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The Journal of Physiology logoLink to The Journal of Physiology
. 2006 Apr 13;573(Pt 3):679–695. doi: 10.1113/jphysiol.2006.108514

Electrophysiological diversity of the dorsal raphe cells across the sleep–wake cycle of the rat

Nadia Urbain 1, Katherine Creamer 1, Guy Debonnel 1
PMCID: PMC1779756  PMID: 16613874

Abstract

Through their widespread projections to the entire brain, dorsal raphe cells participate in many physiological functions and are associated with neuropsychiatric disorders. In previous studies, the width of action potentials was used as a criterion to identify putative serotonergic neurons, and to demonstrate that cells with broad spikes were more active in wakefulness, slowed down their activity in slow wave sleep and became virtually silent during paradoxical sleep. However, recent studies reported that about half of these presumed serotonergic cells were not immunoreactive for tyrosine hydroxylase. Here, we re-examine the electrophysiological properties of dorsal raphe cells across the sleep–wake cycle in rats by the extracellular recording of a large sample of single units (n = 770). We identified two major types of cells, which differ in spike waveform: a first population characterized by broad, mostly positive spikes, and a second one displaying symmetrical positive–negative spikes with a large distribution of spike durations (0.6–3.2 ms). Although we found classical broad-spike cells that were more active in wakefulness, we also found that about one-third of these cells increased or did not change their firing rate during sleep compared with wakefulness. Moreover, 62% of the latter cells were active in paradoxical sleep when most of raphe cells were silent. Such a diversity in the neuronal firing behaviour is important in the light of the recent controversy regarding the neurochemical identity of dorsal raphe cells exhibiting broad spikes. Our results also suggest that the dorsal raphe contains subpopulations of neurons with reciprocal activity across the sleep–wake cycle.

Dorsal raphe nucleus (DRN) neurons project to widespread regions of the forebrain (Azmitia & Segal, 1978; Vertes, 1991), thus influencing many different brain structures. Dorsal raphe serotonergic (5-HT) neurons have indeed been shown to be involved in a broad range of physiological functions and behaviours, including emotion and fear processing, cognition, movement and regulation of the sleep–wake cycle (Jacobs & Azmitia, 1992; Lucki, 1998). Moreover, the role of 5-HT in psychiatric diseases has been widely documented (Artigas et al. 1996; Mann, 1999; Nutt, 2002). Since the DRN, containing about 40% of the 5-HT neurons, is the major 5-HT nucleus of the brainstem (Wiklund et al. 1981; Steinbusch et al. 1981), it is a likely site of 5-HT dysfunction. However, although the DRN is often considered in light of its 5-HT neurons, recent studies underscore the complexity of this nucleus and its heterogeneous neurochemical identity.

Prior electrophysiological studies in anaesthetized animals or slices have provided evidence for two types of neurons in the DRN: cells with long duration spikes (1–2 ms) that display slow regular activity, and cells with shorter-duration spikes and relatively high discharge rates (> 2spikes/s; Aghajanian et al. 1978; Vandermaelen & Aghajanian, 1983). The former cells were considered serotonergic, whereas the latters were presumed GABAergic. The spike width was consequently considered a reliable criterion to identify serotonergic cells. Subsequently, several electrophysiological recordings in unanaesthetized animals agreed that DRN 5-HT neurons fire tonically during wakefulness (W), decrease their activity in slow wave sleep (SWS), and are nearly quiescent during paradoxical sleep (PS; McGinty & Harper, 1976; Trulson & Jacobs, 1979; Levine & Jacobs, 1992; Gervasoni et al. 2000). These criteria were, however, determined on small samples which revealed the dominant activity of presumed serotonergic cells but did not allow a fair assessment of smaller subsets with atypical discharges.

Some studies in cats have reported the presence of presumed 5-HT neurons in the DRN that displayed sustained tonic activity during PS and/or high discharge rates during SWS (Rasmussen et al. 1984; Sakai & Crochet, 2001). Thus, on the basis of electroencephalographic and behavioural correlates in cats (Sakai & Crochet, 2001), a classification of DRN neurons appears particularly complex and points towards a great diversity of DRN cells. Furthermore, recent studies in vitro and in vivo in anaesthetized rats have shown that about half the neurons presumed serotonergic, based on action potential duration, are not tryptophan hydroxylase immunoreactive (Allers & Sharp, 2003; Kirby et al. 2003).

The objective of the present study was to assess the discharge characteristics of DRN neurons in non-anaesthetized head-restrained rats. This semichronic preparation, devoid of painful or stressful conditions, allows electrophysiological experiments across the sleep–wake cycle under physiologically relevant conditions (Darracq et al. 1996; Gervasoni et al. 1998, 2000; Soulière et al. 2000; Urbain et al. 2000, 2002, 2004b). Using extracellular recordings of a large sample of neurons, we demonstrate significant diversity in the firing behaviour of DRN neurons with broad spikes across the sleep–wake cycle in rats. Part of these results has previously been published in abstract form (Urbain et al. 2004a).

Methods

Fixation of the head-restraining system

Male Sprague–Dawley rats (280–320 g, Taconic, NY, USA) were anaesthetized with equithesin (425 mg chloral hydrate, 1 ml 100% ethanol, 98 mg pentobarbitone, 213 mg magnesium sulphate and 3 ml propylene glycol, sterile water per 10 ml of solution; 1 ml per 300 g body weight i.p., supplemented with 0.4 ml h−1 i.p. via a perfusion pump) and positioned conventionally (i.e. with ear- and nose-bars) in a stereotaxic apparatus (David Kopf Instrument, Tujunga, CA, USA). Body temperature was monitored and maintained at 37–38°C with an electric heating pad. The skull was exposed, carefully cleaned, and placed at a 15 deg angle (nose tilted down) to spare the transverse sinus overlying the DRN during the subsequent electrode penetrations. Three stainless-steel screws were implanted over the parietal areas of the skull, and three steel flexible wires inserted into the neck muscles for the monitoring of the electroencephalogram (EEG) and electromyogram (EMG), respectively. The bone was then covered with a thin layer of acrylic cement (C&B Metabond, Patterson Dental, Montreal, Qubec, Canada), except the region overlying the DRN and the lambda suture (stereotaxic reference point). A U-shaped aluminium piece, fixed to a flexible carriage (GFG Co., Pierre-Bénite, France) fastened to the stereotaxic apparatus, was positioned above the DRN and lambda suture. This U-piece was then embedded in dental cement with the EEG screws and EMG wires and their six-pin connector, as already described (Darracq et al. 1996; Gervasoni et al. 1998, 2000; Soulière et al. 2000; Urbain et al. 2000, 2002, 2004b), leaving a well inside the U-piece.

To prevent any infection, a gelfoam soaked with an antibiotic solution (neomycin trisulphate hydrate, Fluka) was laid on the skull inside the well, which was then closed with bone wax. This wound dressing was replaced every day. An antiseptic powder (Sulfanilamide, p-aminobenzenesulphonamide, 99%, Sigma) was poured out on all the borders of the implant to allow an absolute healing. The rat was then removed from the stereotaxic apparatus and received 1 ml per 100 g body weight of 5% glucose. As soon as the rat was awake, 0.1 ml of ibuprofen paediatric drops was also administered orally to relieve pain due to the surgery. The animal was allowed to recover from surgery and anaesthesia for 48 h before the habituation sessions began. The U-shaped piece (about 5 g weight) was well tolerated by the rats, which were able to move, sleep, feed and drink normally in their home cage. All experiments were performed with the approval of the McGill Animal Care Committee and complied with rules set forth in the National Institutes of Health ‘Guide for the Care and Use of Laboratory Animals’ (Publication 80-23). All animals were housed in standard conditions (21 ± 1°C, food and water ad libitum) and all experiments were performed during the light part of the cycle (12 h light–12 h dark).

Training and habituation of rats to the head-restraining frame

During eight to ten successive days, repetitive trials of increasing duration were performed to well habituate the rats to the restraining frame. Their head was painlessly secured to the stereotaxic frame by screwing the U-shaped piece, cemented to the rat's head, with its associated carriage; their body lying comfortably in a hammock. At the end of the training period, they could stay calm for periods of 5–6 h, during which quiet W, SWS, as well as PS episodes were typically observed, attesting that the restraint was well tolerated, as previously described (Soulière et al. 2000; Urbain et al. 2000).

Single-unit and polygraphic recordings

After the 8–10 days of habituation and before the first single-unit recording session, rats were anaesthetized with equithesin (0.8 ml per 300 g body weight i.p.; additional doses as needed), and a 3 mm trephine hole was drilled over DRN. The dura matter was then removed under microscopic control and the well was closed with bone wax. After one day of recovery, daily recording sessions were typically performed over a maximum of 7–10 days, each session lasting about 4–5 h. The brain surface was cleaned under local lignocaine anaesthesia at the beginning of each daily recording session. In order to prevent eventual food or water deprivation during restraint, animals were fed regularly outside recording periods. At the end of each recording session, rats were rewarded with food on returning to their home cage.

Extracellular recordings of DRN neurons were performed using single-barrel glass micropipettes (external tip diameter of 2–3 μm; electrodes tilted posteriorly with an angle of 15 deg), filled with 2% Pontamine Sky Blue dye in sodium acetate (0.5 m, pH 7.5). Electrode impedances measured at 10 Hz ranged between 7 and 15 MΩ. Filtered (AC, 0.3–10 kHz) electrode signal was amplified (Bak Electronics, Inc. Model RP-I) and fed to an oscilloscope (BK Precision 20 MHz, 1522) and an audio monitor. Spike shapes were collected on a personal computer via a 1401 Plus Cambridge Electronic Design (CED, Cambridge, UK) interface using the Spike 2 software, in parallel with analog-to-digital samplings of amplified (P55; Grass Instruments) polygraphic signals (EEG and EMG; sampling rate, 100 or 200 Hz). Single-unit activity (signal-to-noise ratio of at least 3:1) was isolated with an amplitude spike discriminator; an action potential analog waveform of 5 ms duration was digitalized (sampling rate of 16.7 kHz) and stored each time that the input signal crossed the trigger level. Dorsal raphe nucleus neurons were identified on-line by their stereotaxic location relative to lambda (Paxinos & Watson, 1996), i.e. anteroposterior, −3.8 to −4.2 mm; lateral, 0 ± 0.2 mm; and ventral, 5.4–7.0 mm. The recordings were performed only in the medial part of the DRN, i.e. the nucleus part presumed richest in 5-HT neurons compared to its wings (Descarries et al. 1982).

Histological verification of recording sites

On the last day of the experiment, the electrode was left in place at the final recording site, and a classical deposit of Pontamine Sky Blue was performed (−20 μA for 7 min). Then the animal was given a lethal dose of pentobarbial, and its brain was removed and immediately frozen in cold isopentane (−40°C). Subsequent histological location of the labelled site was made on 25 μm-thick Cresyl Violet-stained frontal sections.

Data analysis

The three classical vigilance states described in the rat were discriminated on the basis of the cortical EEG and neck EMG. Wakefulness was identified by a low-amplitude and desynchronized EEG with sustained EMG activity. Slow wave sleep was clearly distinguished by high-voltage delta waves (0.5–5 Hz) and spindles associated with weak EMG activity, the animal being immobile and its eyes closed. Paradoxical sleep was characterized by a low-amplitude EEG with a pronounced theta rhythm (5.5–8.5 Hz) and a complete loss of nuchal muscle tone. In order to avoid transitional periods, such as drowsiness, only periods of typical stationary EEG and EMG, lasting more than 5 s for W and more than 10 s for SWS and PS, were considered for further analyses. Power spectra of the corresponding EEGs were calculated using the Fast Fourier Transform (FFT) of the Spike 2 software. The duration of spike waveforms (averaged over a 50 s period) was determined as the time between the positive deviation from baseline to the return to baseline following the negative phase. Discharge rate and pattern of DRN neurons were analysed off-line for each vigilance state by the Spike 2 analysis and NeuroExplorer softwares.

Mean discharge rates were calculated during W, SWS and PS. For cells recorded for sufficient time, both in W and SWS, to allow analyses, the percentage of variation of the mean firing rate between W and SWS was assessed from the ‘rate ratio’ defined as follows: if the algebraic difference between W and SWS mean firing rate was positive, then the ‘rate ratio’ was (W − SWS)/W mean firing rates, if not, then the ‘rate ratio’ was (SWS − W)/SWS mean firing rates. The discharge patterns of DRN neurons were appreciated by interspike intervals (ISIs) histograms and autocorrelograms. Very slow firing neurons (discharge rate lower than 0.1 spike s−1) or neurons that could not be recorded long enough in a given vigilance state (less than 10 spikes) were not analysed for the discharge pattern. Interspike intervals were collected from each successive period of W, SWS or PS throughout the total recording time of the neuron, and coefficients of variation (c.v.) for the ISIs were measured for each vigilance state, as the ratio of the standard deviation to the mean interspike intervals (i.e. the lower is this value, the more regular is the spike train). For a given vigilance state, the comparisons of firing rates (and c.v.) of the different neuronal populations were performed using ANOVA with neuronal characteristics (see below) as a factor, followed by a Tukey–Kramer post hoc t test (Statview). Within each neuronal population, comparisons of firing rates or c.v. computed in each vigilance state were performed using ANOVA with the vigilance state as a factor. Autocorrelograms were calculated to roughly estimate the temporal organization of the successive spikes, since they have been described as a standard method used to detect some periodicities or singularities in the spike train (Perkel et al. 1967; Pernier & Gerin, 1975). They were computed from 1120 spikes, using 10 ms bins over a 1 s period before and after each spike.

Furthermore, the relationship between coincident unit activity and EEG was evaluated by cross-correlation and spectral analyses. Spectral analyses were performed over short periods of robust and typical cortical activity (typically 5–10 s). Spectral analyses (0–50 Hz) were also used to determine the frequencies of any oscillatory features present in the spike train (Kocsis & Vertes, 1992). This is in keeping with the method described by Kaneoke & Vitek (1996) except that power spectra were taken from rate histogram data instead of autocorrelograms. Power density spectra of the spike train and the coincident EEG were calculated using the Power Density Spectrum transform of the NeuroExplorer software. Finally, the correlation between the unit and cortical activities was estimated by a cross-correlation technique, using the spike-triggered waveform-averaging subroutine of the Spike 2 software. These correlations were computed from 1120 spikes.

All data are expressed as means ± s.e.m., and the significance level for all statistical analyses was set at P < 0.05.

Results

Single-unit recordings in the DRN

When lowering recording electrodes aimed at the medial part of the DRN, a characteristic jump of the baseline was usually recorded, signalling the exit from the acqueductal space. Between 50 and 150 μm deeper, a region containing slow- and fast-firing cells, with broad as well as thin spike waveforms, was encountered (5400–7000 μm below the cortical surface). Histological examination confirmed that this area corresponded to the DRN (Fig. 1). A total of 770 DRN units were recorded from 34 non-anaesthetized rats.

Figure 1. A coronal section showing the recording site (dark spot indicated by the arrow) in the DRN marked by Pontamine Sky Blue iontophoretic deposit.

Figure 1

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Horizontal scale bar represents 500 μm.

In our anaesthetic-free preparation, DRN neurons exhibited two different waveforms (Fig. 2). The majority of neurons showed a particularly broad asymmetrical biphasic spike waveform, with a first positive part of large amplitude (AS, n = 544, 1.5–3.7 ms duration; Fig. 2Aa and b). The descending part, which did or did not exhibit an initial notch, was followed by a smaller amplitude negative component of long duration. A second type of cells, intermingled with the previous ones, displayed a symmetrical positive–negative spike waveform, with both components of equal amplitude (SYM, n = 226, Fig. 2Ba and b). Symmetrical cells presented a large distribution of spike durations from 0.6 to 3.2 ms. Nevertheless, AS cells exhibited a significantly broader spike waveform compared to SYM cells as shown in Fig. 2C. This histogram of spike durations indicates that the DRN neurons were clearly segregated into two groups, with a large overlap in spike durations between the two groups. Indeed, when the recording electrode was lowered closer to these cells, their waveform characteristics remained unchanged until the cells were lost. Therefore, two populations of DRN cells (AS and SYM) were considered for further analyses on the basis of their spike waveform.

Figure 2. Raw tracings and the distribution of DRN spike waveform duration.

Figure 2

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A, 71% of neurons (n = 544) recorded in the medial part of the DRN displayed a broad asymmetrical biphasic spike waveform (AS cells). The first positive part was of great amplitude and the descending part exhibited (Aa) or did not exhibit (Ab) an initial notch, followed by a long duration negative phase of small amplitude. B, a further 29% of cells (n = 226) were characterized by a symmetrical positive–negative spike waveform (SYM cells), which exhibited either a narrow spike (Ba) or a broad spike (Bb). Scale bars: vertical, 1 mV; horizontal, 1 ms. C, histogram of the spike waveform durations of DRN recorded cells. Note the biphasic distribution of the spike durations, indicating two distinct groups of DRN neurons with a large overlap in the spike duration between the two groups.

The overall mean firing rates of these neurons, across the sleep–wake cycle, are illustrated in Fig. 3A. Statistical comparisons showed that AS cells exhibited globally a slower mean firing rate than SYM cells in all vigilance states. In addition, a significantly lower c.v. value was calculated for AS cells compared to SYM cells during W and SWS, reflecting a relatively more regular distribution of the interspike intervals of AS neurons during W and SWS (Fig. 3B).

Figure 3. Overall firing properties of DRN neurons across the sleep–wake cycle.

Figure 3

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A, mean firing rates recorded for each population of DRN cells during each vigilance state. B, mean coefficient of variations (c.v.) in the spike train calculated for each population of DRN cells during each vigilance state. Comparisons between mean firing rates and mean c.v. of the two populations of DRN cells were performed within a same vigilance state by Student's t test for unpaired data. In A and B, *P < 0.001 between the AS and SYM groups. C, scatter plots of the mean firing rates versus the spike duration for AS and SYM cells. Note the inverse relationship between the mean firing rate and spike duration for SYM cells. D, scatter plots of the mean coefficient of variations versus the spike duration for AS and SYM cells. Note the trend of the mean c.v. for SYM cells to decrease as the spike duration increases.

As shown in Fig. 2C, a greater proportion of SYM cells (133 out of 226 neurons) presented a spike waveform duration longer than 1.5 ms as did AS cells. However, no definite criterion, except an arbitrary one, would allow us to divide SYM cells in two distinct groups. Therefore, SYM cells were initially considered as only one population for further analyses. A strong inverse correlation between the mean firing rate and the spike duration was found for SYM cells in the three vigilance states (Fig. 3C), demonstrating that SYM cells with a narrow spike fire faster than other SYM cells; such a correlation was not found for AS cells (coefficient of regression: W, 0.13 for SYM cells versus 0.02 for AS cells; SWS, 0.11 versus 0.04; PS, 0.00 versus 0.21). Moreover, the c.v. values appeared higher for SYM cells with a shorter spike duration (Fig. 3D), suggesting that SYM cells displaying a narrow spike are more irregular than other SYM cells. Again, such a relation was not true for AS cells (coefficient of regression: W, 0.07 for SYM cells versus 0.01 for AS cells; SWS, 0.03 versus 0.00), except in PS, when the c.v. value of AS cells was positively correlated with the spike duration (0.01 for SYM cells versus 0.32 for AS cells).

The modulation of the neuronal activity along the sleep–wake cycle was assessed for each cell recorded long enough in both W and SWS to allow analyses (see Methods). The rate ratio (see Methods) was significantly higher for AS cells than SYM cells (P < 0.001, 72%, n = 431 versus 56%, n = 153). Histograms in Fig. 4A showed that a great proportion of AS cells had a rate ratio in the 80–100% range, suggesting that the firing rate of these AS cells was particularly dependent on vigilance states. In contrast, rate ratios were randomly distributed for SYM cells. Values on the scatter plots of mean firing rates in W and SWS form a large round cloud for AS cells (Fig. 4B), whereas they are organized more along the diagonal for SYM cells, especially for cells with high mean firing rates (above 5 Hz), suggesting that these latter cells all displayed a SWS mean firing rate similar to the one observed during waking.

Figure 4. Firing rate properties of AS and SYM cells in W and SWS.

Figure 4

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A, detailed histograms of rate ratios between W and SWS calculated for AS and SYM cells. Rate ratio was calculated as (W − SWS)/W for W-active neurons and as (SWS − W)/SWS for SWS-active cells (see Methods). Note that a great proportion of AS cells have a rate ratio in the 80–100% range, while these ratios are randomly distributed for SYM cells. B, scatter plots of SWS mean firing rates versus W mean firing rates for each DRN population of cells. Note the difference of scale between AS and SYM cells.

For the following analyses, cells were classified in three groups as a function of the modulation of their activity in W and SWS: (i) W-active cells, which exhibited a higher mean firing rate in W than in SWS and a rate ratio > 0.25; (ii) in contrast, SWS-active cells, which presented a lower activity in W compared to SWS and a rate ratio > 0.25; and (iii) cells with a similar mean discharge rate in W and SWS, i.e. rate ratio < 0.25 (Tables 1 and 2).

Table 1.

Firing properties of AS DRN cells across the sleep–wake cycle

Wakefulness Slow wave sleep Paradoxical sleep
A. Mean firing rates of each subpopulation of AS cells
 W-active
   Mean firing rate 1.26 ± 0.04* 0.29 ± 0.02⋄ 0.04 ± 0.01*⋄
   Range 0.10–5.02 0.00–3.50 0.00–0.95
   n 359 (83%) 86
 SWS-active
   Mean firing rate 0.36 ± 0.10  0.87 ± 0.16  0.49 ± 0.12  
   Range 0.00–3.35 0.04–5.17 0.00–1.1
   n 38 (9%) 10
 W = SWS
   Mean firing rate 0.80 ± 0.10* 0.75 ± 0.08⋄ 0.14 ± 0.08*⋄
   Range 0.13–2.68 0.12–2.11 0.00–0.74
   n 34 (8%) 9
B. Mean c.v. of each subpopulation of AS cells
 W-active
   Mean c.v. 0.60 ± 0.02* 0.95 ± 0.03⋄ 1.89 ± 0.43*⋄
   Range 0.18–1.89 0.17–2.35 1.07–2.97
   n 275 159 4
 SWS-active
   Mean c.v. 1.07 ± 0.15* 0.86 ± 0.07⋄ 2.24 ± 0.24*⋄
   Range 0.44–1.69 0.29–2.01 1.38–3.35
   n 10 33 7
 W = SWS
   Mean c.v. 0.61 ± 0.08* 0.67 ± 0.06⋄ 1.84 ± 0.68*⋄
   Range 0.25–1.62 0.21–1.32 0.57–2.90
   n 21 29 3
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Neurons are classified into three subpopulations considering their rate ratio (see Methods). Note that only cells for which the rate ratio could have been calculated, i.e. recorded long enough both in W and SWS, are presented here. Note also that PS parameters are calculated for cells recorded in the three vigilance states. A, mean firing rates of each subpopulation of AS cells. Comparisons between mean firing rates in the three vigilance states are performed within each subpopulation of AS DRN cells by an ANOVA for paired data and the Tukey–Kramer post hoc test. Since cells are classified as a function of the rate ratio between W and SWS, differences between W and SWS mean firing rates are implicit and only the difference from the PS mean firing rate has been specified:

*

W value is significantly different from PS value

⋄

SWS value is significantly different from PS value; P < 0.001 for W-active cells and P < 0.01 for W = SWS cells. B, mean c.v. of each subpopulation of AS cells. Comparisons between mean c.v. in the different vigilance states are performed within each subpopulation of AS DRN cells by an ANOVA and the Tukey–Kramer post hoc test. Note that only cells which exhibited enough spikes in a given vigilance state to allow the c.v. calculation are presented here.

*W value is significantly different from PS value

⋄SWS value is significantly different from PS value; P < 0.001.

Table 2.

Firing properties of SYM DRN cells across the sleep–wake cycle

Wakefulness Slow wave sleep Paradoxical sleep
A. Mean firing rates of each subpopulation of SYM cells
 W-active
   Mean firing rate 2.87 ± 0.48  1.22 ± 0.29*  4.04 ± 1.56*
   Range 0.09–17.15 0.00–11.44 0.00–23.59
   n 72 (47%) 72 (47%) 16
 SWS-active
   Mean firing rate  1.39 ± 0.37⋄  2.96 ± 0.58*   5.52 ± 1.70*⋄
   Range 0.00–11.90 0.09–17.20 0.07–13.28
   n 47 (31%) 47 (31%) 8
 W = SWS
   Mean firing rate 7.28 ± 1.59 6.92 ± 1.52 5.25 ± 3.14
   Range 0.11–29.71 0.09–29.64 0.00–22.93
   n 34 (22%) 34 (22%) 8
B. Mean c.v. of each subpopulation of SYM cells
 W-active
   Mean c.v.  0.79 ± 0.06‡  1.09 ± 0.08‡  2.27 ± 0.42‡
   Range 0.12–2.36 0.12–2.32 0.76–4.69
   n 56 40 9
 SWS-active
   Mean c.v. 1.47 ± 0.14 1.19 ± 0.05 1.41 ± 0.22
   Range 0.60–3.16 0.60–2.04 0.69–2.26
   n 25 45 7
 W = SWS
   Mean c.v. 0.80 ± 0.09 0.81 ± 0.08 0.18 ± 0.52
   Range 0.18–1.68 0.26–1.90 0.24–2.28
   n 24 33 4
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Neurons are classified into three subpopulations considering their rate ratio (see Methods). Note that only cells for which the rate ratio could have been calculated, i.e. recorded long enough both in W and SWS, are presented here. Note also that PS parameters are calculated for cells recorded in the three vigilance states. A, mean firing rates of each subpopulation of SYM cells. Comparisons between mean firing rates in the three vigilance states are performed within each subpopulation of SYM DRN cells by an ANOVA for paired data and the Tukey–Kramer post hoc test. Since cells are classified as a function of the rate ratio between W and SWS, differences between W and SWS mean firing rates are implicit and only the difference from the PS mean firing rate has been specified:

*

SWS value is significantly different from PS value

⋄

W value is significantly different from PS value; P < 0.05 for W-active cells and P < 0.01 for SWS-active cells. B, mean c.v. of each subpopulation of SYM cells. Comparisons between mean c.v. in the different vigilance states are performed within each subpopulation of SYM DRN cells by an ANOVA and the Tukey–Kramer post hoc test. Note that only cells which exhibited enough spikes in a given vigilance state to allow the c.v. calculation are presented here.

‡

indicates that the three groups are significantly different from each other; P < 0.001.

Firing rate and pattern of AS cells across the sleep–wake cycle

The vast majority (83%) of AS cells were active in W with a relatively regular discharge, as assessed by their low c.v. (Fig. 5 and Table 1). Incidentally, we found some cells that exhibited a particularly marked bursting activity (Fig. 6). They slowed down as the rat fell asleep and became more irregular, displaying isolated spike trains, or even progressively ceased firing. During PS, most of the W-active cells were silent and resumed firing only on subsequent awakening. We observed that the firing rate generally began to increase a few seconds before the increase of muscle tone through the transitions from SWS or PS to W (Figs 5B and C), in agreement with previous studies (Trulson & Jacobs, 1979; Adrien & Lanfumey, 1986). Spikes were then sometimes organized in one or more ‘bursts’ of two to four spikes.

Figure 5. Polygraphic recordings (EMG and EEG) and unit activity of a typical W-active AS DRN neuron across the sleep–wake cycle.

Figure 5

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A, EMG and EEG recordings and rate histogram (bottom, 1 s binwidth) along successive vigilance states. The weak EMG and the presence of high amplitude slow waves on the EEG are characteristic of SWS in the rat, whereas the tonic muscle activity and the low amplitude fast waves indicate a state of W. Paradoxical sleep is clearly recognizable by a low amplitude EEG in the theta range and a complete loss of muscle tone. This cell exhibited a tonic discharge in W (mean firing rate of 2.03 spikes s−1), decreased its activity in SWS (0.08 spikes s−1) and became silent during the PS episode. The time axis has been cut to show waking from PS (total duration, 265 s). Also shown, on an expanded time scale, are a microarousal episode (B) and a PS to W transition (C) of the same neuron illustrated in A. Note that the cell exhibited in both cases a burst of spikes a few seconds prior to the awakening. During W, the discharge was more or less regular (c.v. = 0.51) and decreased progressively as the rat felt asleep (B).

Figure 6. Polygraphic recordings (EMG and EEG) and unit activity of an AS DRN neuron across the sleep–wake cycle.

Figure 6

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This W-active AS cells is characterized by a burst firing pattern in W. Asterisks indicate the presence of doublets, and of a triplet for the first burst.

Moreover, the remaining 17% of the AS cells (72 cells) did not decrease their mean firing rate in SWS compared to W (Table 1A); half of them were even more active in SWS than in W. Among these cells, 19 neurons were recorded in PS, during which eight of them exhibited a mean firing rate higher than 0.4 spikes s−1. Regarding the discharge pattern, it is noteworthy that neurons maintaining the same firing rate through the W to SWS transition displayed a particularly regular discharge pattern (Table 1B).

Firing rate and pattern of SYM cells across the sleep–wake cycle

About half of the SYM cells exhibited mean firing rates significantly higher in W and PS than in SWS (Fig. 7A and Table 2). The mean c.v. calculated in W was lower than in SWS and PS, suggesting at first glance a more regular activity of these cells in W compared to sleep. Indeed, Fig. 7Aa shows that the PS firing rate was not stable; tonic spike trains were separated by episodes of slower activity or even silences. However, the firing pattern within these tonic spike trains could be particularly regular (Fig. 7Ab).

Figure 7. Polygraphic recordings (EMG and EEG) and rate histogram (1 s binwidth) or unit activity of SYM DRN neurons across the sleep–wake cycle.

Figure 7

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Aa, this typical W-active SYM DRN neuron was active in W (mean firing rate, 1.96 spikes s−1) and ceased to fire in SWS; the time axis has been cut to show the transition towards PS (SWS total duration, 148 s, during which the cell was silent). During PS, the activity of this neuron dramatically increased (mean firing rate, 8.72 spikes s−1); note that the firing rate was also not stable, in that tonic spike trains of sustained activity were separated by episodes of slower activity and sometimes silences (mean PS c.v. = 4.69). Ab, expanded time scale of recordings through W (left) and PS (right) of the neuron illustrated in Aa. Note the local regularity within the spike train during both vigilance states. B, this typical SWS-active SYM DRN cell, particularly slow in W (mean firing rate, 0.17 spikes s−1), started to fire as soon as the rat fell asleep (0.61 spikes s−1) and dramatically increased its activity during the PS episode (7.61 spikes s−1). Note that the discharge rate began to increase prior to the onset of the PS episode. Ca, this typical SYM (W = SWS) DRN neuron exhibited a sustained activity in all vigilance states, with a similar mean firing rate in W and SWS (2.01 and 2.00 spikes s−1, respectively) and a slightly higher discharge in PS (2.22 spikes s−1). Note the highly regular pattern of discharge of this neuron during all the three vigilance states (Cb): SWS c.v. = 0.44, PS c.v. = 0.34 and W c.v. = 0.32.

One-third of SYM cells increased their firing in SWS compared to W, and again significantly sped up their activity in PS (Fig. 7B and Table 2). Therefore, these neurons exhibited an activity that was a mirror image of that of the majority of AS DRN cells. Furthermore, regarding the firing pattern, they displayed an irregular activity in the three vigilance states.

The last 22% of SYM cells presented a similar mean firing rate in W and SWS. Regarding the discharge pattern of this group of cells, it should be pointed out that SYM cells characterized by a broad spike (> 1.5 ms) displayed a surprisingly regular and stable activity in all vigilance states (Fig. 7C; mean c.v.: 0.54 ± 0.08 in W, n = 12 cells, and 0.57 ± 0.08 in SWS, n = 19). In contrast, cells with a narrower spike were more irregular, with a mean W c.v. of 1.05 ± 0.11 (n = 13 cells) and a mean SWS c.v. of 1.12 ± 0.09 (n = 14). It is noteworthy that, for all SYM cells with a spike duration shorter than 1.5 ms, transient increases of the firing rate were often observed during W, when the rats made brisk movements, which is in line with previous electrophysiological studies describing cells that displayed responses to a variety of sensorimotor events (Waterhouse et al. 2004).

During PS, 84% (48 out of 57 cells) of the recorded SYM cells exhibited a mean firing rate higher than 0.2 spike s−1; higher than 1.1 spikes s−1 for 56% of the cells (32 out of 57 cells). More specifically, all the SYM cells with a spike duration shorter than 1.5 ms were spontaneously active in PS. These cells generally displayed an irregular, more or less bursty, pattern in all vigilance states. Since Kocsis & Vertes (1992) showed that the activity of some DRN cells is correlated with the theta rhythm of the hippocampus in freely moving rats, we assessed whether such a correlation was present in unanaesthetized rats. Firing pattern analyses during PS were conducted on 17 SYM cells with short spike durations (< 1.5 ms); among them, six exhibited an activity dependent on the EEG theta rhythm. The activity of one of these cells is depicted in Fig. 8A. A pronounced concentration of power in a narrow frequency range of 6–8 Hz, corresponding to the theta rhythm, was present, as expected, in the FFT computed for 5 s of PS. Associated with this, a prominent peak emerged in the coincident unit's power density spectrum in the same frequency range. The peak-shaped cross-correlation (CC) between the unit and EEG activities during PS indicates a strong correlation between the two signals. During SWS, no relationship between the EEG and spikes power density spectra of the same neuron was present, and the coincident EEG activity was very poorly related to the spike train, as testified by a monotonous CC (Fig. 8B). The mean power density spectrum of unit activities for the six SYM neurons which exhibited a discharge pattern in the frequency of the theta bands during PS has been plotted in Fig. 8C, with the mean FFT of the coincident EEG. A clear relationship appears between the EEG and spikes power density spectra during PS, but not in SWS.

Figure 8. Relationship between the EEG and spike frequency spectra of SYM units during sleep.

Figure 8

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A and B show the firing pattern during PS and SWS, respectively, of a typical SYM (SWS-active) DRN on an expanded time scale (5 s of raw data) in the top panels. Below are shown the corresponding power spectral densities (PSD) of the EEG and of the spike train, the autocorrelogram (AC) of the spike train and cross-correlation (CC) between coincident unit activity and EEG as estimated by a spike-triggered averaging technique. AC and CC are plotted for the total episodes of PS and SWS (1120 spikes). Please note that CCs are not plotted with the same amplitude in order to take into account the fact that the PS EEG amplitude is about 2.5 times smaller than the SWS EEG amplitude. During PS (A), the EEG power was highly concentrated in the theta band. This SYM neuron was rhythmically bursting acitivity during PS as shown by clear peaks and troughs on the AC, corresponding to a frequency of 7 Hz on the PSD. The spike train appeared strictly correlated to the coincident EEG activity, as testified by the peak-shaped CC and the strict relationship between the EEG and spike periodograms. The same SYM neuron, recorded in SWS (B), decreased bursting and fired more randomly, as evidenced by a less peak-shaped AC and the flat PSD. The coincident EEG activity was very poorly related to the spike train, as testified by a monotonous CC and the absence of relationship between the EEG and spike periodograms. In the left panel of C are plotted the mean PSD of 6 SYM neurons which exhibited a discharge pattern in the frequency of the theta bands during PS. In the right panel, for the same neurons, during SWS, no relationship between the EEG and spike periodograms could be found.

Discussion

In the present work, we demonstrate a significant diversity in the firing behaviour of DRN neurons across the sleep–wake cycle in rats, using extracellular recording of a large sample of single units. We identified two major types of cells, based on their spike waveforms: (i) AS cells, characterized by broad mostly positive spikes; and (ii) SYM cells, displaying symmetrical positive–negative spikes with a large distribution of spike durations (0.6–3.2 ms). Eighty-three per cent of AS cells were active in W, displaying a relatively regular discharge. They slowed or even progressively stopped firing when the rat fell asleep and became virtually silent during PS, in accordance with previous studies in awake animals (McGinty & Harper, 1976; Trulson & Jacobs, 1979; Levine & Jacobs, 1992; Gervasoni et al. 2000). These cells are classically assumed to be serotonergic. Nevertheless, cells characterized by the same spike waveform but exhibiting higher mean firing rate in SWS compared to W were also recorded. Besides the well-described AS cells, we found a large number of SYM cells, which also showed broad spikes, but symmetrical positive–negative. Some of the SYM cells exhibited a discharge pattern as regular as that observed in most of the AS cells. In contrast with the majority of AS cells, 47% of SYM cells were active in both W and PS compared to SWS, and one-third were more active in SWS and PS than in W. In summary, we found that one-third of AS/SYM cells exhibiting broad spikes increased or did not change their firing rate during sleep compared with wakefulness. Moreover, 62% of these cells were active in paradoxical sleep when most of raphe cells were silent.

It should be noted that the recordings were made in head-restrained animals. Although this preparation is quite stable, we need to deal with the relative instability of the brainstem compared to the forebrain and the unpredictability in behaviour of the rat. Therefore, performing juxtacellular labelling, for instance, which requires a particularly stable preparation to be as close as possible to the cell without injuring it, is technically challenging. This is one of the limitations of working in non-anaesthetized animals. Another limitation of this technique is the head fixation of the rat. It provides stable enough conditions for neuronal recordings of excellent quality and therefore allows the performance of challenging experiments, but it does not give access to the complete range of behaviours expressed by freely moving animals, such as locomotion or head movements. Therefore, the head-restraining technique is a compromise between the fully stable and malleable anaesthetized preparation and the particularly unstable but colourful freely moving preparation.

Our data may account for an important controversy regarding the neurochemical identity of DRN cells exhibiting broad spikes; only half of the broad DRN immunohistochemically identified cells contain 5-HT (Allers & Sharp, 2003; Kirby et al. 2003). Anatomical studies have shown that the middle portion of the rat DRN presents the largest proportion of 5-HT-labelled cells (Steinbusch et al. 1981; Descarries et al. 1982), but the same region also contains twice as many non-5-HT neurons. Taken together, these data suggest that the majority of DRN neurons do not contain 5-HT. The DRN contains a large number of GABAergic neurons (Nanopoulos et al. 1982; Varga et al. 2001; Allers & Sharp, 2003). Neurons containing glutamate, dopamine and/or various neuropeptides have also been described (Wiklund et al. 1981; Trulson et al. 1985; Yoshida et al. 1989; Clements et al. 1987; Charara & Parent, 1998; Valentino & Commons, 2005). Beck's group (Kirby et al. 2003; Beck et al. 2004) have demonstrated that the intracellular characteristics previously used to distinguish 5-HT-containing cells were not different between the 5-HT-immunopositive and 5-HT-immunonegative cells. In keeping with other groups (Day et al. 2004), they showed that a significant number of non-5-HT as well as 5-HT neurons express the 5-HT1A receptor, making it impossible to distinguish 5-HT from non-5-HT cells using only pharmacological data. In addition, besides the neurons that appear to be 5-HT cells but in fact are not, some groups described cells that did not exhibit typical electrophysiological properties of 5-HT neurons, e.g. burst-firing or fast-firing activities, but which nonetheless contain 5-HT (Shima et al. 1986; Hajos et al. 1995; Allers & Sharp, 2003). Taken together, these data suggest that the electrophysiological and pharmacological characteristics classically used to distinguish 5-HT from non-5-HT cells in the DRN are not reliable.

Taken together, the SYM cells recorded in the present study displayed a faster mean firing rate and a thinner spike than AS cells. Their biphasic symmetrical waveforms are in keeping with those previously described for DRN non-5-HT cells (Aghajanian et al. 1978; Vandermaelen & Aghajanian, 1983; Allers & Sharp, 2003). Using juxtacellular labellings, Allers & Sharp (2003) recently showed that most of the fast-firing DRN neurons (> 3 Hz) were immunopositive for glutamic acid decarboxylase (GAD). These neurons were most frequently found within the lateral regions of the DRN where the density of 5-HT neurons is lower, but they were also present amongst 5-HT neurons. These data could suggest that part of SYM cells represent GABAergic cells.

One-third of the SYM cells exhibited a mirror image activity to that of W-active AS cells, i.e. they became more and more active as the rat fell asleep and increased their firing discharge during SWS to PS transitions. These data suggest an active inhibition of W-active AS cells through local SWS-active SYM ones. This hypothesis is in line with previous studies demonstrating that the cessation of firing of monoaminergic neurons could be caused by an active inhibition through local GABAergic neurons (Ford et al. 1995; Nitz & Siegel, 1997; Gervasoni et al. 2000). Moreover, among the SYM cells that were active in PS, one-third displayed a discharge pattern in the theta band frequency, in keeping with a previous study in freely behaving rats (Kocsis & Vertes, 1992). In the median raphe, increasing GABA tone leads to induction of hippocampal theta rhythm by releasing the theta generators from serotonergic inhibition. Besides the inhibition of 5-HT neurons, putative raphe glutamatergic theta-promoting circuits are activated, which can also be targets of GABAergic inhibition (Li et al. 2005).

A large number of non-5-HT DRN neurons, notably GABAergic and glutamatergic, are projection cells (Datiche et al. 1995; Ford et al. 1995; Kiss et al. 2002; Aznar et al. 2004). Both 5-HT and non-5-HT projection neurons have richly branched axon collaterals (Li et al. 2001), suggesting that they are also involved in local mechanisms within the DRN. Axon collaterals of ascending 5-HT fibres exert negative feedback influence by impinging directly upon somatodendritic autoreceptors of 5-HT neurons themselves (Blier & de Montigny, 1987; Sprouse & Aghajanian, 1987). Another feedback loop involves local GABAergic cells (Liu et al. 2000; Boothman & Sharp, 2005), synapsing on 5-HT cells (Wang et al. 1992), which also express GABAA and GABAB receptors (Gao et al. 1993; Rodriguez-Pallares et al. 2001; Wirtshafter & Sheppard, 2001). Therefore non-5-HT projecting neurons can regulate the activity of 5-HT cells through local circuits or mediate afferent inputs.

In summary, from our recordings of 770 DRN cells, we found two main populations of neurons in relation to their spike waveforms: AS cells with broad asymmetrical spikes and SYM cells displaying either broad or narrow symmetrical spikes. Both of these groups are subdivided into three subpopulations as a function of their activity along the sleep–wake cycle. Incidentally, a theta-related activity has been found only for a subset of SYM cells. We can therefore argue for a great diversity in the firing behaviour of DRN cells in unanaesthetized rats. Such a DRN heterogeneity may be masked in anaesthetized animals, firstly because of the obvious lack of changes in vigilance states and secondly because anaesthesia interferes with the modulation of 5-HT activity, since anaesthetics act directly as allosteric modulators of GABAA receptors (Narahashi et al. 1998; Belelli et al. 1999). Since it has been shown that prefrontal cortex and lateral habenula influence large numbers of 5-HT neurons by targeting DRN GABA neurons (Ferraro et al. 1996; Hajos et al. 1998; Varga et al. 2001, 2003; Jankowski & Sesack, 2004), modifications of GABAergic components may alter neuronal responses (Tao & Auerbach, 1994). Moreover, the tonic activation of 5-HT1A autoreceptors is masked in anaesthetized rats (Haddjeri et al. 2004). Our model may therefore reveal various patterns of DRN activity undetectable under anaesthesia.

Different subpopulations of AS or SYM cells could represent neurons of the same neurochemical identity but receiving different inputs. Anatomical studies indicate that a large number of forebrain structures are able to modulate the activity of DRN cells, and these studies describe a differential distribution of these afferents to different subdivisions of the DRN (Peyron et al. 1998; Gervasoni et al. 2000). Differential inputs could explain the existence of a SYM cells subpopulation, characterized by narrow spikes and all active during PS, with one-third displaying a discharge pattern in the theta band frequency. Indeed, recent studies described two activity patterns of 5-HT neurons, with respect to their correlated activity with hippocampal theta rhythm (Kocsis et al. 2006).

The neurochemical identity of these different subpopulations of cells is a tricky problem. Our results put into question the dogma of slow and pacemaker firing, broad-spike, 5-HT cells which are active in W, decrease their firing in SWS and become silent in PS. The present work illustrates the great diversity of DRN activities across the sleep–wake cycle in rats. Taken together, these data may account for the controversy regarding the neurochemical identity of DRN cells exhibiting broad spikes (Allers & Sharp, 2003; Kirby et al. 2003), as well as the heterogeneity of responses from presumed 5-HT cells to pharmacological protocols (Liu et al. 2000; Martin-Ruiz & Ugedo, 2001; Mlinar et al. 2005). They also suggest the existence of potential local circuits able to shape the activity of 5-HT neurons efficiently or modulate their afferent inputs. Such a diversity of DRN cell behaviour in unanaesthetized animals can help to explain the complexity of symptoms associated with serotonergic dysfunction. Its comprehension might therefore bring relevant physiological answers to important health problems.

Acknowledgments

This work was supported by grants from Canadian Institutes of Health Research (CIHR). N.U. was recipient of a fellowship from Wyeth-Ayerst Canada & CIHR-IRSC. We thank Dr Damien Gervasoni for his efficient computational expertise. We also thank Drs Bernat Kocsis and Lionel Dahan for their helpful comments on our manuscript.

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