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1 Department of Anatomy and Physiology, School of Medicine, Laval University, Québec, Canada G1K 7P4
2 Department of Clinical Science, Osp. L. Sacco, School of Medicine, University of Milan, 20157, Milan, Italy
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(Received 7 July 2004;
accepted after revision 2 November 2004;
first published online 4 November 2004)
Corresponding author I. Timofeev: Department of Anatomy and Physiology, School of Medicine, Laval University, Québec, Canada G1K 7P4. Email: igor.timofeev{at}phs.ulaval.ca
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Introduction |
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Despite the wide clinical use of the SEP, its cellular basis and variability across the various phases of activity is not well understood. Some studies argued that the variability of cortical responses was based on the intrinsic membrane properties of cortical neurones (Istvan & Zarzecki, 1994; Zhu & Connors, 1999) and suggested that local circuit interneurones were the first cortical neurones to respond to peripheral stimuli (Yamamoto et al. 1988). Other data suggested that ongoing activity in the cortical network may significantly contribute to the variability of responses (Arieli et al. 1996; Azouz & Gray, 1999; Kisley & Gerstein, 1999; Sachdev et al. 2004).
SO occurs as the cyclic alternation of positive and negative EEG waves in animals and humans (Steriade et al. 1993a; Steriade et al. 1993b, c; Achermann & Borbely, 1997). Under this condition all cortical neurones are depolarized and fire spikes during EEG depth-negative waves while, during depth-positive EEG waves, they are hyperpolarized (Steriade et al. 2001; Timofeev et al. 2001b). In anaesthetized animals, both thalamocortical (TC) and thalamic reticular (RT) neurones are hyperpolarized during the EEG depth-positive waves. During the depth-negative EEG waves, the RT neurones fire spike bursts while TC neurones are hyperpolarized, often revealing spindle-like activity (Contreras & Steriade, 1995, 1996; Timofeev & Steriade, 1996; Timofeev et al. 2001a). Thus, both cortical and thalamic neurones are silent during depth-positive EEG waves, and reveal various forms of activity during depth-negative EEG waves. We were interested to learn how the presence of such diverse ongoing activity in the TC network would modify cortical responses to peripheral sensory stimuli. Even a moderate spontaneous hyperpolarization of TC neurones during depth-positive EEG waves is sufficient to displace them from firing threshold, thereby blocking transmission of information toward the cerebral cortex (Timofeev et al. 1996; Bazhenov et al. 1998). These findings contrast with multiple studies showing the maintenance of responses to peripheral stimuli during both sleep states and anaesthesia (Cauller & Kulics, 1988; Emerson et al. 1988; Istvan & Zarzecki, 1994; Azouz & Gray, 1999; Kisley & Gerstein, 1999; Zhu & Connors, 1999; Massimini et al. 2003).
We asked whether or not the stimulation site (prethalamic versus peripheral) is responsible for the difference of responses at the cortical level. We thus performed electrophysiological experiments on the somatosensory system of cats to study the cellular mechanisms underlying the modulation of SEPs during SO.
Some of these data were previously presented in abstract form (Rosanova & Timofeev, 2002).
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Methods |
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Acute experiments were carried out on 35 adult cats of both sexes that had been anaesthetized with ketamine and xylazine (10–15 and 2–3 mg kg–1I.M., respectively). All pressure points and the tissues to be incised were infiltrated with lignocaine (lidocaine; 0.5%). The animals were paralysed with gallamine triethiodide and artificially ventilated, maintaining the end-tidal CO2 concentration at 3.5–3.8%. A permanent sleep-like state, as ascertained by continuous recording of the EEG, was maintained throughout the experiments by administering additional doses of ketamine (5 mg kg–1). For temporal depression of network activity, 1.0–2.0% of halothane was added to the ventilated air for 2–3 min (see Fig. 4). The body temperature was monitored by a rectal probe and maintained at 37°C via a feedback-controlled heating pad. The heart rate was continuously monitored (90–110 beats min–1). The stability of intracellular recordings was ensured by cisternal drainage, bilateral pneumothorax, hip suspension and by filling the hole made for recordings with a solution of 4% agar.
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For peripheral stimulation, the skin above the contralateral median forelimb nerve (MN) was carefully shaved and covered with EEG electrode paste. Tape was then used to affix a pair of silver flat electrodes over the forelimb median nerve. The intensity of stimuli was limited to values that were sufficient to elicit a minimal visible movement in the forepaws. (Similar functional criteria for the intensity of stimuli are used in diagnosis of human subjects.) After establishing the intensity of peripheral stimuli, the animal was paralysed with gallamine triethiodide (20 mg kg–1), and the intensity of stimuli was not modified again. Superficial silver-ball field potential electrodes were used in each experiment to estimate the focus of neocortical response to median nerve stimuli on the contralateral side. This site was used for single, dual, triple and quadruple simultaneous intracellular recordings as well as multisite, multiunit recordings. Up to four David Kopf stepping microdrives were used to advance the intracellular micropipettes. Parallel recordings of focal field potential were obtained by means of tungsten electrodes inserted at different depths in the vicinity of recording pipettes. The same electrodes were used to collect multiunit activities. For electrical stimulation of the medial lemniscus (ML), the only pathway connecting dorsal column nuclei with ventral posterior lateral (VPL) thalamic nucleus (Steriade et al. 1997), a coaxial bipolar electrode was inserted ipsilaterally to the cortical or thalamic recording sites and contralaterally to the nucleus cuneatus at L 3 mm, H –1.5 mm, and A 3 mm. The intensity of ML stimuli was adjusted to elicit cortical field potential responses with an amplitude and shape similar to those evoked by contralateral MN stimuli during the active states of the network (see Fig. 2). Stimuli were applied either to the median forelimb nerve or to the ML with a frequency of one every two seconds. Between 30 and 100 stimuli were collected for analysis from each studied neurone. To obtain intracellular recordings from VPL neurones, a small portion of the suprasylvian gyrus (3–5 mm), underlying the white matter and fornix above the VPL nucleus, was removed by suction. Then, the pipettes were placed into either the anterior or middle parts of the ventrobasal complex at the expected location of the forelimb responsive neurones (Rose & Mountcastle, 1952). The neurones responding to median nerve stimuli were located at 3–5 mm ventral to the thalamic surface. Extra- and intracellular recordings from dorsal column nuclei (nucleus cuneatus) were performed at 1–2 mm lateral and 1–3 mm posterior to the obex.
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). A high-impedance amplifier (bandpass, 10 kHz) with an active bridge circuitry was used to record and inject current into the cells. All electrical signals were sampled at 20 kHz and digitally stored on Vision (Nicolet, Wisconsin, USA). Offline computer analysis of electrographic recordings was done with IgorPro software (Lake Oswego, Oregon, USA). Statistical analysis was conducted with JMP software (Cary, North Carolina, USA). All numerical values are expressed as a mean ±standard deviation (S.D.). At the end of experiments, the cats were given a lethal dose of intravenous sodium pentobarbital (50 mg kg–1). All experimental procedures were performed according to national guidelines and were approved by the committee for animal care of Laval University.
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Responses of cortical neurones to peripheral and prethalamic stimuli
Using single or simultaneous double, triple and quadruple intracellular recordings, we studied the responsiveness of 132 cortical neurones to forelimb median nerve and/or ML stimuli during various phases of SO. Concomitantly, we recorded the local field potentials and multiunit activities. All intracellularly recorded neurones were classified by electrophysiological criteria as regular-spiking, fast-rhythmic-bursting or intrinsically bursting (Connors & Gutnick, 1990; Gray & McCormick, 1996; Steriade et al. 1998). Fast-spiking neurones were not recorded in this set of experiments. Since we did not find any consistent relationship between the electrophysiological properties of neurones and their response patterns to MN or ML stimuli, we do not make further reference to the electrophysiological type of neurones in this paper.
We then compared the responses of cortical neurones as observed during different components of cortical SO (Fig. 1). All cortical neurones responded to peripheral MN stimuli during all phases of SO; however, the pattern of field potential and cellular responses varied as a function of SO phases. During active states of the network, which corresponded to the up state of cortical neurones, the initial components of field depth-negative potential were polyphasic, composed of two or three negative deflections (Fig. 1B). During the silent phase of network activity, which corresponds to an EEG depth-positive field potential and to the down state of cortical neurones, the responses were different. On average, latency to the peak of the field potential response increased by 4.5 ms (P < 0.001 (Student's paired t test) (Table 1)) and the initial phase of cortical field potential response had a tendency to be monophasic (Fig. 1B). Intracellularly, all the neurones located within the focus of field potential response were found to respond to forelimb MN stimulation. We found three major types of excitatory responses of cortical neurones to MN stimuli (Fig. 1B1–3) and one inhibitory (Fig. 1B4). During the active phases of network, 68.7% of neurones fired reliably and with low-latency variability (<1 ms) during the first peak of field evoked potential (Fig. 1B1 and Fig. 2A, cells 1 and 2). These neurones fired in 48.3 ± 7.2% of cases to the stimuli applied during the silent phases of cortical activity, as estimated by depth-positive EEG waves, and the latency of both the onset of synaptic response and spikes was longer and varied in a range of 2–3 ms. During active states the second group of neurones (19.7%) did not always fire spikes, but if they did, the spikes occurred during the second peak of evoked potential (Fig. 1B2 and Fig. 2A, cells 3 and 4). These same neurones never fired spikes in response to stimuli applied during depth-positive EEG waves and the amplitude of depolarizing response was largely reduced during this phase of oscillation. A third group of neurones exhibiting excitatory responses (5.3%) fired throughout the initial excitatory component of field response during both the active and silent phases of SO (Fig. 1B3). The remaining seven neurones (5.3%) responded with primary IPSPs during active network states (Fig. 1B4) and their response was absent during silent network states. The response latency in a sampled group of neurones was found to have a bimodal distribution (Fig. 2B). Although the range of response latency during active and silent states overlapped (Table 1), the latency of responses during silent states was always longer than the latency of responses during active states (P < 0.001; Student's paired t test; Fig. 2C). All the neurones with short-latency responses (<10.5 ms) fired spikes during the active phase of SO and the variability of firing latency did not exceed 1 ms. It is likely that the firing of the VPL TC neurones directly depolarized the cortical neurones that revealed the short-latency EPSP. Once these neurones fired spikes, they excited other cortical neurones, which had longer EPSP latencies. The neurones that responded during active states with EPSP latencies of longer than 12 ms (those excited during the second peak of evoked potentials) did not fire spikes during the depth-positive phase of SO (Fig. 1B2) and the amplitude of their responses was reduced during the depth-positive EEG waves (Fig. 2, cell 4). Most likely, those neurones were located farther along in the intracortical functional chains and during a depth-positive EEG wave they were not excited by intermediate neurones. Furthermore, an absence of inhibitory responses during silent network states suggests that the excitatory drive produced by MN stimulation was not strong enough to induce the firing of inhibitory interneurones during this network state. Overall, the firing probability during active states was 71.0 ± 0.5% and during silent states it decreased to 24.8 ± 0.3%.
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The difference in responses to ML and MN stimuli during the silent network states led to the hypothesis that the firing ability of TC neurones elicited by the two types of stimuli during these states of network was different. Thus, we obtained intracellular recordings from VPL TC neurones.
Responses of thalamocortical neurones to peripheral and prethalamic stimulation
We recorded the intracellular activities of 45 TC neurones from the thalamic VPL nucleus. Similar to the TC neurones located within ventrolateral nucleus of cats anaesthetized with ketamine–xylazine (Contreras et al. 1996; Timofeev et al. 1996), the TC neurones from the VPL nucleus revealed a slow oscillatory pattern (Fig. 3). During cortical EEG depth-positivity, the VPL neurones were hyperpolarized due to a total disfacilitation in the corticothalamic network. The onset of cortical depth-negativity was associated with a depolarizing potential, probably reflecting a cortical excitatory drive (Fig. 3A, see arrow in expanded panel), which was often followed by a rebound low-threshold spike-burst. Thereafter, the VPL neurones revealed a brief spindle sequence composed of rhythmic IPSPs, often mixed with sequences of EPSPs. Intracellular application of depolarizing current pulses during the period of disfacilitation, associated with prolonged hyperpolarizing potential, elicited a high-frequency spike burst. When current pulses with the same intensity occurred during relatively depolarized active states, they elicited tonic firing (Fig. 3B). All recorded VPL neurones were identified according to their ability to respond with EPSPs to the activation of skin receptive fields (Fig. 4). Seventy per cent of neurones responded to mechanical skin stimulation with tonic excitatory activities, lasting for the duration of stimulus (see upper panel in Fig. 4), while the remaining neurones responded with synaptic excitation in ‘on’–‘off’ mode (not shown). Out of 45 neurones located within the VPL nucleus, 21 neurones responded with EPSPs to both electrical stimulation of the forelimb median nerve and electrical ML stimulation (Fig. 4). The responses of these neurones were subjected to detailed analysis. While the VPL neurones responded with a group of several spikes to MN stimuli during both active and silent phases (Fig. 3C), to ML stimuli they responded with an EPSP in isolation during silent network states and EPSP-spike during active network states (see Fig. 5).
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30 ms. The latency of the first EPSP, however, revealed slight variability (Table 1).The membrane potential of VPL neurones oscillated with amplitudes sometimes exceeding 20 mV (see above, Fig. 3). Thus, following the MN stimulation, we explored the ability of VPL neurones to trigger spikes and measured the latencies of these spikes over a wide range of membrane potentials (Fig. 5). At depolarized levels of membrane potential, the first or the second EPSP of a group of EPSPs arriving at a VPL relay neurone elicited the first spike, transmitting the peripheral signals toward the cerebral cortex. At voltages more depolarized than –65 mV, the VPL neurones usually fired between three and five action potentials. The latency of the first spike in the spike-trains was 9.28 ± 0.27 ms (Fig. 5D). Hyperpolarization of the neurone induced a progressive increase in the latency of the first spike (Fig. 5B and D). At hyperpolarized levels of membrane potential (i.e. below –70 mV), the first 3–5 EPSPs summated and they triggered low-threshold spikes (LTSs) crowned by fast spikes, and the variability of first spike latency increased as indicated by the large S.D. LTSs elicited by MN stimuli at hyperpolarized voltages were accompanied by 3–5 spikes. Only at voltages between –70 and –65 mV did the VPL neurones decrease their firing to 2–3 spikes (Fig. 5C). This suggests that, at these voltages (between –70 and –65 mV), the driving force of summated EPSPs was not strong enough to fire a greater number of spikes in the tonic mode and that LTSs could not be fully de-inactivated to fire spike-bursts. Hence, although the VPL neurones fired spikes at de- and hyperpolarized voltages in response to peripheral stimuli, the latencied of the responses and the firing patterns were different. The response of VPL neurones to ML stimuli was represented by a single EPSP (Fig. 5A, right). The latency to the onset of the EPSP was short and stable (Table 1). When the membrane potential was hyperpolarized below –60 mV, we were unable to elicit either fast action potentials or LTSs. Thus, peripheral stimulation elicited groups of EPSPs, which at more depolarized voltages fired groups of spikes in tonic firing mode and, at hyperpolarized voltages LTSs, accompanied by high-frequency spike-bursts. The latency of initial spikes varied as a function of membrane potential, providing a basis for the variability of early EPSP latencies in cortical neurones. The outright blockage of firing in TC neurones was seen only when prethalamic (ML) stimulation was used at hyperpolarized voltages and the VPL neurones did not fire spikes.
Given the dramatically different responses that VPL neurones had to ML stimulation (single EPSP) and stimulation of the MN (barrages of EPSPs), we decided to examine the firing patterns elicited by MN stimulation at the prethalamic level and thus performed recordings in the dorsal column cuneate nucleus.
Responses of dorsal column nuclei to forelimb stimulation
We intracellularly recorded the activities of 17 neurones located in the nucleus cuneatus that had responded to the MN stimuli. The pattern of cortically generated slow oscillation was not observed in these neurones. In six cases, by approaching the nucleus we were able to obtain intracellular recordings from the axons of primary afferent fibres (Fig. 6). In intra-axonal recordings, the onset of a spike was not preceded by any slow depolarizing events and the rising phase of a spike was 0.2 ± 0.07 ms. Because of such fast spiking, the electrical capacitance of the recording pipette affected the amplitude of these action potentials. The latency of spike onset from the recorded fibres was 3.8 ± 0.3 ms. The responses of peripheral fibres and the initial responses of nucleus cuneatus neurones were highly stereotyped (Fig. 6A). The latency of the initial EPSP recorded from the neurones of the nucleus cuneatus was 4.7 ± 0.3 ms, and each initial EPSP elicited by MN stimulation triggered a spike with a latency of 5.2 ± 0.2 ms. In response to MN stimuli, most of these neurones (n= 10) revealed a secondary depolarization with a latency of 6.1 ± 0.4 ms. In approximately 10% of cases, the second EPSP gave rise to a second spike with a latency of 6.6–7.8 ms (Fig. 6A). The latter depolarizing component could have been generated either by synaptically arriving EPSPs or by the activation of intrinsic neuronal currents. When we compared the waveform of spontaneous spikes with spikes elicited by peripheral stimuli (Fig. 6A, right) we found that all spikes were followed by a fast, brief and pronounced afterhyperpolarizing potential, then followed by a slight afterdepolarizing potential (ADP). In the case of the spontaneous spikes, the ADP of the spike never did reach the firing threshold, while in the case of a spike's ADP evoked by peripheral stimuli; the depolarization had a complex shape and was larger in amplitude, suggesting a contribution of synaptic components to the generation of this depolarizing potential. Therefore, the second spike occurred due to the arrival of EPSPs and not due to any strong, intrinsically generated ADP. In all cases, the first negative peak of cortical evoked potential followed the aforementioned response components of the nucleus cuneatus by 5.2 ± 6.1 ms, indicating that cortical feedback to the dorsal column nuclei (Canedo et al. 2000) did not contribute to the generation of initial response components. Thereafter, the neurones of the nucleus cuneatus revealed brief, multiple synaptic depolarizing potentials, lasting for 15–20 ms, which were superimposed on a steady hyperpolarizing potential (Fig. 6A). These activities were probably generated by intranuclear networks (Canedo & Aguilar, 2000). The second group of spikes, exhibiting a response latency of 10–15 ms, were seen in most (10 out of 17) nucleus cuneatus neurones and occurred after the second negative component of the cortical evoked potential. These spikes were generated, probably, by an excitatory corticofugal drive (Canedo & Aguilar, 2000). Although the responses of dorsal column nuclei neurones to peripheral stimuli were complex, there was no variability in the initial components of their responses.
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Some remarks on terminology
SO was originally identified at the cortical level by M. Steriade and colleagues (Steriade et al. 1993c), who showed that the de- and hyperpolarizing phases of neuronal activities, respectively, give rise to EEG depth-negative and EEG depth-positive waves. Later, similar patterns were recorded in neostriatal neurones by Wilson and Kawaguchi who introduced the now widely used terms ‘up state’ for the depolarizing phases of SO and ‘down state’ for the hyperpolarizing phases of SO (Wilson & Kawaguchi, 1996). Cortically generated SO was also found to entrain the thalamus (Steriade et al. 1993a; Contreras & Steriade, 1995; Timofeev & Steriade, 1996). Similar to neocortical and neostriatal neurones, both the thalamic reticular and TC neurones are hyperpolarized during depth-positive EEG waves due to disfacilitation, i.e. an absence of spontaneous synaptic activities (Wilson et al. 1983; Wilson, 1986; Contreras et al. 1996; Steriade et al. 2001). During depth-negative EEG waves, cortical, neostriatal and inhibitory thalamic reticular neurones are depolarized and fire spikes, while TC neurones are hyperpolarized, reveal rhythmic IPSPs and occasionally fire rebound spike bursts (Contreras & Steriade, 1995; Timofeev & Steriade, 1996). Thus, in the dorsal thalamus, the intracellular activities occurring during depth-negative EEG waves cannot be characterized as either depolarizing or up state. For the purposes of the present study, we used the terms ‘active states’ for processes occurring during EEG depth-negative waves and ‘silent states’ for processes occurring during EEG depth-positive waves. We believe that such terminology better represents the functional state of the TC network during the SO.
The latencies of somatosensory responses along the somatosensory pathway
At the medullary level, the intra-axonal recordings from prenuclear fibres revealed that peripheral stimulation produced incoming volleys at a latency of 3.8 ± 0.3 ms. Similarly to previous findings, the firing of nucleus cuneatus neurones occurred 1.1 ms later (Canedo & Aguilar, 2000). Consistent with recent studies (Coleman et al. 2003a; Coleman et al. 2003b), we found no failures or variability in latency or firing probability at the level of the cuneate nucleus, suggesting a fast and very reliable synaptic transmission of incoming signals. Occasionally, spike doublets were seen and the latency of the second spike reached 6.6–7.8 ms (Fig. 6). In vitro, the dorsal column nuclei neurones did not reveal bursting properties (Nuñez & Buno, 1999), although the intrinsic oscillations in cellular cultures obtained from these neurones can lead to spike clusters (Reboreda et al. 2003), whereas intracellular recordings in vivo often revealed spike doublets (Canedo & Aguilar, 2000; Canedo et al. 2000). Our data suggest that the second spike of a spike doublet is probably driven by synaptic events (Fig. 6) originating in intranuclear local circuits (Canedo et al. 2000). The second group of spikes occurred with latencies of more than 20 ms and was coincident with, or followed, the second negative component of the cortical evoked potential, suggesting that corticomedullary pathways contributed to the generation of these spikes. The multiple EPSPs that were seen between these two groups of spikes did not lead to firing because they occurred on a steady hyperpolarizing envelope, which was probably mediated by the firing of local inhibitory neurones (Canedo et al. 2000).
The TC neurones played a critical role in the modulation of cortical responsiveness to peripheral stimulation during SO: the latency of EPSPs in VPL–TC neurones was around 7.6 ms (Table 1) and the latency of firing of the first spike in the same neurones ranged from 8.2 to 15.8 ms (Fig. 5). Then shortest latencies were found when TC neurones were depolarized and prethalamic volleys brought the membrane potential to the firing threshold; longer latencies occurred when TC neurones were hyperpolarized and the first 3–5 EPSPs of the barrage triggered an LTS, which was covered with fast spikes. The single EPSPs that were elicited by ML stimulation were unable to trigger spikes at voltages hyperpolarized below –60 mV. Since the nucleus cuneatus neurones fired 1–2 spikes in response to peripheral stimuli and these responses were of quite similar latency, the expected response of TC neurones would consist of 1–2 EPSPs. In rats, most excitatory interactions in cuneate–VPL neuronal pairs involved intervals of 1–7 ms between successive cuneate and thalamic discharges (Alloway et al. 1994). Given a larger length of the dorsal column–lemniscal pathway in cats, the variability of latency of EPSPs in VPL neurones could be larger. Our data showing a variable latency of antidromic responses of cuneate neurones that followed ML stimulation suggest that different conductance velocity of axons originating in the cuneate nucleus could contribute to the origin of barrages of EPSPs in VPL neurones. Altogether, the origin of EPSP barrages in VPL neurones that followed MN stimulation was mediated by a convergence of fibres with different conductance velocities originating in the dorsal column nuclei, spike doublets of cuneate nucleus neurones and excitatory connections within the cuneate nucleus. It is unlikely that corticothalamic neurones contributed to the phasic excitation of VPL neurones because (a) the strength of cortico-RE connections is more than three-fold stronger than the strength of cortico-TC connections and thus cortico-TC excitation plays only a modulatory role (Golshani et al. 2001; Gentet & Ulrich, 2004), and (b) most cortical neurones did not fire spikes in response to MN stimuli during silent network states (Figs 1 and 2), but the responses of VPL neurones nevertheless remained strong during this network state. Also, the coticothalamic EPSPs display long-lasting rise times (Deschénes & Hu, 1990; Timofeev et al. 1996) contrasting the sharp rising EPSPs recorded in the present study (Fig. 5A).
Consistent with other studies on cats and raccoons (Yamamoto et al. 1990; Istvan & Zarzecki, 1994), we found that 40% of cortical neurones responded with the shortest latencies of EPSPs being 9.5–10.5 ms. The shortest latency of a cortical response to a TC cell's firing is 1.2–1.8 ms (Steriade et al. 1997; Swadlow et al. 2002). This indicates that, theoretically, the shortest latency of EPSPs in VPL neurones to a spike should be 8.3 ms. Our recordings taken from VPL neurones, revealed that the shortest latency of firing was 8.2 ms (Fig. 5).
Different neuronal processes mediated in a different way the generation of field potential responses to MN stimuli. During active network states, the firing of the first group of neurones contributed to the generation of the first depth-negative peak (Fig. 1B,) and the excitation of the second group of neurones was a factor generating the second component of the depth-negative peak (Fig. 1B2). The histogram of latency of neuronal responses demonstrates a bimodal distribution (Fig. 2B), which corresponds to the two depth-negative maximums of the evoked potential. An absence of inhibitory responses during the depth-positive EEG wave (Fig. 1B4) transforms the clear cut biphasic field potential response to a monophasic one (Figs 1B and 2A).
Thalamic and cortical gating during slow sleep oscillation
The TC network is composed of billions of synaptically connected neurones. The consistently integrated behaviour of such a network has been put forward as a necessary condition for conscious perception phenomena (Edelman & Tononi, 2000; Steriade, 2001). In particular, a minimal time interval of stable TC activity is required to achieve conscious perceptions (Libet et al. 1967), supported by continuous cortico-cortical and thalamo-cortical coherent activities (Singer & Gray, 1995; Varela et al. 2001). Incoming EPSP bursts to TC neurones at depolarized voltages triggered trains of spikes and, at hyperpolarized voltages, similar synaptic volleys elicited a high-frequency spike burst (Figs 3 and 5). The total number of spikes was similar under both voltage conditions, but the timing of spike occurrence was different. We would like to emphasize that outright blockage of cortical responses at the thalamic level during silent phases of activity was achieved only when prethalamic ML stimulation was used. This outcome was identical to our previous study of motor systems in which the brachium conjuctivum–VL thalamic nucleus–motocortex chain was investigated (Timofeev et al. 1996).
At the neocortical level, responses to peripheral stimuli varied in latency, amplitude, shape and ability to fire as a function of the phase of SO (Figs 1 and 2). The spike timing is critical in cortical information processing (Foffani et al. 2004). The primary source of the variability in cortical response latency was variability in the latency of TC neurones' firing, which was longer and more variable during the silent phases of slow oscillation, and shorter and less variable during active phases of slow oscillation. In the neocortex, the firing probability for neurones with the shortest latency of response was reduced by 50% during silent phases of SO, though the amplitude of synaptic responses was still high due to a stronger driving force from more hyperpolarized levels of membrane potential and higher input resistance (Fig. 2 cells 1 and 2). This last finding could explain the higher N20 amplitude during the putative silent state observed in sleeping humans (Massimini et al. 2003). Finally, the amplitude of EPSPs activated by oligosynaptic intracortical chains was reduced and those neurones never fired during silent phases of slow oscillation.
We conclude that: (a) sensory inputs after peripheral stimulation pass the thalamic gate during all phases of sleep slow oscillations because of the spike barrages generated in the lemniscal pathway; (b) however, due to spontaneous fluctuations in the membrane potential of both thalamic and cortical neurones, cortical responses are highly variable; (c) specifically, the reduced probability of spikes being transmitted further within cortical circuits during the silent state of the slow oscillation may contribute to impairing sensory processing during sleep.
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) and medial lemniscus stimuli (•). Note the presence of multiple synaptic events and high-frequency spike trains elicited by forelimb stimulation and a single EPSP leading to spikes elicited by medial lemniscus stimulation.
