experiments; L.S.S. and B.G.L. prepared figures; L.S.S., S.C.N., K.F.M., and B.G.L. drafted manuscript; L.S.S., S.C.N., K.F.M., and B.G.L. edited and revised manuscript; L.S.S., S.C.N., M.M.O., R.O., K.F.M., and B.G.L. approved final version of manuscript.

ACKNOWLEDGMENTS

We thank P. Alencar, D. Shuman, K. Ross, A. Ross, K. Carpentier, and P. Barnhill for excellent technical and surgical assistance.

Present e-mail addresses: M. Ott: mott@nuhs.edu; R. O’Connor: roconnor@alum.mit.edu.

REFERENCES

Aertsen AM, Gerstein GL. Evaluation of neuronal connectivity: sensitivity of cross-correlation. Brain Res 340: 341–354, 1985. doi: 10.1016/0006-8993(85)90931-X. [DOI] [PubMed] [Google Scholar]
Amorim MR, Pena RFO, Souza GMPR, Bonagamba LGH, Roque AC, Machado BH. Firing properties of ventral medullary respiratory neurons in sino-aortic denervated rats. Exp Physiol 104: 39–49, 2019. [DOI] [PubMed] [Google Scholar]
Arata A, Hernandez YM, Lindsey BG, Morris KF, Shannon R. Transient configurations of baroresponsive respiratory-related brainstem neuronal assemblies in the cat. J Physiol 525: 509–530, 2000. doi: 10.1111/j.1469-7793.2000.t01-1-00509.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
Arshamian A, Iravani B, Majid A, Lundström JN. Respiration modulates olfactory memory consolidation in humans. J Neurosci 38: 10286–10294, 2018. doi: 10.1523/JNEUROSCI.3360-17.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
Ausborn J, Koizumi H, Barnett WH, John TT, Zhang R, Molkov YI, Smith JC, Rybak IA. Organization of the core respiratory network: Insights from optogenetic and modeling studies. PLoS Comput Biol 14: e1006148, 2018. doi: 10.1371/journal.pcbi.1006148. [DOI] [PMC free article] [PubMed] [Google Scholar]
Baekey DM, Molkov YI, Paton JFR, Rybak IA, Dick TE. Effect of baroreceptor stimulation on the respiratory pattern: insights into respiratory-sympathetic interactions. Respir Physiol Neurobiol 174: 135–145, 2010. doi: 10.1016/j.resp.2010.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
Baertsch NA, Baertsch HC, Ramirez JM. The interdependence of excitation and inhibition for the control of dynamic breathing rhythms. Nat Commun 9: 843, 2018. doi: 10.1038/s41467-018-03223-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
Baertsch NA, Severs LJ, Anderson TM, Ramirez J-M. A spatially dynamic network underlies the generation of inspiratory behaviors. Proc Natl Acad Sci USA 116: 7493–7502, 2019. doi: 10.1073/pnas.1900523116. [DOI] [PMC free article] [PubMed] [Google Scholar]
Bailey TW, Appleyard SM, Jin Y-H, Andresen MC. Organization and properties of GABAergic neurons in solitary tract nucleus (NTS). J Neurophysiol 99: 1712–1722, 2008. doi: 10.1152/jn.00038.2008. [DOI] [PubMed] [Google Scholar]
Barman SM 2019 Ludwig Lecture: Rhythms in sympathetic nerve activity are a key to understanding neural control of the cardiovascular system. Am J Physiol Regul Integr Comp Physiol 318: R191–R205, 2020. doi: 10.1152/ajpregu.00298.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57: 289–300, 1995. doi: 10.1111/j.2517-6161.1995.tb02031.x. [DOI] [Google Scholar]
Berman AL The Brain Stem of the Cat: A Cytoarchitectonic Atlas with Stereotaxic Coordinates. Madison, WI: University of Wisconsin Press, 1968. [Google Scholar]
Birznieks I, Boonstra TW, Macefield VG. Modulation of human muscle spindle discharge by arterial pulsations—functional effects and consequences. PLoS One 7: e35091, 2012. doi: 10.1371/journal.pone.0035091. [DOI] [PMC free article] [PubMed] [Google Scholar]
Bishop B Carotid baroreceptor modulation of diaphragm and abdominal muscle activity in the cat. J Appl Physiol 36: 12–19, 1974. doi: 10.1152/jappl.1974.36.1.12. [DOI] [PubMed] [Google Scholar]
Broersen PMT Finite sample criteria for autoregressive order selection. IEEE Trans Signal Process 48: 3550–3558, 2000. doi: 10.1109/78.887047. [DOI] [Google Scholar]
Censi F, Calcagnini G, Lino S, Seydnejad SR, Kitney RI, Cerutti S. Transient phase locking patterns among respiration, heart rate and blood pressure during cardiorespiratory synchronisation in humans. Med Biol Eng Comput 38: 416–426, 2000. doi: 10.1007/BF02345011. [DOI] [PubMed] [Google Scholar]
Chang EY, Morris KF, Shannon R, Lindsey BG. Repeated sequences of interspike intervals in baroresponsive respiratory related neuronal assemblies of the cat brain stem. J Neurophysiol 84: 1136–1148, 2000. doi: 10.1152/jn.2000.84.3.1136. [DOI] [PubMed] [Google Scholar]
Cherniack NS, Edelman NH, Fishman AP. Pattern of discharge of respiratory neurons during systemic vasomotor waves. Am J Physiol 217: 1375–1383, 1969. doi: 10.1152/ajplegacy.1969.217.5.1375. [DOI] [PubMed] [Google Scholar]
Cohen MI Discharge patterns of brain-stem respiratory neurons in relation to carbon dioxide tension. J Neurophysiol 31: 142–165, 1968. doi: 10.1152/jn.1968.31.2.142. [DOI] [PubMed] [Google Scholar]
Convertino VA Mechanisms of inspiration that modulate cardiovascular control: the other side of breathing. J Appl Physiol (1985) 127: 1187–1196, 2019. doi: 10.1152/japplphysiol.00050.2019. [DOI] [PubMed] [Google Scholar]
Davison AC, Hinkley DV. Bootstrap Methods and Their Application. Cambridge, UK: Cambridge University Press, 1997. [Google Scholar]
Dick TE, Morris KF. Quantitative analysis of cardiovascular modulation in respiratory neural activity. J Physiol 556: 959–970, 2004. doi: 10.1113/jphysiol.2003.060418. [DOI] [PMC free article] [PubMed] [Google Scholar]
Dick TE, Shannon R, Lindsey BG, Nuding SC, Segers LS, Baekey DM, Morris KF. Arterial pulse modulated activity is expressed in respiratory neural output. J Appl Physiol (1985) 99: 691–698, 2005. doi: 10.1152/japplphysiol.01124.2004. [DOI] [PubMed] [Google Scholar]
Dick TE, Shannon R, Lindsey BG, Nuding SC, Segers LS, Baekey DM, Morris KF. Pontine respiratory-modulated activity before and after vagotomy in decerebrate cats. J Physiol 586: 4265–4282, 2008. doi: 10.1113/jphysiol.2008.152108. [DOI] [PMC free article] [PubMed] [Google Scholar]
Dutschmann M, Dick TE. Pontine mechanisms of respiratory control. Compr Physiol 2: 2443–2469, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
Ford TW, Kirkwood PA. Cardiac modulation of alpha motoneuron discharges. J Neurophysiol 119: 1723–1730, 2018. doi: 10.1152/jn.00025.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
Funk GD, Parkis MA. High frequency oscillations in respiratory networks: functionally significant or phenomenological? Respir Physiol Neurobiol 131: 101–120, 2002. doi: 10.1016/S1569-9048(02)00041-1. [DOI] [PubMed] [Google Scholar]
Gabriel M, Seller H. Excitation of expiratory neurones adjacent to the nucleus ambiguus by carotid sinus baroreceptor and trigeminal afferents. Pflugers Arch 313: 1–10, 1969. doi: 10.1007/BF00586323. [DOI] [PubMed] [Google Scholar]
Gerstein GL Gravitational clustering In: Analysis of Parallel Spike Trains edited by Grün S, Rotter S. New York: Springer, 2010, p. 157–172. [Google Scholar]
Ghali MGZ, Marchenko V. Fast oscillations during gasping and other non-eupneic respiratory behaviors: clues to central pattern generation. Respir Physiol Neurobiol 187: 176–182, 2013. doi: 10.1016/j.resp.2013.03.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
Grün S, Rotter S. (Editors). Analysis of Parallel Spike Trains. New York: Springer, 2010. [Google Scholar]
Grunstein MM, Derenne JP, Milic-Emili J. Control of depth and frequency of breathing during baroreceptor stimulation in cats. J Appl Physiol 39: 395–404, 1975. doi: 10.1152/jappl.1975.39.3.395. [DOI] [PubMed] [Google Scholar]
Guyenet PG Regulation of breathing and autonomic outflows by chemoreceptors. Compr Physiol 4: 1511–1562, 2014. doi: 10.1002/cphy.c140004. [DOI] [PMC free article] [PubMed] [Google Scholar]
Guyenet PG, Stornetta RL, Bochorishvili G, Depuy SD, Burke PGR, Abbott SBG. C1 neurons: the body’s EMTs. Am J Physiol Regul Integr Comp Physiol 305: R187–R204, 2013. doi: 10.1152/ajpregu.00054.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
Heck DH, Kozma R, Kay LM. The rhythm of memory: how breathing shapes memory function. J Neurophysiol 122: 563–571, 2019. doi: 10.1152/jn.00200.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
Hennequin G, Agnes EJ, Vogels TP. Inhibitory plasticity: balance, control, and codependence. Annu Rev Neurosci 40: 557–579, 2017. doi: 10.1146/annurev-neuro-072116-031005. [DOI] [PubMed] [Google Scholar]
Herrero JL, Khuvis S, Yeagle E, Cerf M, Mehta AD. Breathing above the brain stem: volitional control and attentional modulation in humans. J Neurophysiol 119: 145–159, 2018. doi: 10.1152/jn.00551.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
Heymans C, Bouckaert JJ. Sinus caroticus and respiratory reflexes: I. Cerebral blood flow and respiration. Adrenaline apnoea. J Physiol 69: 254–266, 1930. doi: 10.1113/jphysiol.1930.sp002648. [DOI] [PMC free article] [PubMed] [Google Scholar]
Julien C The enigma of Mayer waves: facts and models. Cardiovasc Res 70: 12–21, 2006. doi: 10.1016/j.cardiores.2005.11.008. [DOI] [PubMed] [Google Scholar]
Kirkwood PA, Romaniuk JR, Kowalski KE. Further observations on cardiac modulation of thoracic motoneuron discharges. Neurosci Lett 694: 57–63, 2019. doi: 10.1016/j.neulet.2018.11.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
Kirsten EB, St. John WM. A feline decerebration technique with low mortality and long-term homeostasis. J Pharmacol Methods 1: 263–268, 1978. doi: 10.1016/0160-5402(78)90057-8. [DOI] [Google Scholar]
Kolpakova J, Li L, Hatcher JT, Gu H, Zhang X, Chen J, Cheng ZJ. Responses of Nucleus Tractus Solitarius (NTS) early and late neurons to blood pressure changes in anesthetized F344 rats. PLoS One 12: e0169529, 2017. doi: 10.1371/journal.pone.0169529. [DOI] [PMC free article] [PubMed] [Google Scholar]
Lescot T, Naccache L, Bonnet MP, Abdennour L, Coriat P, Puybasset L. The relationship of intracranial pressure Lundberg waves to electroencephalograph fluctuations in patients with severe head trauma. Acta Neurochir (Wien) 147: 125–129, 2005. doi: 10.1007/s00701-004-0355-8. [DOI] [PubMed] [Google Scholar]
Lindsey BG, Arata A, Morris KF, Hernandez YM, Shannon R. Medullary raphe neurones and baroreceptor modulation of the respiratory motor pattern in the cat. J Physiol 512: 863–882, 1998. [Erratum in J Physiol (Lond) 513: 935, 1998]. doi: 10.1111/j.1469-7793.1998.863bd.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
Lindsey BG, Hernandez YM, Shannon R. Cooperativity in Distributed Respiratory and Cardiovascular-Related Brainstem Neural Assemblies: Insights from Many-Neuron Recordings. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991, p. 131–137. [Google Scholar]
Lindsey BG, Morris KF, Shannon R, Gerstein GL. Repeated patterns of distributed synchrony in neuronal assemblies. J Neurophysiol 78: 1714–1719, 1997. doi: 10.1152/jn.1997.78.3.1714. [DOI] [PubMed] [Google Scholar]
Lindsey BG, Nuding SC, Segers LS, Morris KF. Carotid bodies and the integrated cardiorespiratory response to hypoxia. Physiology (Bethesda) 33: 281–297, 2018. doi: 10.1152/physiol.00014.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
Lindsey BG, Nuding SC, Segers LS, Morris KF. Brain stem circuit operations during baroreceptor modulation of the motor pattern for breathing (Abstract). FASEB J 33: 548.3, 2019. [Google Scholar]
Lindsey BG, Rybak IA, Smith JC. Computational models and emergent properties of respiratory neural networks. Compr Physiol 2: 1619–1670, 2012. doi: 10.1002/cphy.c110016. [DOI] [PMC free article] [PubMed] [Google Scholar]
Mautner-Huppert D, Haberl RL, Dirnagl U, Villringer A, Schmiedek P, Einhäupl K. B-waves in healthy persons. Neurol Res 11: 194–196, 1989. doi: 10.1080/01616412.1989.11739891. [DOI] [PubMed] [Google Scholar]
McMullan S, Dick TE, Farnham MMJ, Pilowsky PM. Effects of baroreceptor activation on respiratory variability in rat. Respir Physiol Neurobiol 166: 80–86, 2009. doi: 10.1016/j.resp.2009.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
McMullan S, Pilowsky PM. The effects of baroreceptor stimulation on central respiratory drive: a review. Respir Physiol Neurobiol 174: 37–42, 2010. doi: 10.1016/j.resp.2010.07.009. [DOI] [PubMed] [Google Scholar]
Melssen WJ, Epping WJ. Detection and estimation of neural connectivity based on crosscorrelation analysis. Biol Cybern 57: 403–414, 1987. doi: 10.1007/BF00354985. [DOI] [PubMed] [Google Scholar]
Menuet C, Connelly AA, Bassi JK, Melo MR, Le S, Kamar J, Kumar NN, McDougall SJ, McMullan S, Allen AM. PreBötzinger complex neurons drive respiratory modulation of blood pressure and heart rate. eLife 9: e57288, 2020. doi: 10.7554/eLife.57288. [DOI] [PMC free article] [PubMed] [Google Scholar]
Mifflin SW What does the brain know about blood pressure? News Physiol Sci 16: 266–271, 2001. doi: 10.1152/physiologyonline.2001.16.6.266. [DOI] [PubMed] [Google Scholar]
Miura K, Okada M, Amari S-I. Estimating spiking irregularities under changing environments. Neural Comput 18: 2359–2386, 2006. doi: 10.1162/neco.2006.18.10.2359. [DOI] [PubMed] [Google Scholar]
Montano N, Cogliati C, Dias da Silva VJ, Gnecchi-Ruscone T, Malliani A. Sympathetic rhythms and cardiovascular oscillations. Auton Neurosci 90: 29–34, 2001. doi: 10.1016/S1566-0702(01)00264-8. [DOI] [PubMed] [Google Scholar]
Monti A, Porciello G, Tieri G, Aglioti SM. The “embreathment” illusion highlights the role of breathing in corporeal awareness. J Neurophysiol 123: 420–427, 2020. doi: 10.1152/jn.00617.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
Moore GP, Segundo JP, Perkel DH, Levitan H. Statistical signs of synaptic interaction in neurons. Biophys J 10: 876–900, 1970. doi: 10.1016/S0006-3495(70)86341-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
Morris KF, Nuding SC, Segers LS, Baekey DM, Shannon R, Lindsey BG, Dick TE. Respiratory and Mayer wave-related discharge patterns of raphé and pontine neurons change with vagotomy. J Appl Physiol (1985) 109: 189–202, 2010. doi: 10.1152/japplphysiol.01324.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
Morris KF, Nuding SC, Segers LS, Iceman KE, O’Connor R, Dean JB, Ott MM, Alencar PA, Shuman D, Horton K-K, Taylor-Clark TE, Bolser DC, Lindsey BG. Carotid chemoreceptors tune breathing via multipath routing: reticular chain and loop operations supported by parallel spike train correlations. J Neurophysiol 119: 700–722, 2018. doi: 10.1152/jn.00630.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
Nishino T, Honda Y. Changes in pattern of breathing following baroreceptor stimulation in cats. Jpn J Physiol 32: 183–195, 1982. doi: 10.2170/jjphysiol.32.183. [DOI] [PubMed] [Google Scholar]
Nuding SC, Segers LS, Baekey DM, Dick TE, Solomon IC, Shannon R, Morris KF, Lindsey BG. Pontine-ventral respiratory column interactions through raphe circuits detected using multi-array spike train recordings. J Neurophysiol 101: 2943–2960, 2009a. doi: 10.1152/jn.91305.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
Nuding SC, Segers LS, Iceman KE, O’Connor R, Dean JB, Bolser DC, Baekey DM, Dick TE, Shannon R, Morris KF, Lindsey BG. Functional connectivity in raphé-pontomedullary circuits supports active suppression of breathing during hypocapnic apnea. J Neurophysiol 114: 2162–2186, 2015. doi: 10.1152/jn.00608.2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
Nuding SC, Segers LS, Shannon R, O’Connor R, Morris KF, Lindsey BG. Central and peripheral chemoreceptors evoke distinct responses in simultaneously recorded neurons of the raphé-pontomedullary respiratory network. Philos Trans R Soc Lond B Biol Sci 364: 2501–2516, 2009b. doi: 10.1098/rstb.2009.0075. [DOI] [PMC free article] [PubMed] [Google Scholar]
O’Connor RE, Barnhill PR, Nuding SC, Morris KF, Lindsey BG. Open source spike sorting software for large multi-electrode systems (Abstract). Society for Neuroscience Washington, DC, November 12–16, 2005. [Google Scholar]
O’Connor RE, Nuding SC, Segers LS, Ott MM, Lindsey BG, Morris KF. Ventral respiratory column neuron interactions and discharge patterns during expression of low-frequency or very low frequency brainstem network oscillations (Abstract). FASEB J 27: A1214.3, 2013. [Google Scholar]
Ott MM, Nuding SC, Segers LS, Lindsey BG, Morris KF. Ventrolateral medullary functional connectivity and the respiratory and central chemoreceptor-evoked modulation of retrotrapezoid-parafacial neurons. J Neurophysiol 105: 2960–2975, 2011. doi: 10.1152/jn.00262.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
Ott MM, Nuding SC, Segers LS, O’Connor R, Morris KF, Lindsey BG. Central chemoreceptor modulation of breathing via multipath tuning in medullary ventrolateral respiratory column circuits. J Neurophysiol 107: 603–617, 2012. doi: 10.1152/jn.00808.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
Park H-D, Barnoud C, Trang H, Kannape OA, Schaller K, Blanke O. Breathing is coupled with voluntary action and the cortical readiness potential. Nat Commun 11: 289, 2020. doi: 10.1038/s41467-019-13967-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
Pauluis Q, Baker SN. An accurate measure of the instantaneous discharge probability, with application to unitary joint-even analysis. Neural Comput 12: 647–669, 2000. doi: 10.1162/089976600300015736. [DOI] [PubMed] [Google Scholar]
Perkel DH, Gerstein GL, Moore GP. Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophys J 7: 419–440, 1967. doi: 10.1016/S0006-3495(67)86597-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
Poliaček I, Morris KF, Lindsey BG, Segers LS, Rose MJ, Corrie LW-C, Wang C, Pitts TE, Davenport PW, Bolser DC. Blood pressure changes alter tracheobronchial cough: computational model of the respiratory-cough network and in vivo experiments in anesthetized cats. J Appl Physiol (1985) 111: 861–873, 2011. doi: 10.1152/japplphysiol.00458.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
Preiss G, Iscoe S, Polosa C. Analysis of a periodic breathing pattern associated with Mayer waves. Am J Physiol 228: 768–774, 1975. doi: 10.1152/ajplegacy.1975.228.3.768. [DOI] [PubMed] [Google Scholar]
Richter DW, Seller H. Baroreceptor effects on medullary respiratory neurones of the cat. Brain Res 86: 168–171, 1975. doi: 10.1016/0006-8993(75)90651-4. [DOI] [PubMed] [Google Scholar]
Rogers RF, Rose WC, Schwaber JS. Simultaneous encoding of carotid sinus pressure and dP/dt by NTS target neurons of myelinated baroreceptors. J Neurophysiol 76: 2644–2660, 1996. doi: 10.1152/jn.1996.76.4.2644. [DOI] [PubMed] [Google Scholar]
Sebe JY, van Brederode JF, Berger AJ. Inhibitory synaptic transmission governs inspiratory motoneuron synchronization. J Neurophysiol 96: 391–403, 2006. doi: 10.1152/jn.00086.2006. [DOI] [PubMed] [Google Scholar]
Segers LS, Nuding SC, Dick TE, Shannon R, Baekey DM, Solomon IC, Morris KF, Lindsey BG. Functional connectivity in the pontomedullary respiratory network. J Neurophysiol 100: 1749–1769, 2008. doi: 10.1152/jn.90414.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
Segers LS, Nuding SC, Ott MM, Dean JB, Bolser DC, O’Connor R, Morris KF, Lindsey BG. Peripheral chemoreceptors tune inspiratory drive via tonic expiratory neuron hubs in the medullary ventral respiratory column network. J Neurophysiol 113: 352–368, 2015. doi: 10.1152/jn.00542.2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
Segers LS, Nuding SC, Vovk A, Pitts T, Baekey DM, O’Connor R, Morris KF, Lindsey BG, Shannon R, Bolser DC. Discharge identity of medullary inspiratory neurons is altered during repetitive fictive cough. Front Physiol 3: 223, 2012. doi: 10.3389/fphys.2012.00223. [DOI] [PMC free article] [PubMed] [Google Scholar]
Segers LS, Shannon R, Saporta S, Lindsey BG. Functional associations among simultaneously monitored lateral medullary respiratory neurons in the cat. I. Evidence for excitatory and inhibitory actions of inspiratory neurons. J Neurophysiol 57: 1078–1100, 1987. doi: 10.1152/jn.1987.57.4.1078. [DOI] [PubMed] [Google Scholar]
Shannon R, Baekey DM, Morris KF, Li Z, Lindsey BG. Functional connectivity among ventrolateral medullary respiratory neurones and responses during fictive cough in the cat. J Physiol 525: 207–224, 2000. doi: 10.1111/j.1469-7793.2000.00207.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
Stockwell RG, Mansinha L, Lowe RP. Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44: 998–1001, 1996. doi: 10.1109/78.492555. [DOI] [Google Scholar]
Taylor EW, Jordan D, Coote JH. Central control of the cardiovascular and respiratory systems and their interactions in vertebrates. Physiol Rev 79: 855–916, 1999. doi: 10.1152/physrev.1999.79.3.855. [DOI] [PubMed] [Google Scholar]
Tort ABL, Brankačk J, Draguhn A. Respiration-entrained brain rhythms are global but often overlooked. Trends Neurosci 41: 186–197, 2018. doi: 10.1016/j.tins.2018.01.007. [DOI] [PubMed] [Google Scholar]
Zuperku EJ, Stucke AG, Krolikowski JG, Tomlinson J, Hopp FA, Stuth EA. Inputs to medullary respiratory neurons from a pontine subregion that controls breathing frequency. Respir Physiol Neurobiol 265: 127–140, 2019. doi: 10.1016/j.resp.2018.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B1] Aertsen AM, Gerstein GL. Evaluation of neuronal connectivity: sensitivity of cross-correlation. Brain Res 340: 341–354, 1985. doi: 10.1016/0006-8993(85)90931-X. [DOI] [PubMed] [Google Scholar]

[B2] Amorim MR, Pena RFO, Souza GMPR, Bonagamba LGH, Roque AC, Machado BH. Firing properties of ventral medullary respiratory neurons in sino-aortic denervated rats. Exp Physiol 104: 39–49, 2019. [DOI] [PubMed] [Google Scholar]

[B3] Arata A, Hernandez YM, Lindsey BG, Morris KF, Shannon R. Transient configurations of baroresponsive respiratory-related brainstem neuronal assemblies in the cat. J Physiol 525: 509–530, 2000. doi: 10.1111/j.1469-7793.2000.t01-1-00509.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B4] Arshamian A, Iravani B, Majid A, Lundström JN. Respiration modulates olfactory memory consolidation in humans. J Neurosci 38: 10286–10294, 2018. doi: 10.1523/JNEUROSCI.3360-17.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B5] Ausborn J, Koizumi H, Barnett WH, John TT, Zhang R, Molkov YI, Smith JC, Rybak IA. Organization of the core respiratory network: Insights from optogenetic and modeling studies. PLoS Comput Biol 14: e1006148, 2018. doi: 10.1371/journal.pcbi.1006148. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B6] Baekey DM, Molkov YI, Paton JFR, Rybak IA, Dick TE. Effect of baroreceptor stimulation on the respiratory pattern: insights into respiratory-sympathetic interactions. Respir Physiol Neurobiol 174: 135–145, 2010. doi: 10.1016/j.resp.2010.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B7] Baertsch NA, Baertsch HC, Ramirez JM. The interdependence of excitation and inhibition for the control of dynamic breathing rhythms. Nat Commun 9: 843, 2018. doi: 10.1038/s41467-018-03223-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B8] Baertsch NA, Severs LJ, Anderson TM, Ramirez J-M. A spatially dynamic network underlies the generation of inspiratory behaviors. Proc Natl Acad Sci USA 116: 7493–7502, 2019. doi: 10.1073/pnas.1900523116. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B9] Bailey TW, Appleyard SM, Jin Y-H, Andresen MC. Organization and properties of GABAergic neurons in solitary tract nucleus (NTS). J Neurophysiol 99: 1712–1722, 2008. doi: 10.1152/jn.00038.2008. [DOI] [PubMed] [Google Scholar]

[B10] Barman SM 2019 Ludwig Lecture: Rhythms in sympathetic nerve activity are a key to understanding neural control of the cardiovascular system. Am J Physiol Regul Integr Comp Physiol 318: R191–R205, 2020. doi: 10.1152/ajpregu.00298.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B11] Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57: 289–300, 1995. doi: 10.1111/j.2517-6161.1995.tb02031.x. [DOI] [Google Scholar]

[B12] Berman AL The Brain Stem of the Cat: A Cytoarchitectonic Atlas with Stereotaxic Coordinates. Madison, WI: University of Wisconsin Press, 1968. [Google Scholar]

[B13] Birznieks I, Boonstra TW, Macefield VG. Modulation of human muscle spindle discharge by arterial pulsations—functional effects and consequences. PLoS One 7: e35091, 2012. doi: 10.1371/journal.pone.0035091. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B14] Bishop B Carotid baroreceptor modulation of diaphragm and abdominal muscle activity in the cat. J Appl Physiol 36: 12–19, 1974. doi: 10.1152/jappl.1974.36.1.12. [DOI] [PubMed] [Google Scholar]

[B15] Broersen PMT Finite sample criteria for autoregressive order selection. IEEE Trans Signal Process 48: 3550–3558, 2000. doi: 10.1109/78.887047. [DOI] [Google Scholar]

[B16] Censi F, Calcagnini G, Lino S, Seydnejad SR, Kitney RI, Cerutti S. Transient phase locking patterns among respiration, heart rate and blood pressure during cardiorespiratory synchronisation in humans. Med Biol Eng Comput 38: 416–426, 2000. doi: 10.1007/BF02345011. [DOI] [PubMed] [Google Scholar]

[B17] Chang EY, Morris KF, Shannon R, Lindsey BG. Repeated sequences of interspike intervals in baroresponsive respiratory related neuronal assemblies of the cat brain stem. J Neurophysiol 84: 1136–1148, 2000. doi: 10.1152/jn.2000.84.3.1136. [DOI] [PubMed] [Google Scholar]

[B18] Cherniack NS, Edelman NH, Fishman AP. Pattern of discharge of respiratory neurons during systemic vasomotor waves. Am J Physiol 217: 1375–1383, 1969. doi: 10.1152/ajplegacy.1969.217.5.1375. [DOI] [PubMed] [Google Scholar]

[B19] Cohen MI Discharge patterns of brain-stem respiratory neurons in relation to carbon dioxide tension. J Neurophysiol 31: 142–165, 1968. doi: 10.1152/jn.1968.31.2.142. [DOI] [PubMed] [Google Scholar]

[B20] Convertino VA Mechanisms of inspiration that modulate cardiovascular control: the other side of breathing. J Appl Physiol (1985) 127: 1187–1196, 2019. doi: 10.1152/japplphysiol.00050.2019. [DOI] [PubMed] [Google Scholar]

[B21] Davison AC, Hinkley DV. Bootstrap Methods and Their Application. Cambridge, UK: Cambridge University Press, 1997. [Google Scholar]

[B22] Dick TE, Morris KF. Quantitative analysis of cardiovascular modulation in respiratory neural activity. J Physiol 556: 959–970, 2004. doi: 10.1113/jphysiol.2003.060418. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B23] Dick TE, Shannon R, Lindsey BG, Nuding SC, Segers LS, Baekey DM, Morris KF. Arterial pulse modulated activity is expressed in respiratory neural output. J Appl Physiol (1985) 99: 691–698, 2005. doi: 10.1152/japplphysiol.01124.2004. [DOI] [PubMed] [Google Scholar]

[B24] Dick TE, Shannon R, Lindsey BG, Nuding SC, Segers LS, Baekey DM, Morris KF. Pontine respiratory-modulated activity before and after vagotomy in decerebrate cats. J Physiol 586: 4265–4282, 2008. doi: 10.1113/jphysiol.2008.152108. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B25] Dutschmann M, Dick TE. Pontine mechanisms of respiratory control. Compr Physiol 2: 2443–2469, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B26] Ford TW, Kirkwood PA. Cardiac modulation of alpha motoneuron discharges. J Neurophysiol 119: 1723–1730, 2018. doi: 10.1152/jn.00025.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B27] Funk GD, Parkis MA. High frequency oscillations in respiratory networks: functionally significant or phenomenological? Respir Physiol Neurobiol 131: 101–120, 2002. doi: 10.1016/S1569-9048(02)00041-1. [DOI] [PubMed] [Google Scholar]

[B28] Gabriel M, Seller H. Excitation of expiratory neurones adjacent to the nucleus ambiguus by carotid sinus baroreceptor and trigeminal afferents. Pflugers Arch 313: 1–10, 1969. doi: 10.1007/BF00586323. [DOI] [PubMed] [Google Scholar]

[B29] Gerstein GL Gravitational clustering In: Analysis of Parallel Spike Trains edited by Grün S, Rotter S. New York: Springer, 2010, p. 157–172. [Google Scholar]

[B30] Ghali MGZ, Marchenko V. Fast oscillations during gasping and other non-eupneic respiratory behaviors: clues to central pattern generation. Respir Physiol Neurobiol 187: 176–182, 2013. doi: 10.1016/j.resp.2013.03.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B31] Grün S, Rotter S. (Editors). Analysis of Parallel Spike Trains. New York: Springer, 2010. [Google Scholar]

[B32] Grunstein MM, Derenne JP, Milic-Emili J. Control of depth and frequency of breathing during baroreceptor stimulation in cats. J Appl Physiol 39: 395–404, 1975. doi: 10.1152/jappl.1975.39.3.395. [DOI] [PubMed] [Google Scholar]

[B33] Guyenet PG Regulation of breathing and autonomic outflows by chemoreceptors. Compr Physiol 4: 1511–1562, 2014. doi: 10.1002/cphy.c140004. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B34] Guyenet PG, Stornetta RL, Bochorishvili G, Depuy SD, Burke PGR, Abbott SBG. C1 neurons: the body’s EMTs. Am J Physiol Regul Integr Comp Physiol 305: R187–R204, 2013. doi: 10.1152/ajpregu.00054.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B35] Heck DH, Kozma R, Kay LM. The rhythm of memory: how breathing shapes memory function. J Neurophysiol 122: 563–571, 2019. doi: 10.1152/jn.00200.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B36] Hennequin G, Agnes EJ, Vogels TP. Inhibitory plasticity: balance, control, and codependence. Annu Rev Neurosci 40: 557–579, 2017. doi: 10.1146/annurev-neuro-072116-031005. [DOI] [PubMed] [Google Scholar]

[B37] Herrero JL, Khuvis S, Yeagle E, Cerf M, Mehta AD. Breathing above the brain stem: volitional control and attentional modulation in humans. J Neurophysiol 119: 145–159, 2018. doi: 10.1152/jn.00551.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B38] Heymans C, Bouckaert JJ. Sinus caroticus and respiratory reflexes: I. Cerebral blood flow and respiration. Adrenaline apnoea. J Physiol 69: 254–266, 1930. doi: 10.1113/jphysiol.1930.sp002648. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B39] Julien C The enigma of Mayer waves: facts and models. Cardiovasc Res 70: 12–21, 2006. doi: 10.1016/j.cardiores.2005.11.008. [DOI] [PubMed] [Google Scholar]

[B40] Kirkwood PA, Romaniuk JR, Kowalski KE. Further observations on cardiac modulation of thoracic motoneuron discharges. Neurosci Lett 694: 57–63, 2019. doi: 10.1016/j.neulet.2018.11.026. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B41] Kirsten EB, St. John WM. A feline decerebration technique with low mortality and long-term homeostasis. J Pharmacol Methods 1: 263–268, 1978. doi: 10.1016/0160-5402(78)90057-8. [DOI] [Google Scholar]

[B42] Kolpakova J, Li L, Hatcher JT, Gu H, Zhang X, Chen J, Cheng ZJ. Responses of Nucleus Tractus Solitarius (NTS) early and late neurons to blood pressure changes in anesthetized F344 rats. PLoS One 12: e0169529, 2017. doi: 10.1371/journal.pone.0169529. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B43] Lescot T, Naccache L, Bonnet MP, Abdennour L, Coriat P, Puybasset L. The relationship of intracranial pressure Lundberg waves to electroencephalograph fluctuations in patients with severe head trauma. Acta Neurochir (Wien) 147: 125–129, 2005. doi: 10.1007/s00701-004-0355-8. [DOI] [PubMed] [Google Scholar]

[B44] Lindsey BG, Arata A, Morris KF, Hernandez YM, Shannon R. Medullary raphe neurones and baroreceptor modulation of the respiratory motor pattern in the cat. J Physiol 512: 863–882, 1998. [Erratum in J Physiol (Lond) 513: 935, 1998]. doi: 10.1111/j.1469-7793.1998.863bd.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B45] Lindsey BG, Hernandez YM, Shannon R. Cooperativity in Distributed Respiratory and Cardiovascular-Related Brainstem Neural Assemblies: Insights from Many-Neuron Recordings. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991, p. 131–137. [Google Scholar]

[B46] Lindsey BG, Morris KF, Shannon R, Gerstein GL. Repeated patterns of distributed synchrony in neuronal assemblies. J Neurophysiol 78: 1714–1719, 1997. doi: 10.1152/jn.1997.78.3.1714. [DOI] [PubMed] [Google Scholar]

[B47] Lindsey BG, Nuding SC, Segers LS, Morris KF. Carotid bodies and the integrated cardiorespiratory response to hypoxia. Physiology (Bethesda) 33: 281–297, 2018. doi: 10.1152/physiol.00014.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B48] Lindsey BG, Nuding SC, Segers LS, Morris KF. Brain stem circuit operations during baroreceptor modulation of the motor pattern for breathing (Abstract). FASEB J 33: 548.3, 2019. [Google Scholar]

[B49] Lindsey BG, Rybak IA, Smith JC. Computational models and emergent properties of respiratory neural networks. Compr Physiol 2: 1619–1670, 2012. doi: 10.1002/cphy.c110016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B50] Mautner-Huppert D, Haberl RL, Dirnagl U, Villringer A, Schmiedek P, Einhäupl K. B-waves in healthy persons. Neurol Res 11: 194–196, 1989. doi: 10.1080/01616412.1989.11739891. [DOI] [PubMed] [Google Scholar]

[B51] McMullan S, Dick TE, Farnham MMJ, Pilowsky PM. Effects of baroreceptor activation on respiratory variability in rat. Respir Physiol Neurobiol 166: 80–86, 2009. doi: 10.1016/j.resp.2009.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B52] McMullan S, Pilowsky PM. The effects of baroreceptor stimulation on central respiratory drive: a review. Respir Physiol Neurobiol 174: 37–42, 2010. doi: 10.1016/j.resp.2010.07.009. [DOI] [PubMed] [Google Scholar]

[B53] Melssen WJ, Epping WJ. Detection and estimation of neural connectivity based on crosscorrelation analysis. Biol Cybern 57: 403–414, 1987. doi: 10.1007/BF00354985. [DOI] [PubMed] [Google Scholar]

[B54] Menuet C, Connelly AA, Bassi JK, Melo MR, Le S, Kamar J, Kumar NN, McDougall SJ, McMullan S, Allen AM. PreBötzinger complex neurons drive respiratory modulation of blood pressure and heart rate. eLife 9: e57288, 2020. doi: 10.7554/eLife.57288. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B55] Mifflin SW What does the brain know about blood pressure? News Physiol Sci 16: 266–271, 2001. doi: 10.1152/physiologyonline.2001.16.6.266. [DOI] [PubMed] [Google Scholar]

[B56] Miura K, Okada M, Amari S-I. Estimating spiking irregularities under changing environments. Neural Comput 18: 2359–2386, 2006. doi: 10.1162/neco.2006.18.10.2359. [DOI] [PubMed] [Google Scholar]

[B57] Montano N, Cogliati C, Dias da Silva VJ, Gnecchi-Ruscone T, Malliani A. Sympathetic rhythms and cardiovascular oscillations. Auton Neurosci 90: 29–34, 2001. doi: 10.1016/S1566-0702(01)00264-8. [DOI] [PubMed] [Google Scholar]

[B58] Monti A, Porciello G, Tieri G, Aglioti SM. The “embreathment” illusion highlights the role of breathing in corporeal awareness. J Neurophysiol 123: 420–427, 2020. doi: 10.1152/jn.00617.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B59] Moore GP, Segundo JP, Perkel DH, Levitan H. Statistical signs of synaptic interaction in neurons. Biophys J 10: 876–900, 1970. doi: 10.1016/S0006-3495(70)86341-X. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B60] Morris KF, Nuding SC, Segers LS, Baekey DM, Shannon R, Lindsey BG, Dick TE. Respiratory and Mayer wave-related discharge patterns of raphé and pontine neurons change with vagotomy. J Appl Physiol (1985) 109: 189–202, 2010. doi: 10.1152/japplphysiol.01324.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B61] Morris KF, Nuding SC, Segers LS, Iceman KE, O’Connor R, Dean JB, Ott MM, Alencar PA, Shuman D, Horton K-K, Taylor-Clark TE, Bolser DC, Lindsey BG. Carotid chemoreceptors tune breathing via multipath routing: reticular chain and loop operations supported by parallel spike train correlations. J Neurophysiol 119: 700–722, 2018. doi: 10.1152/jn.00630.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B62] Nishino T, Honda Y. Changes in pattern of breathing following baroreceptor stimulation in cats. Jpn J Physiol 32: 183–195, 1982. doi: 10.2170/jjphysiol.32.183. [DOI] [PubMed] [Google Scholar]

[B63] Nuding SC, Segers LS, Baekey DM, Dick TE, Solomon IC, Shannon R, Morris KF, Lindsey BG. Pontine-ventral respiratory column interactions through raphe circuits detected using multi-array spike train recordings. J Neurophysiol 101: 2943–2960, 2009a. doi: 10.1152/jn.91305.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B64] Nuding SC, Segers LS, Iceman KE, O’Connor R, Dean JB, Bolser DC, Baekey DM, Dick TE, Shannon R, Morris KF, Lindsey BG. Functional connectivity in raphé-pontomedullary circuits supports active suppression of breathing during hypocapnic apnea. J Neurophysiol 114: 2162–2186, 2015. doi: 10.1152/jn.00608.2015. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B65] Nuding SC, Segers LS, Shannon R, O’Connor R, Morris KF, Lindsey BG. Central and peripheral chemoreceptors evoke distinct responses in simultaneously recorded neurons of the raphé-pontomedullary respiratory network. Philos Trans R Soc Lond B Biol Sci 364: 2501–2516, 2009b. doi: 10.1098/rstb.2009.0075. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B66] O’Connor RE, Barnhill PR, Nuding SC, Morris KF, Lindsey BG. Open source spike sorting software for large multi-electrode systems (Abstract). Society for Neuroscience Washington, DC, November 12–16, 2005. [Google Scholar]

[B67] O’Connor RE, Nuding SC, Segers LS, Ott MM, Lindsey BG, Morris KF. Ventral respiratory column neuron interactions and discharge patterns during expression of low-frequency or very low frequency brainstem network oscillations (Abstract). FASEB J 27: A1214.3, 2013. [Google Scholar]

[B68] Ott MM, Nuding SC, Segers LS, Lindsey BG, Morris KF. Ventrolateral medullary functional connectivity and the respiratory and central chemoreceptor-evoked modulation of retrotrapezoid-parafacial neurons. J Neurophysiol 105: 2960–2975, 2011. doi: 10.1152/jn.00262.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B69] Ott MM, Nuding SC, Segers LS, O’Connor R, Morris KF, Lindsey BG. Central chemoreceptor modulation of breathing via multipath tuning in medullary ventrolateral respiratory column circuits. J Neurophysiol 107: 603–617, 2012. doi: 10.1152/jn.00808.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B70] Park H-D, Barnoud C, Trang H, Kannape OA, Schaller K, Blanke O. Breathing is coupled with voluntary action and the cortical readiness potential. Nat Commun 11: 289, 2020. doi: 10.1038/s41467-019-13967-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B71] Pauluis Q, Baker SN. An accurate measure of the instantaneous discharge probability, with application to unitary joint-even analysis. Neural Comput 12: 647–669, 2000. doi: 10.1162/089976600300015736. [DOI] [PubMed] [Google Scholar]

[B72] Perkel DH, Gerstein GL, Moore GP. Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophys J 7: 419–440, 1967. doi: 10.1016/S0006-3495(67)86597-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B73] Poliaček I, Morris KF, Lindsey BG, Segers LS, Rose MJ, Corrie LW-C, Wang C, Pitts TE, Davenport PW, Bolser DC. Blood pressure changes alter tracheobronchial cough: computational model of the respiratory-cough network and in vivo experiments in anesthetized cats. J Appl Physiol (1985) 111: 861–873, 2011. doi: 10.1152/japplphysiol.00458.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B74] Preiss G, Iscoe S, Polosa C. Analysis of a periodic breathing pattern associated with Mayer waves. Am J Physiol 228: 768–774, 1975. doi: 10.1152/ajplegacy.1975.228.3.768. [DOI] [PubMed] [Google Scholar]

[B75] Richter DW, Seller H. Baroreceptor effects on medullary respiratory neurones of the cat. Brain Res 86: 168–171, 1975. doi: 10.1016/0006-8993(75)90651-4. [DOI] [PubMed] [Google Scholar]

[B76] Rogers RF, Rose WC, Schwaber JS. Simultaneous encoding of carotid sinus pressure and dP/dt by NTS target neurons of myelinated baroreceptors. J Neurophysiol 76: 2644–2660, 1996. doi: 10.1152/jn.1996.76.4.2644. [DOI] [PubMed] [Google Scholar]

[B77] Sebe JY, van Brederode JF, Berger AJ. Inhibitory synaptic transmission governs inspiratory motoneuron synchronization. J Neurophysiol 96: 391–403, 2006. doi: 10.1152/jn.00086.2006. [DOI] [PubMed] [Google Scholar]

[B78] Segers LS, Nuding SC, Dick TE, Shannon R, Baekey DM, Solomon IC, Morris KF, Lindsey BG. Functional connectivity in the pontomedullary respiratory network. J Neurophysiol 100: 1749–1769, 2008. doi: 10.1152/jn.90414.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B79] Segers LS, Nuding SC, Ott MM, Dean JB, Bolser DC, O’Connor R, Morris KF, Lindsey BG. Peripheral chemoreceptors tune inspiratory drive via tonic expiratory neuron hubs in the medullary ventral respiratory column network. J Neurophysiol 113: 352–368, 2015. doi: 10.1152/jn.00542.2014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B80] Segers LS, Nuding SC, Vovk A, Pitts T, Baekey DM, O’Connor R, Morris KF, Lindsey BG, Shannon R, Bolser DC. Discharge identity of medullary inspiratory neurons is altered during repetitive fictive cough. Front Physiol 3: 223, 2012. doi: 10.3389/fphys.2012.00223. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B81] Segers LS, Shannon R, Saporta S, Lindsey BG. Functional associations among simultaneously monitored lateral medullary respiratory neurons in the cat. I. Evidence for excitatory and inhibitory actions of inspiratory neurons. J Neurophysiol 57: 1078–1100, 1987. doi: 10.1152/jn.1987.57.4.1078. [DOI] [PubMed] [Google Scholar]

[B82] Shannon R, Baekey DM, Morris KF, Li Z, Lindsey BG. Functional connectivity among ventrolateral medullary respiratory neurones and responses during fictive cough in the cat. J Physiol 525: 207–224, 2000. doi: 10.1111/j.1469-7793.2000.00207.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B83] Stockwell RG, Mansinha L, Lowe RP. Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44: 998–1001, 1996. doi: 10.1109/78.492555. [DOI] [Google Scholar]

[B84] Taylor EW, Jordan D, Coote JH. Central control of the cardiovascular and respiratory systems and their interactions in vertebrates. Physiol Rev 79: 855–916, 1999. doi: 10.1152/physrev.1999.79.3.855. [DOI] [PubMed] [Google Scholar]

[B85] Tort ABL, Brankačk J, Draguhn A. Respiration-entrained brain rhythms are global but often overlooked. Trends Neurosci 41: 186–197, 2018. doi: 10.1016/j.tins.2018.01.007. [DOI] [PubMed] [Google Scholar]

[B86] Zuperku EJ, Stucke AG, Krolikowski JG, Tomlinson J, Hopp FA, Stuth EA. Inputs to medullary respiratory neurons from a pontine subregion that controls breathing frequency. Respir Physiol Neurobiol 265: 127–140, 2019. doi: 10.1016/j.resp.2018.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Blood pressure drives multispectral tuning of inspiration via a linked-loop neural network

Lauren S Segers

Sarah C Nuding

Mackenzie M Ott

Russell O’Connor

Kendall F Morris

Bruce G Lindsey

Series information

Abstract

INTRODUCTION

METHODS

Fig. 1.

Table 1.

RESULTS

Fig. 2.

Table 2.

Table 3.

Table 4.

Intra- and interregional functional connectivity among baroresponsive neurons support circuit operations for tuning of inspiratory drive by blood pressure.

Fig. 3.

Table 5.

VRC inspiratory neuron chain has an embedded array of recurrent inhibitory loops.

Fig. 4.

Fig. 5.

Perturbation of high-frequency oscillations and “low-drive” augmented burst events.

Fig. 6.

Fig. 7.

Other baroresponsive circuit modules with pontine neurons linked to the raphe and VRC.

Fig. 8.

Fig. 9.

Disruption and resetting of slow oscillations in the inspiratory motor pattern.

Fig. 10.

Fig. 11.

Fig. 12.

DISCUSSION

Fig. 13.

Consideration of methods and approach.

Relationship to other prior work.

Summary.

GRANTS

DISCLOSURES

AUTHOR CONTRIBUTIONS

ACKNOWLEDGMENTS

REFERENCES

ACTIONS

PERMALINK

RESOURCES

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