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1Department of Molecular Pharmacology and Physiology and 2Neuroscience Program, School of Biomedical Sciences, University of South Florida College of Medicine, Tampa, Florida; 3Department of Medicine and 4Department of Neurosciences, Case Western Reserve University, Cleveland, Ohio; and 5Department of Physiology and Biophysics, State University of New York at Stony Brook, Stony Brook, New York
Submitted 28 March 2008; accepted in final form 11 July 2008
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ABSTRACT |
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INTRODUCTION |
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, many studies have demonstrated that the pons plays an important role in shaping the respiratory motor pattern (Lumsden 1923) and that removal of the rostral pons in vagotomized animals converts eupnea to apneusis (Stella 1938), which is characterized by a dramatically increased inspiratory duration. Connections between the pons and the medulla must be intact for the generation of a eupneic-like breathing pattern in the anesthetized or decerebrate, vagotomized cat (Berger et al. 1978; Bertrand and Hugelin 1971; Cohen 1958).
Pontine neurons with respiratory-modulated discharge patterns were first identified in the 1950s (Cohen and Wang 1959; Takagi and Nakayama 1958). Current models propose that a neuronal network in the ventrolateral medulla generates the basic respiratory rhythm and motor patterns for breathing and that this ventrolateral respiratory column (VRC) (Rybak et al. 2004; Smith et al. 2007) is profoundly influenced by neurons of the "pontine respiratory group" (PRG) in the parabrachial–Kölliker–Fuse–pons/mesencephalic region (Alheid et al. 2004; Bianchi et al. 1995; Cohen 1979; Dick et al. 1994; St. John 1985, 1986).
The PRG has been shown to modulate both respiratory rate and drive. Differential effects on inspiratory and expiratory phase durations are correlated with the anatomic site of experimental interventions within the PRG (Chamberlin and Saper 1998; Cohen 1979; Jodkowski et al. 1997; Okazaki et al. 2002; St. John and Zhou 1991). Lesions in the PRG affect respiratory drive by attenuating the response to hypoxia and hypercapnia (Fung and St. John 1994; Mizusawa et al. 1995; St. John 1979), blocking poststimulus plasticity evoked by chemoreceptor and other afferent stimulation (Coles and Dick 1996; Dick and Coles 2000; Siniaia et al. 2000), and decreasing breathing pattern stability (Oku and Dick 1992). Moreover, sectioning studies have suggested that the rostral pons is capable of generating a (possibly respiratory-related) rhythm when separated from the medulla and, together with other data, suggest that the PRG may also contribute to respiratory system redundancy as a backup rhythm generator (St. John 1983, 1985; St. John and Bledsoe 1985).
Recent models have proposed that the respiratory modulation of the firing rates of PRG neurons is a consequence of direct efferent projections from VRC neurons with similar respiratory discharge profiles (Rybak et al. 2004). In addition, recent data and reviews suggest that the dorsolateral pons may be a critical component of eupneic pattern generation (Dutschmann and Herbert 2006; Smith et al. 2007; St. John and Paton 2004). However, the functional connectivity among PRG and VRC neurons is not well understood. Although antidromic microstimulation (Bianchi and St. John 1981, 1982; Ezure and Tanaka 2006; Song et al. 1998) and anatomical tract tracing studies (Herbert et al. 1990; Kalia 1977; King 1980; Smith et al. 1989) have suggested reciprocal projections, a 1985 study from this laboratory remains the sole published effort to address the functional connectivity between individual neurons in PRG and the VRC (Segers et al. 1985). At that time, our data suggested that monosynaptic interactions between medullary and rostral pontine respiratory neurons play a limited role in the control of the respiratory cycle in the decerebrate vagotomized cat.
Although the locations, cytoarchitecture, and respiratory-modulated firing rates of PRG neurons have been described (Bertrand et al. 1974; Dick et al. 1994; Ezure and Tanaka 2006; Song et al. 2006), circuits within the PRG that may shape pontine respiratory neuron discharge patterns remain largely unexplored. Alterations in membrane potentials have been interpreted to suggest that connections among Kölliker–Fuse neurons are similar to those in the core medullary network (Dick et al. 1994), but the sources of such synaptic activity remain unknown. To our knowledge, there have been only two published studies of simultaneously recorded neurons in the region of the PRG in the cat. One of those earlier works was based on a total of 40 neuron pairs (Harper and Sieck 1980) and the other on a subset of the same data (Frostig et al. 1984); the respiratory modulation of the correlated neurons was not described in either study.
Neurons with no respiratory modulation (NRM) of their firing rates are found in the regions of the PRG and the VRC (Bianchi and St. John 1981, 1982; Dick et al. 1994). The extent to which these cells influence neighboring neurons with respiratory-modulated firing rates is not well understood, nor is it known whether NRM neurons in each of these two regions functionally influence or are influenced by respiratory-modulated cells in the other area. Although anatomic findings indicate projections between the ventrolateral medulla and the rostral pons (Herbert et al. 1990; Kalia 1977; King 1980; Smith et al. 1989), electrophysiological studies have found a relative paucity of respiratory-modulated activity in the pons (Cohen and Wang 1959; Dick et al. 1994; Segers et al. 1985). It is possible that NRM neurons have a key role in the respiratory control function attributed to the dorsolateral pons.
The present work was motivated by the gaps in knowledge enumerated earlier and addressed the following four model-based hypotheses: 1) the respiratory modulation of PRG neurons reflects, at least in part, paucisynaptic actions of multiple VRC populations; 2) functional connectivity among PRG neurons shapes and coordinates their respiratory-modulated activities; 3) the PRG acts on multiple VRC populations, contributing to respiratory phase-switching; and 4) NRM neurons located in close proximity to the VRC and PRG have widely distributed actions on respiratory-modulated cells. Our approach used multiple arrays of microelectrodes with fine individual depth adjustment and computational methods to screen large data sets of simultaneously recorded spike trains for short-timescale correlations indicative of paucisynaptic functional connectivity.
The results document correlation linkages among PRG and VRC neurons and suggest network mechanisms underlying the proposed modulatory functions of the PRG. A companion paper (Rybak et al. 2008) describes a computational model of the pontomedullary respiratory network developed in conjunction with the present work. Preliminary accounts of the results have been reported (Morris et al. 2006; Nuding et al. 2006, 2007).
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METHODS |
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Experiments were performed under protocols approved by the University of South Florida's Institutional Animal Care and Use Committee and were performed 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 obtained from 10 adult cats (2.8–5.6 kg) of either sex. Surgical procedures were similar to those described previously (Baekey et al. 2001; Shannon et al. 1998, 2000). Briefly, animals were initially anesthetized with isoflurane (2–5%; n = 4) or with an intramuscular (im) ketamine hydrochloride injection (5.5 mg kg–1; n = 6) followed by isoflurane and later decerebrated using a technique adapted from Kirsten and St. John (1978). The level of anesthesia was assessed periodically by noxious stimuli (toe pinch); if the withdrawal reflex occurred or there was an increase in blood pressure or respiration, the percentage of isoflurane in the inspired gas was increased until the response was absent. Animals were artificially ventilated through a tracheal cannula with a respirator. End-tidal CO2 was monitored continuously and maintained at 4–4.5%. Femoral arteries and veins were catheterized to monitor arterial blood pressure, administer fluids and drugs intravenously (iv), and acquire arterial blood samples for the periodic measurement of pO2, pCO2, and pH. These parameters were maintained within normal limits with solutions of 6% Dextran 70 in 0.9% sodium chloride, 0.04 to 0.1% dopamine, or 0.075–0.3 mg mL–1 phenylephrine in lactated Ringer solution; sodium bicarbonate solution (8%) was used to correct metabolic acidosis. Atropine (0.5 mg kg–1, im) and, in five experiments, diphenhydramine hydrochloride (1.8 mg kg–1, iv) were administered to reduce mucus secretion in the airways, and dexamethasone (initial bolus of 2.0 mg kg–1 followed by 4.5 mg·kg–1·h–1, iv) was administered to help prevent hypotension and minimize brain stem swelling. The trachea was periodically suctioned and the lungs were hyperinflated to counteract atelectasis. A urinary catheter was inserted to monitor urine flow as an indication of renal function. Rectal temperature was maintained at 38 ± 0.5°C.
In preparation for decerebration, the external carotid arteries were bilaterally ligated rostral to the lingual arteries. The animals were placed prone in a stereotaxic frame. A parietal craniotomy was performed and the cerebellum was partially removed by suction to expose the dorsal surface of the brain stem. At this point, an anesthetic assessment was performed, animals were neuromuscularly blocked by pancuronium bromide (initial bolus of 0.1 mg kg–1 followed by 0.2 mg·kg–1·h–1, iv), and the brain stem was immediately transected at the midcollicular level. Brain tissue rostral to the transection was aspirated. Isoflurane was removed from the inhaled gas circuit after the decerebration was complete and the flow rate was increased to aid elimination of residual isoflurane (Sasano et al. 2001; Vesely et al. 2003). During this period, sufficient CO2 was added to the gas mixture to maintain pCO2 at normocapnic levels of 
30 mmHg (Herbert and Mitchell 1971; Lovering et al. 2003) to prevent decreased brain stem microcirculation resulting from hypocapnia.
The T4 vertebra was exposed and clamped to suspend the abdomen and thorax and a bilateral thoracotomy was performed to minimize brain stem movement. Vagosympathetic nerve trunks were isolated and sectioned bilaterally to eliminate vagal afferent feedback from pulmonary stretch receptors and aortic baroreceptors. When necessary, the fraction of inspired O2 was increased to prevent the hypoxemia resulting from ventilation–perfusion mismatching caused by the open chest. At the end of the experiments, cats were killed with an injection of sodium pentobarbital (28 mg kg–1) followed by 5 mL of a saturated solution of KCl in water.
RECORDING NERVE ACTIVITY. The left C5 phrenic nerve rootlet was exposed, desheathed, cut, and its efferent activity recorded with bipolar silver electrodes covered with mineral oil. Nerve signals were amplified, filtered (band-pass 10 Hz to 10 kHz), and integrated (full-wave rectified signal to a resistor–capacitor integrator; 0.2-s time constant) to obtain a moving time average of activity in the respective nerves. Nerve discharges were observed using oscilloscopes and audio monitors and the integrated signal was recorded continually on a polygraph to monitor the respiratory motor pattern and to define the respiratory phases.
NEURAL RECORDINGS AND DATA ACQUISITION.
The brain stem surface was covered by warm mineral oil. Extracellular recordings of pontine and medullary neuronal activity were made using two arrays, each with 8 to 32 tungsten microelectrodes (10–12 M
). Electrode placement was guided by appropriate stereotaxic coordinates (see RESULTS) derived from Berman (1968) and numerous previous studies reviewed herein. The electrodes in the arrays were arranged either linearly or in a rectangular grid with adjacent tips 150–400 microns apart and individually advanced in submicron steps, allowing isolation of signals from single neurons. These signals were band-pass filtered (0.1–5 kHz) and, together with multifiber efferent nerve activities, systemic arterial blood pressure, tracheal pressure, and end-tidal CO2, stored on digital recorders (16-bit accuracy, 24- or 25-kHz sampling frequency per channel).
Data analysis
Most of the analytic methods used have been described in detail previously (Li et al. 1999a,b; Morris et al. 1996). Briefly, action potentials from single neurons were converted to arrays of occurrence times with spike-sorting software (Datawave Technologies; O'Connor et al. 2005). Time stamps indicating the onset of each inspiratory and expiratory phase were derived from the phrenic nerve signal. Coordinates of recording sites were mapped into the three-dimensional space of a computer-based brain stem atlas (Segers et al. 1987) using a common origin (obex), rotating the Berman coordinate axes to align with our coordinate frame of reference, and calculating a scaling factor to match both coordinate systems. The version of the program used in this study incorporated open source code of the IBM Open Visualization Data Explorer and included the coordinates of 19 frontal section traces from the cat brain stem together with outlined substructures derived from The Brain Stem of the Cat: A Cytoarchitectonic Atlas with Stereotaxic Coordinates (Berman 1968) with permission of the University of Wisconsin Press.
RESPIRATORY MODULATION OF FIRING RATES.
Both standard and normalized respiratory cycle-triggered histograms (CTHs) were computed for all recorded neurons. The normalized CTH was calculated using a spike train in which the durations of the inspiratory and expiratory phases were normalized to the average phase lengths; individual spike times within each phase were proportionally shifted to fit within the normalized phases. The normalization procedure reduced temporal dispersion of the average activity pattern caused by varying cycle lengths during the recordings. The CTHs were used to identify the phase (inspiration [I]; expiration [E]) or phase transition (IE and EI) in which the neuron was most active (Cohen 1968). Each neuronal spike train was additionally evaluated for respiratory modulation using two statistical tests (Morris et al. 1996). Neurons with no preferred phase of maximum activity as assessed by both statistical tests were designated "nonrespiratory-modulated" neurons (NRMs). A measure of respiratory modulation, 
2, was calculated for each neuron (Orem and Dick 1983).
CROSS-CORRELATION ANALYSIS.
Cross-correlation histograms (CCHs) were calculated for each pair of simultaneously recorded spike trains to detect and evaluate effective neuronal connectivity (Moore et al. 1970; Perkel et al. 1967b). The CCH gives an estimate of the probability that an action potential in one (reference) spike train will be preceded or followed by action potentials in a second (target) spike train. The significance of correlogram features (peaks and troughs) was evaluated by calculating a "detectability index" [DI, equal to the ratio of the maximum amplitude of departure from the background to the background, divided by the SD of the correlogram noise (Aertsen and Gerstein 1985; Melssen and Epping 1987)]. Features with DI values >3 were considered significant. Cumulative sum histograms with statistical confidence limits set at ±3 SDs (Davey et al. 1986) were calculated for all CCHs with significant DI values to confirm the significance of the correlogram feature (Ellaway 1978). Autocorrelograms were calculated for each spike train to verify that the activity of only a single neuron was represented and to aid interpretation of CCHs (Moore et al. 1970; Perkel et al. 1967a,b).
CORRELATION LINKAGE MAPS. The analysis results of each CCH, together with descriptions of each neuron's respiratory modulation, were placed in a standard relational database. Correlation linkage maps for groups of simultaneously monitored neurons were generated automatically by database queries using software based on the open source graph visualization tool Graphviz. The plotted positions of the represented neurons corresponded to their relative anterior–posterior recording site coordinates. The thickness of lines connecting the neuronal "nodes" of the graph provided an indication of correlation strength. Correlation features were represented by line color and type (e.g., Fig. 7A).
GRAVITY ANALYSIS.
The gravity method for the analysis and visualization of groups of simultaneously recorded neurons (Gerstein and Aertsen 1985; Gerstein et al. 1985) was also used to confirm assembly identification in selected data sets. This method represents each neuron as a particle in N-space with a time-varying charge that is a filtered version of the corresponding spike train. Movement of a particular particle in the N-space is calculated by evaluating a vector sum of pair forces with all other particles. Resulting trajectories of particles and their aggregation reflect neuronal timing relationships (e.g., Fig. 7C). A recently described "tuning" enhancement to the gravity method that improves detection of both short- and long-time lag correlations was incorporated into the analysis (Lindsey and Gerstein 2006). Charge kernels used for each pair of particles were offset by an amount determined by time lags to peaks or troughs in corresponding CCHs. The significance of particle aggregation was evaluated using confidence limits defined with an empirical Monte Carlo method (Lindsey et al. 1992a).
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RESULTS |
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In all, 427 single-neuron spike trains were recorded with electrode arrays in the region of the pontine respiratory group (PRG; 145 neurons) and ventral respiratory column (VRC; 282 neurons) in 10 decerebrate, vagotomized, and ventilated cats. Figure 1 A shows the coordinates of the recording sites of neurons included in this study mapped in a standard brain stem atlas (Berman 1968). Cells recorded at the same coordinates are shown as vertically displaced spheres, color-coded to indicate the presence (red) or absence (blue) of respiratory-modulated activity. Recording sites in the PRG ranged from 2.0 mm anterior to 2.0 mm posterior to the caudal border of the inferior colliculus, 2.5 to 5.8 mm lateral to the midline, and 1.2 to 4.5 mm below the dorsal surface of the pons. Cells recorded in the VRC were located 3.0 mm caudal to 8.6 mm rostral to the obex, 3.0 to 4.5 mm lateral to the midline, and 2.1 to 6.5 mm below the dorsal surface of the medulla. The spike trains of 
82 neurons were simultaneously monitored in a single animal. Figure 1B shows representative signals from eight electrodes (left) and corresponding sections of the spike trains of 10 neurons isolated from them (right). Firing rate histograms of those spike trains along with those of 36 other concurrently monitored neurons are shown in Fig. 1C.
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; Shannon et al. 2000). In addition, four VRC neurons were designated I–EI neurons. The onset of their increase in activity began slightly before phrenic nerve discharge; their firing rates peaked in the early I phase and then slowly decremented before abruptly decreasing at the I-to-E phase transition. The firing characteristics of these neurons were consistent with possible roles as "I-driver" neurons (Morris et al. 1996; Segers et al. 1987).
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2 values ranged from 0.01 to 0.45. Neurons with peak firing rates during the second half of the inspiratory or expiratory phase following a generally augmenting "ramp" of activity were designated I and E cells, respectively. Pontine neurons with peak firing rates either during the transition from one phase to the other or at the beginning of a phase followed by a decrementing firing rate were designated either IE or EI, depending on the phase transition temporally juxtaposed to the peak rate. Examples of respiratory-modulated discharge pattern in each category are shown in Fig. 2B. Cross-correlation analysis
Spike trains of 8,601 pairs of neurons were screened for short-timescale correlations. Overall, CCHs for 554 (6.4%) of the neuron pairs displayed a significant feature indicative of mono- or paucisynaptic connectivity (Table 1). Correlations were detected between the spike trains of 3.4% of the pairs composed of a PRG and a VRC neuron, 12.1% of PRG–PRG pairs, and 7.4% of VRC–VRC neuron pairs. Primary CCH features included an offset peak or trough (n = 282, 3.3%; 204 peaks, 78 troughs) or a central feature (n = 272, 3.2%; 232 peaks, 40 troughs).
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; Kirkwood 1979; Moore et al. 1970). A central peak is indicative of shared inputs with similar actions, whereas a central trough can be attributed to functional inputs with opposite actions on each of the neurons. A peak offset in time relative to the trigger event origin suggests excitation of the target neuron or an unobserved shared input that influences both cells with different conduction delays. Conversely, an offset trough suggests an inhibitory process, operationally defined as a mono- or paucisynaptic relationship that reduces target cell firing probability following trigger neuron spikes. Cross-correlation and related approaches thus define an abbreviated set of possible connections (i.e., the simplest neuronal model) that replicate the experimentally observed features (Aertsen et al. 1989).
Cross-correlation feature summary diagrams (e.g., Fig. 3 A) provide an overview of offset features for pairs of neurons with the indicated respiratory-modulated discharge profiles and recording sites. Reference neurons are represented on the left side of each diagram; target neuron categories are shown across the top. The lines indicate the correlation linkage for that particular reference and target neuron pair type. Each small circle on the target cell at the end of each line indicates an offset feature present in at least one CCH. Numbers enclosed in circles reference the corresponding CCH shown in the figure or elsewhere in RESULTS. For example, correlogram features for different VRC IE 
PRG IE neuron pairs (Fig. 3A) included an offset peak (e.g., Fig. 3B, CCH 8) and a trough (not shown).
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PRG functional connectivityShort-timescale correlations simply interpreted as evidence of excitatory and inhibitory functional connections from VRC to PRG neurons were detected in the spike trains from 48 of the 3,218 VRC–PRG neuron pairs analyzed (Fig. 3A). Primary offset correlogram features included 33 peaks and 15 troughs. Table 2 provides details on these features. In this and subsequent tables, VRC neurons are grouped into their major categories of respiratory modulation to facilitate discussion of correlations of similar and dissimilar respiratory types. Offset peaks were found in CCHs for 8 of 466 respiratory-modulated neuron pairs in which both neurons shared the same category of respiratory modulation; one additional pair of such neurons had an offset trough. Of the 1,075 neuron pairs composed of respiratory-modulated neurons with different firing profiles, 17 had offset features (8 peaks and 9 troughs).
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Correlations were detected for 15 of 1,381 pairs of neurons (1.1%) composed of a VRC or PRG NRM neuron and a respiratory-modulated neuron in the PRG or VRC, respectively. VRC NRM neurons were correlated with PRG I and IE cells (Fig. 3B, CCH 2); PRG NRM cells had primary offset positive-lag peaks following spikes in VRC I-EI (Fig. 3B, CCH 7), I-Dec, I-Aug, and E-Aug neurons. Of 296 cell pairs composed of VRC and PRG NRM neurons, 7 pairs (2.4%) were significantly correlated.
Evidence for PRG PRG functional connectivity
Spike trains from 1,043 pontine neuron pairs were analyzed for short-timescale correlations. A summary of the 54 (5.2%) CCHs with significant offset features is shown in Fig. 4 A. Primary offset features included 48 peaks and 6 troughs (Table 3). Offset peaks were detected for 4 of 100 pairs composed of neurons with similar respiratory profiles (Fig. 4B, CCHs 9 and 15). A total of 220 pairs of respiratory-modulated neurons with different activity patterns were evaluated; 10 pairs had offset peaks and 2 had offset troughs in their correlograms.
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The majority of PRG–PRG neuron pairs with offset correlation features contained at least one NRM neuron (38 of 54, 70.4%). PRG NRM neurons were involved in 22 correlated pairings with PRG respiratory-modulated cells (21 offset peaks and 1 offset trough; see Fig. 4B, CCHs 16 and 17). Cross-correlograms for pairs of PRG NRM neurons contained offset features in 16 cases (13 peaks and 3 troughs; 29.6% of offset features detected for PRG neuron pairs).
Evidence for PRG VRC functional connectivity
Offset features consistent with paucisynaptic actions of PRG neurons on the VRC were detected in 37 of 3,218 PRG–VRC neuron pairs (Fig. 5 A). These primary features included 22 peaks and 15 troughs (Table 4).
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Five peaks (Fig. 5, CCHs 19 and 24) and six troughs were detected in pairs composed of a VRC respiratory-modulated neuron and a PRG IE cell. VRC I-Dec neurons had decreased short-timescale firing probabilities following spikes in each type of PRG respiratory-modulated neuron (Fig. 5A; e.g., Fig. 5B, CCH 20). Neurons in other VRC respiratory categories exhibited increased and decreased firing rates following spikes in several types of PRG respiratory cells.
Twelve offset feature correlations were detected for PRG–VRC pairs composed of an NRM neuron and a respiratory-modulated cell. Peaks were detected in VRC NRM neuron firing probabilities in CCHs triggered by several types of PRG respiratory neurons (e.g., Fig. 5B, CCH 18). Correlograms for several categories of VRC respiratory neurons triggered by PRG NRM neuron spikes also exhibited offset peaks and troughs (e.g., Fig. 5B, CCHs 23 and 25). Three of 296 CCHs from pairs of VRC and PRG NRM neurons had offset features consistent with PRG-to-VRC neuron interactions.
Two PRG neurons—an IE and an NRM neuron—were correlated with the same VRC I-EI target neuron. Correlogram features for these two pairs suggested opposite actions on the VRC neuron. The CTHs for the PRG IE neuron (529) and the VRC target (103) are shown in Fig. 5C (top). The CCH for this pair (Fig. 5C, CCH 24) identified an increased firing probability in the VRC target cell following spikes in the PRG IE neuron. The second correlogram, calculated using the PRG NRM neuron as the reference cell (532; CTH not shown), had an offset trough (Fig. 5C, CCH 25).
Evidence for VRC VRC functional connectivity
The diagram in Fig. 6 A summarizes correlation features obtained from the analysis of 4,340 pairs of VRC neuron spike trains. Offset features were detected in 143 of these pairs (3.3%; 101 peaks and 42 troughs; Table 5).
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Primary offset features (13 peaks, 16 troughs) were found in CCHs from a total of 1,482 pairs of VRC neurons in which one cell was respiratory-modulated and the other was not (Fig. 6B, CCHs 26, 27, 28, and 32). VRC NRM neurons were correlated with cells of each broad respiratory category (Table 5). Cross-correlograms of 3 of the 368 pairs of VRC NRM neurons included 2 offset peaks and 1 trough.
Correlations between contralateral neurons
Most recordings were made from ipsilateral respiratory groups. In two cases, however, electrode arrays were placed in contralateral areas. Cross-correlograms for 3 of 48 contralateral VRC–VRC neuron pairs contained offset features. Offset peaks were detected in two pairs of VRC E-Dec neurons. The third VRC pair contained an offset trough, indicating that the E-Dec neuron had decreased firing probability following spikes in the contralateral I-Dec cell. One of 200 contralateral VRC–PRG pairs contained an offset peak; the VRC E-Dec cell had increased firing probability following spikes in the PRG IE neuron. Contralateral pairs of PRG neurons were not recorded.
Extended correlation linkages
Our use of arrays of microelectrodes with individual depth adjustment permitted detection of correlations among neurons recorded in parallel at multiple sites and provided evidence for distributed functional circuits. The correlation linkage map of a group of 16 simultaneously recorded PRG neurons shown in Fig. 7 A includes information on short-timescale correlations among the neurons. Line color, type, and thickness reflect the type (peak or trough), location (offset or central), and strength (DI) of the feature in the CCH constructed for the corresponding pair of neurons. The rectangles in the extended correlation linkage map contain each neuron's ID code and discharge pattern, which is also reflected by the color of the rectangle. For example, the central peak in the CCH for neurons 501 and 529 (Fig. 7B) can be simply interpreted as evidence of a shared input. The linkage map reported central peaks for several other cell pairs that include one of these two neurons: 501–521, 501–539, 521–529, 501–507, 507–529, and 529–539.
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The linkage map in Fig. 8 A gives a summary of 44 different correlogram features detected in the analysis of a group of 46 simultaneously recorded PRG and VRC neurons. In addition to many offset features, a central trough and several central peaks indicative of shared influences were also identified. The central trough in CCH 38 (Fig. 8B) is from a pair of PRG neurons most active at opposing phase transitions. This feature is indicative of an unobserved shared influence that altered the activities of the neurons oppositely. Shift control correlograms (not shown) suggest that the central trough was not solely attributable to the difference in the neuron's discharge patterns. The same PRG IE neuron tended to discharge in near synchrony with a VRC E-Dec neuron (Fig. 8B, CCH 39); both neurons were most active during or immediately following the I-to-E phase transition. Pontine IE neurons were correlated with multiple classes of VRC neurons in this data set.
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Another example of a central peak from the group represented in Fig. 8A was found in the CCH for a VRC I-Aug cell and a VRC NRM neuron (Fig. 8B, CCH 40). Overall, 49% (272 of 554) of the detected primary features were central peaks or troughs. Table 6 provides details on primary central features with regard to the respiratory modulation profile and location of each neuron in a pair. In each group—PRG–VRC, PRG–PRG, and VRC–VRC—the ratio of central peaks to troughs was similar.
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DISCUSSION |
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), various conventions have been used to label neurons according to their discharge patterns during the respiratory cycle (see the Fig. 9 legend for more details about grouping of neuron types in this diagram).
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). Previously suggested functional connections supported by the present results are labeled with both a yellow star and a green square or blue triangle. Similarly, axonal projections between the PRG and VRC previously identified by antidromic activation studies (Bianchi and St. John 1981, 1982; Ezure and Tanaka 2006) are noted with green arrows; those supported by inferences from the correlogram features in this study are additionally marked with stars. The starred connections in Fig. 9 represent simple interpretations of offset correlogram peaks and troughs. Inferred shared inputs consonant with central correlogram features detected in this study can also be seen in the diagram. For example, PRG IE neurons exciting other IE cells and inhibiting PRG EI neurons would be a source of shared inputs of opposite sign that generate a central trough (Fig. 8B, CCH 38).
Results support the motivating model-based hypotheses
The catalog of inferred functional connectivity generated in the present work informed the coordinated development of a new computational model of the pontomedullary respiratory network. A companion paper describes the connections incorporated into the new model, together with related hypotheses on network organization, and other coordinated experiments that tested model predictions (Rybak et al. 2008). Figure 10 highlights some pontine and medullary neuron interactions suggested by the present work that support the four motivating model-based hypotheses. We note here that detected offset features between different categories of neurons were, in general, relatively sparse, as indicated by the respective percentages of correlated pairs to total pairs for the inferred actions summarized in Table 2 through 5 and further detailed in Fig. 10. We consider possible explanations for the overall paucity of identified interactions later in the DISCUSSION.
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Figure 10A shows inferred VRC-to-PRG excitatory and inhibitory functional connections (blue and red arrows, respectively) appropriate for roles in the generation and shaping of the respiratory modulation of PRG neuron activity. The core VRC circuit in this and subsequent diagrams is abstracted from the computational model. Each category of PRG neuron—I, IE, E, and EI—receives excitatory input from VRC neurons with similar respiratory profiles. The PRG I and IE patterns are further shaped by excitation or inhibition from VRC neurons with dissimilar respiratory-modulated activities. The VRC EI to PRG IE inhibition is consistent with a previous intracellular recording study in which PRG postinspiratory neurons (classified here as IE neurons) were shown to be strongly inhibited during early I (Dick et al. 1994). VRC EI neurons in the present study were typically most active near the onset of the inspiratory phase. We note that evidence of inferred VRC-to-PRG projections was detected for 27 of the 203 (13.3%) VRC neurons with respiratory modulated discharge profiles, an incidence similar to the percentage (13%) of axonal projections to or through the pons from the VRC detected in the antidromic microstimulation studies of Bianchi and St. John (1981).
2)Functional connections among PRG neurons shape and coordinate their respiratory-modulated activities
Inferred interactions among PRG respiratory neurons are predominantly excitatory (Fig. 10B), with most respiratory types receiving inputs from other PRG neurons with similar as well as dissimilar discharge patterns. The inferred inhibitory actions of PRG IE on PRG EI neurons involve two categories of neurons most active at opposing phase transitions.
3)Neurons of the PRG exert modulatory actions on multiple VRC populations
The PRG is considered to be involved in the determination of respiratory phase durations. The diagrams in Fig. 10C summarize inferred excitatory and inhibitory actions of each category of respiratory neuron in the PRG region on various VRC respiratory cell types. The proposed connectivity suggests a basis for PRG facilitation of I-to-E phase switching. Inhibition of I cells and excitation of E neurons within the VRC network by PRG IE neurons increases E-Dec and E-Aug neuron activity directly and via disinhibition (due to inhibition of VRC I-Dec neurons), leading to further inhibition of the VRC I populations and the transition from I to E.
In turn, PRG EI neurons may promote the E-to-I transition by exciting VRC I-Aug neurons and reducing VRC E-Dec neuron inhibition of the I-Driver population, leading to the inspiratory phase. Evidence for inferred PRG-to-VRC projections was detected in 27.3% (18 of 66) of recorded PRG neurons with respiratory-modulated firing patterns.
4)Nonrespiratory-modulated neurons located within and near the PRG and VRC exert widely distributed actions on neurons with respiratory-related discharge properties
Neurons with no respiratory modulation of their firing rates are intermingled with respiratory-modulated cells, both within and near the VRC and the PRG (Fig. 1). Our results suggest that these medullary pericolumnar and pontine NRM neurons provide a tonic bias and modulatory influence to multiple classes of respiratory neurons located within their respective regions or in the opposite region (Fig. 10D). On the basis of the present results and preliminary data (Nuding et al. 2007), we suggest that these neurons have modulatory functions, including involvement in the transmission and transformation of central and peripheral chemosensory information.
It is noteworthy that some NRM neurons have presumptive excitatory and inhibitory inputs from other cells with diverse patterns of respiratory-modulated firing rates (Fig. 9). There are potential network mechanisms that could account for this apparently paradoxical observation, including the balanced or "blended" effects of multiple convergent respiratory-modulated inputs of like or opposite sign (Rybak et al. 2008). These absorbed respiratory influences may subsequently affect other cells in the network. An alternate hypothesis is that NRM cells are members of a single population composed of neurons with a variety of respiratory discharge patterns. Elsewhere (Morris et al. 2007), we have described a network model with recurrent inhibition suggested by previous correlational data (Lindsey et al. 1992a,b,c). The model includes one target population with two inputs: a direct inspiratory-modulated input and a recurrent inhibitory input driven by the same inspiratory population. The two sets of inputs are "tuned" to generate tonic firing in the target population as a whole. Because of the randomness of distributed synapses, some individual neurons in the target population are not respiratory modulated, whereas others are classified as I or E using conventional criteria. Such a model also offers a parsimonious circuit mechanism for a previous observation of respiratory phase-dependent firing synchrony among neurons with no respiratory modulation of their individual firing rates (Lindsey et al. 1992b).
The 2 value calculated for each neuron in this study is an indication of the strength and consistency of its respiratory discharge pattern (Orem and Dick 1983). Cells with high 2 values are robustly respiratory modulated; neurons with low 2 values are, to varying degrees, more weakly modulated, presumably reflecting, at least in some cases, a mixture of inputs with both respiratory and nonrespiratory forms. Orem and colleagues have proposed that high 2-valued cells represent the "automatic" system for breathing and low 2-valued cells are the interface between nonrespiratory inputs, such as those for behavioral control, and the automatic system (Orem and Trotter 1994). The present results support a respiratory network model with both high and low 2-valued cells. We found evidence for functional connections involving high and low 2-valued cells within each broad class of respiratory neuron: inspiratory, expiratory, and phase-spanning.
Sparseness of detected connectivity and relationship to other studies
Previous studies have established that cross-correlation analysis is sufficiently sensitive to detect evidence for functional interactions among brain stem respiratory neurons (Bianchi et al. 1995; Feldman and Speck 1983; Lindsey et al. 2000; Segers et al. 1985, 1987). Advantages and limitations of cross-correlation and gravity methods have been discussed elsewhere (Lindsey et al. 1992c, 1994). As considered in previous studies on functional connectivity between brain stem domains (Lindsey et al. 1994), the results may reflect a paucity of functional links between neurons in the sampled domains, or the regions may be tightly coupled through many distributed connections, each with limited divergence and convergence. Both arrangements would result in a low probability that sampled neurons would be directly linked. Connection properties may also preclude generation of transient changes in neuronal firing probability time-locked to spikes in a trigger neuron that are detectable with cross-correlation. The size and time course of underlying conductance and potential changes and whether the connections are mono- or polysynaptic would influence detectability. Moreover, excitation may be more difficult to detect when target neurons have high firing rates or when the rate of impulses is limited by refractoriness. Conversely, inhibition is more difficult to detect than excitation when target neurons have low rates (Aertsen and Gerstein 1985).
We used both cross-correlation and gravity analysis methods to screen our data for indications of functional connectivity. Signs of interactions between neurons with transient periods of correlated activity may be averaged out in cross-correlation analysis. This limitation and the requirement of the cross-correlation method for stationary data do not apply to the gravity method for neuronal assembly analysis. We have previously unmasked transient relationships not identified by cross-correlation techniques when the gravity method was applied to the same data (Lindsey et al. 1992b). Interactions identified with the gravity method in this study were confirmed with cross-correlation analysis.
A 1985 study from this lab is the lone previously published effort to address functional connectivity between individual PRG and VRC neurons (Segers et al. 1985). In that study, 11 of 255 (4.3%) respiratory-modulated PRG–VRC neuron pairs were correlated with a short-latency offset or central feature. Three offset correlations were suggestive of a paucisynaptic projection from the PRG to the VRC (1.2%; all peaks). Offset features consistent with VRC-to-PRG functional connections were detected in five pairs (2.0%; four peaks, one trough). Correlograms for the remaining three correlated pairs contained a central peak. Neurons with no respiratory modulation were not considered in that study. When PRG–VRC pairs that include an NRM neuron(s) are excluded from the present study, the results are similar to our earlier work. Of 1,541 respiratory-modulated PRG–VRC neuron pairs, 61 (4.0%) were significantly correlated with offset or central peaks or troughs. Evidence was detected for 22 pontomedullary and 26 medullopontine functional projections (1.4 and 1.7%, respectively).
The small percentages of correlations between PRG and VRG neurons in both studies are consistent with a sparse distribution of functional connections, as were the results of earlier studies using antidromic stimulation methods (Bianchi and St. John 1981, 1982). Given the strong influence of the pons on the medullary respiratory network, it is reasonable to suggest, as noted earlier, that widely distributed or "weaker" effective connections not detectable with our cross-correlation methods are also present. Another possibility is that interactions between neurons of the PRG and VRC also include less direct pathways. Supporting the latter hypothesis, we have recently identified multiple short-timescale correlations among simultaneously recorded PRG, brain stem midline, and VRC neurons consistent with serial and recurrent paucisynaptic PRG–VRC pathways that include intervening midline neurons (Nuding et al. 2006).
A preliminary report of a cross-correlation study of PRG neurons in the rat indicated a correlation in about 6% of the neuron pairs analyzed (Yu et al. 2006). All of the correlogram features reported were offset peaks. The respiratory modulation of the cells was not described. Offset peaks were detected in 48 of 1,043 (4.6%) pontine pairs in the present study and the incidence of intra-PRG correlation was greater (12%). Furthermore, correlogram features included central peaks and central and offset troughs in addition to offset peaks.
The current study largely focused on ipsilateral connectivity because labeling (Bystrzycka 1980; Denavit-Saubie and Riche 1977) and stimulation studies (Bianchi and St. John 1981, 1982; Ezure and Tanaka 2006) in the cat and the rat indicate that projections between the PRG and VRC are predominantly ipsilateral. Moreover, although the pontine and medullary respiratory groups are distributed bilaterally, each side of the cat brain stem is capable of producing a respiratory rhythm (Eldridge and Paydarfar 1989).
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GRANTS |
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ACKNOWLEDGMENTS |
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Present address of D. M. Baekey: Case Western Reserve University, Department of Medicine, Cleveland, OH 44106.
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FOOTNOTES |
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Address for reprint requests and other correspondence: B. G. Lindsey, Department of Molecular Pharmacology and Physiology, School of Biomedical Sciences, University of South Florida College of Medicine, 12901 Bruce B. Downs Blvd., Tampa, FL 33612-4799 (E-mail: blindsey{at}health.usf.edu)
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INTRODUCTION
