Chapter III 3. Focal generation of paroxysmal fast runs during electrographic seizures

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Sofiane Boucetta, Sylvain Chauvette, Maxim Bazhenov and Igor Timofeev. Submitted to the Journal of Neurophysiology (2005).

Les crises de type Lennox-Gastaut générées par le cortex sont associées à des décharges de type pointe-onde et polypointes-onde de 2-3 Hz et des ondes paroxysmales rapides ( fast runs) de 8-15 Hz. Dans cette étude, en utilisant des enregistrements intracellulaires et des potentiels de champ à multisites in vivo , et des modèles computationels, nous avons analysé les patrons de synchronisation des ondes paroxysmales rapides durant des crises spontanées enregistrées chez des chats anesthésiés par ketamine-xylazine. Dans différentes expériences, les électrodes d’enregistrement ont été placées soit à courtes distances (< 1 mm) ou à longues distances (jusqu’à 12 mm). Dans la majorité des cas, le commencement et la fin des ondes rapides se produisent presque simultanément dans les différents sites d’enregistrement, ce qui suggère un contrôle extracortical de ces évènements. L’amplitude et la durée des ondes rapides peuvent varier significativement jusqu’à l’abolition totale enregistrée par certaines électrodes. Pendant les ondes rapides, les patrons de synchronisation enregistrés au niveau des différentes électrodes ont été les suivants : (i) synchrone, en phase, (ii) synchrone, avec décalage en phase, (iii) changement, répété en phase/ des transitions avec décalage en phase et (iv) non-synchrone, soit des fréquences légèrement différentes dans les différents sites d’enregistrement, soit l’absence de l’activité oscillatoire dans un des sites d’enregistrement; Tous ces patrons peuvent être enregistrés dans la même paire d’électrode durant différentes crises. Les neurones en bouffées de potentiels d’action (IB) déchargent plus de potentiels d’action par cycle que d’autres neurones, ce qui suggère leur rôle principal dans la génération des ondes paroxysmiques rapides. Une fois commencées, les ondes paroxysmiques rapides sont générées localement avec peu, s’il y en a, de communication entre les foyers corticaux voisins.

A cortically generated Lennox-Gastaut type seizure is associated with spike-wave/polyspike-wave discharges at 2-3 Hz and fast runs at 7-16 Hz. In this study, using multisite field potential and intracellular recordings in vivo and computational models, we analyzed the synchronization patterns of paroxysmal fast runs during spontaneous seizures recorded from cats anesthetized with ketamine-xylazine. In different experiments, the recording electrodes were located either at short distances (<1mm) or at longer distances (up to 12 mm). In the majority of cases, the onset and the offset of fast runs occurred almost simultaneously in different recording sites suggesting extracortical control of these events. The amplitude and duration of fast runs could vary significantly, up to full abolition recorded with some electrodes. Within the fast runs, the patterns of synchronization recorded in different electrodes were as following: (i) synchronous, in phase, (ii) synchronous, with phase shift, (iii) patchy, repeated in phase/phase shift transitions and (iv) non-synchronous, slightly different frequencies in different recording sites or absence of oscillatory activity in one of the recording sites; the synchronous patterns (in phase or with phase shifts) were most common. All these patterns could be recorded in the same pair of electrodes during different seizures. Intrinsically-bursting (IB) neurons fired more spikes per cycle than any other neurons suggesting their leading role in the fast run generation. Once started, the fast runs are generated locally with little, if any, communication between neighboring cortical foci.

Sleep related epilepsy is characterized by seizures developing during periods of slow-wave sleep. Studies on experimental animals point to the intracortical generation of some types of such seizures (Steriade and Contreras 1998). These seizures are characterized by spike-wave (SW) or spike-wave/polyspike-wave (SW/PSW) complexes of 1.5-3 Hz, intermingled with episodes of fast runs at ~7-16 Hz (Neckelmann et al. 1998; Steriade and Contreras 1998; Steriade et al. 1998b; Timofeev et al. 1998). The evolvement of these seizures from the cortically generated slow oscillation, might be shaped by the thalamus (Hughes et al. 2002; Steriade and Contreras 1995; Steriade and Timofeev 2001; Steriade et al. 1998b). The electrographical pattern of these seizures as well as their occurrence during slow-wave sleep resemble the seizures accompanying Lennox-Gastaut syndrome of humans (Halasz 1991; Kotagal 1995; Niedermeyer 1999a, b). The prolonged (more than 2-3 sec) periods of runs of fast EEG spikes were usually associated with tonic components of seizures accompanying Lennox-Gastaut syndrome (Niedermeyer 1999a). The detailed pattern of synchronization as well as the cellular basis of fast runs is not well understood. Previous single-case human study have shown that an increase in the synchronization stops the runs of fast EEG spikes (Ferri et al. 2004). Animal studies showed that (a) the thalamus is not involved in the generation of fast runs (Timofeev et al. 1998), (b) the inhibitory activities in neocortex are impaired during the fast runs (Timofeev et al. 2002), and (c) these changes occur in parallel with decrease in the neuronal input resistance (Matsumoto et al. 1969; Neckelmann et al. 2000; Timofeev et al. 2002) and decrease in the concentration of extracellular Ca2+ (Amzica et al. 2002; Heinemann et al. 1977).

During normal (not paroxysmal) brain activities, both the excitatory and inhibitory synaptic interactions provide mechanisms for the synchronization among neighboring recording sites. In a condition of low extracellular Ca2+ concentration, impaired inhibitory activities, and low input resistance, the synchronization should be impaired. Thus, we hypothesize that paroxysmal runs of fast EEG spikes are generated locally in a quasi independent manner. The present study supports this hypothesis and provides data on the neuronal basis and patterns of synchronization during fast runs generated within spontaneous electrographic seizures.

Experiments were conducted on adult cats anesthetized with ketamine-xylazine anesthesia (10-15 and 2-3 mg/kg i.m., respectively). The animals were paralyzed with gallamine triethiodide (20 mg/kg) after the EEG showed typical signs of deep general anesthesia, essentially consisting of a slow oscillation (0.5-1 Hz). Supplementary doses of the same anesthetics (5 and 1 mg/kg) or simply ketamine (5 mg/kg) were administered at the slightest changes toward the diminished amplitudes of slow waves. The cats were ventilated artificially with the control of end-tidal CO2 at 3.5-3.7%. The body temperature was maintained at 37-38oC and the heart rate was ~90-100 beats/min. For intracellular recordings, the stability was ensured by the drainage of cisterna magna, hip suspension, bilateral pneumothorax, and by filling the hole made for recordings with a solution of 4% agar. At the end of experiments, the cats were given a lethal dose of pentobarbital (50 mg/kg i.v.). All experimental procedures were performed according to national guidelines and were approved by the committee for animal care of Laval University.

Single, dual, triple or quadruple intracellular recordings from suprasylvian association areas 5 and 7 were performed using glass micropipettes filled with a solution of 3 M potassium-acetate (KAc). A high-impedance amplifier with active bridge circuitry was used to record the membrane potential (Vm) and inject current into the neurons. Field potentials were recorded in the vicinity of impaled neurons and also from more distant sites, using bipolar coaxial electrodes, with the ring (pial surface) and the tip (cortical depth) separated by 0.8-1 mm. In 12 cats, arrays of 7 or 8 electrodes, ~1.5 mm apart, were inserted along the suprasylvian gyrus. 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.

Two cortical models were simulated: 1) a circuit with coupled pyramidal (PY) cell and inhibitory interneuron (IN); 2) a one-dimensional chain of 100 PY neurons and 25 INs. In the network model, the connection fan out was ±5 neurons for AMPA-mediated PY-PY synapses, ±1 neuron for AMPA-mediated PY-IN synapses, ±5 neurons for GABAA-mediated IN-PY synapses. All AMPA- and GABAA-mediated synapses were modeled by first-order activation schemes (Destexhe et al. 1994), and the expressions for the kinetics are given elsewhere (Bazhenov et al. 1998). A simple model of synaptic plasticity (USE=7%, τ=700 msec) was used to describe depression of synaptic connections (Abbott et al. 1997; Galarreta and Hestrin 1998; Timofeev et al. 2000; Tsodyks and Markram 1997). A maximal synaptic conductance was multiplied to depression variable, D ≤ 1, representing the amount of available ``synaptic resources'': D = 1 - (1 - Di(1-U)) exp(-(t-ti)/τ), where U=0.07 is the fraction of resources used per action potential, τ = 700 ms the time constant of recovery of the synaptic resources, Di the value of D immediately before the ith event, and (t-ti) the time after ith event.

Each PY and IN cell was modeled by two-compartments that included fast Na+ channels, INa, and a persistent sodium current, INa(p), (Alzheimer et al. 1993; Kay et al. 1998) in the axo-somatic and dendritic compartments (Mainen and Sejnowski 1996). A slow voltage-dependent non-inactivating K+ current, IKm, a slow Ca2+ dependent K+ current, IK(Ca), a high-threshold Ca2+ current, ICa, hyperpolarization-activated depolarizing current Ih were included in the dendritic compartment. A fast delayed rectifier potassium K+ current, IK, was present in the axo-somatic compartment. The expressions for the voltage- and Ca2+-dependent transition rates for all other currents are given in (Bazhenov et al. 2004; Timofeev et al. 2000). Reversal potentials for all K+-mediated currents were calculated using Nernst equation. The extracellular K+ concentration was continuously updated based on K+ currents, K+ pumps, K+ buffering simulating the glial K+ uptake system (Bazhenov et al. 2004). The glial buffering was modeled by first order kinetics (Kager et al. 2000). The firing properties of this two compartment model depend on the coupling conductance between compartments (g=1/R, where R is resistance between comartments) and the ratio of dendritic area to axo-somatic area. The ratio, r, controls the firing patterns in the model (Mainen and Sejnowski 1996). We used a model of a regular-spiking neuron for PY cells (r = 165) and a model of a fast spiking neuron for IN cells (r = 50). The firing patterns of these cell types in response to DC pulses of different amplitude are shown in (Rulkov et al., 2004; Fig. 3B).

We performed various experiments to study different aspects of thalamocortical physiology. During these experiments, some of the cats displayed electrographic seizures. In these animals we stopped the initially thought experiment and changed experimental approach to address hypothesis described in the introduction the present study. We recorded electrophysiological activities from 87 cats initially anesthetized with ketamine-xylazine. As a mean, supplementary doses of anesthesia were administrated every two hours. In 41 cats, an initial anesthesia was followed by additional doses of ketamine. Similar to other studies (Steriade and Contreras 1995), in these experiments ~30 % of cats (12 cats out of 41) developed spontaneous paroxysmal discharges. Adding ketamine-xylazine as supplementary anesthetic resulted in the generation of electrographic seizures in ~75% of cats (35 out of 46). Usually, the seizures started after administration of the third-fourth supplementary dose. These seizures, consisting of SW complexes at 2–4 Hz or SW/PSW complexes associated with fast runs at 7–16 Hz, were developed from sleep-like slow rhythm. In total, we analyzed 224 electrographic seizures recorded with field potential electrodes. In parallel with EEG recording, we recorded intracellular activities of 157 neurons. This includes 8 simultaneous quadruple, 15 triple and 22 dual records. In 11 neurons, we recorded more than 20 seizures. An example of typical seizure is shown in Fig. 3.1 A in which the first 5 sec show a normal slow oscillation. The beginning of electrographic seizure was associated with ampler EEG waves and ampler intracellularly recorded depolarizing potentials repeated at frequencies 1.0-3.0 Hz. In some of the neurons the enhanced depolarizing potentials were associated with an increased firing (as in neuron 1 and neuron 4 in Fig. 3.1). After several cycles of spike-wave/polyspike-wave discharges the activity switched to runs of paroxysmal spikes (7-16 Hz) during which the long-lasting depth-positive EEG waves and associated intracellular hyperpolarizing potentials were absent. The fast runs appeared as a prolongation of polyspike-wave complexes. In this study, the polyspike discharges exceeding 1 sec were considered as runs of fast EEG spikes. The total duration of seizures was between 10 and 120 s (37.2±22.2 s, mean±SD, Fig. 3.1 C). We analyzed 252 periods of fast runs, which lasted between 1 and 30 s (4.9±5.7 s, Fig. 3.1 C). Out of these, 174 periods lasted for less than 5 sec. Each electrographic seizure recorded in these experimental conditions could have several periods of fast runs (1-8, mean 2.4±1.4, Fig. 3.1 C) and spike-wave complexes. Below, we will focus on the generation of paroxysmal runs of fast spikes.

Using multisite recordings, we evaluated the patterns of synchronization between the field potentials and intracellular activities during fast runs. Within the fast runs, the patterns of synchronization recorded with different electrodes were as following: (a) synchronous, in phase, (b) synchronous, with phase shift (one recording lead preceded or followed the activity in reference electrode), (c) "patchy", repeated in phase/phase shift transitions, and (d) non-synchronous, different frequencies in different recording sites or absence of rhythmic activities at one of the recording sites (see Fig. 3.4C). All these patterns could be recorded in the same pair of electrodes during different seizures. Some of these patterns are illustrated in Fig. 1 where the field potential and quadruple intracellular recordings were used. In this experiment, the electrodes were located in the suprasylvian gyrus: the most anterior intracellular electrode (Intra-cell 1) was located in the anterior part of area 5, the other electrodes were equally spaced with a distance between neighboring electrodes about 4 mm, and the most posterior electrode (Intra-cell 4) was located at the most posterior part of area 21. The seizure contained three periods of fast runs (2 of them are shown at a higher time resolution in the Fig. 3.1A). To estimate the patterns of synchrony we used the maximum of depth negativity of field potential as the reference time (see vertical black lines in the expended parts of Fig. 3.1 A). During the first period of fast runs the maximal depolarization of neurons 1, 2, and 3 preceded the maximal field potential depth negativity and the maximal depolarization of neuron 4 followed the field potential (see black lines in Fig. 3.1 A and B). The onset of each oscillatory cycle occurred first at the electrode Intra-cell 2 (see blue dotted lines in Fig. 3.1) during all the cycles of this period of fast runs (Fig. 3.1 B). During the second period of fast runs the most rostrally located neuron (Intra-cell 1) was always the first in the generation of each oscillatory cycle. The onset of depolarization in the neuron 2, estimated as 10% of amplitude between the most hyperpolarized and the most depolarized (spikes excluded) values of the oscillatory cycle, occurred coincidentally with the spike in the neuron 1; the onset of depolarization in the neuron 3 was delayed and the delay fluctuated in 20-30 milliseconds range. The oscillatory activity in the neuron 4 was dumped and the neuron 4 revealed a patchy pattern of activity: there was a phase shifts during each several cycles. Generally, the frequency of activity during the same period of relatively short (<5 sec) fast runs remained similar in different recording sites, but the phase-shifts were very variable during different epochs of the fast runs. Thus, the multisite intracellular and field potential recordings revealed that runs of fast spikes behave as quasi-independent oscillators.

To characterize the patterns of synchronization during the fast runs we performed cross-correlation analysis. As expected, during the spike-wave/polyspike-wave complexes, the cross-correlation between intracellular activities and EEG was generally negative since active periods were characterized by the neuronal depolarization and depth-negative EEG waves, and during depth-positive EEG waves the neurons were hyperpolarized (Fig. 3.2). The cross-correlation between pairs of neurons was positive (not shown). The fast runs were characterized by variable patterns of synchronization. In the majority of cases (70 %), the delay between the two recording leads was stable throughout the period of fast runs. As shown in the example in Fig. 3.2 B, the Intra-cell 1 preceded the EEG by about 35 ms during the first period of fast runs. During the second period of fast runs, the intracellular activities of this neuron preceded the EEG during initial period of fast oscillations and it was synchronous in phase oscillation during the second period of fast runs. The second neuron (Intra-cell 2) and EEG had stable relation during fast runs and the maximum of neuronal excitation preceded the EEG with delay of about 10 ms during both periods of fast runs (Fig. 3.2 B). The relation between the two neurons occurred in a similar way. During the spike-wave/polyspike-wave complexes both neurons oscillated in phase, with very little variability in the phase shifts. During the first period of fast runs the phase-shift was high and the neurons oscillated with the phase-shift approaching 180o. During the second period of fast runs, the synchronization had a patchy pattern. At the beginning, the neuron 2 preceded the neuron 1 by about 50 ms, but during the second part of the same train of fast EEG spikes, the neuron 2 preceded the neuron 1 by only about 10 ms (not shown).

Although in the vast majority of cases the periods of fast runs occurred almost simultaneously in all recording leads along the suprasylvian gyrus, on some occasions, the pattern was different. The Fig. 3.3 shows an example in which the seizure started from the fast run generated in the anterior part of suprasylvian cortex (EEG 7). The correlation between the anterior and posterior electrodes had small values (Fig. 3.3B, 3.1). Progressively, this activity involved more posterior regions of suprasylvian gyrus and the oscillation with frequency 11 Hz becomes dominant on all 7 EEG leads with a high value of correlation accompanied by a small propagation of activity in posterior-anterior direction (Fig. 3.3 B, 3.2). Another period of the fast runs, during the same seizure, started almost simultaneously in all EEG electrodes (frequency 8.5 Hz, see the middle expanded part in Fig. 3.3 A), and the correlation between all electrodes was high with a marked anterior-posterior propagation (Fig. 3.3 B, 3.3). At the end of the seizure the runs of fast spikes (9.5 Hz) were dominant in the posterior part of suprasylvian gyrus, while the anterior suprasylvian gyrus displayed frequency around 4 Hz, therefore the anterior-posterior correlation was low (Fig. 3.3 B, 3.4).

Thus, our multisite recordings demonstrated that in the majority of cases the onset and the end of runs of fast spikes occurred almost simultaneously at different locations, but either the frequency of oscillation or the phase shifts between different locations were dissimilar in each period of the fast runs, and even the dominating pattern of activity could switch during the same epoch of fast runs.

As we have shown above with distant multisite recordings, the phase shift during the same run of fast spikes could be very variable when it is recorded with two or more electrodes located at distances larger than several millimeters. In long-lasting recordings (longer than 10 min), we analyzed the patterns of synchronization during fast runs between neurons and field potential recorded at about 1 mm apart (Fig. 3.4). We found that each pair of records (cell-EEG) had preferred phase relationship, but on some occasions, it revealed significant variations in the pattern of synchronization. Similar to distant recordings, in the majority of cases, the paroxysmal oscillation was synchronous, but each period of fast runs had a particular phase shift (Fig. 3.1, cell 1, 2, 3, Fig. 3.2, cell 2 and Fig. 3.3 periods 2, 3). Even in close recordings the pattern of synchronization during fast runs within the same seizure could vary, and during one period of fast runs, the oscillation in two electrodes could be synchronous (0 time lag), while during another period the oscillation in two electrodes could be of different frequencies (Fig. 3.4). As an extreme case, we recorded large amplitude EEG fast runs and absence of such or similar oscillation in a closely located neuron (n=5), while the previous or following periods of fast runs showed synchronous oscillations in both electrodes (not shown). During 218 periods of fast runs we analyzed the pattern of synchronization between activities recorded with intracellular and nearby field potential electrodes (Fig. 3.4, C). We found that in only 20 % of cases the oscillation was in phase (the neuron fired during ascending phase or maximum of the EEG depth-negative wave). However, the synchronous patterns with or without different delays were recorded in 70% of cases. In 22 % of cases the activity was not synchronous: either two sites revealed oscillation with different frequencies or record showed arrhythmic activity (Fig. 3.4, C). In 8% of cases the activity was patchy; there were two or more changes in the pattern of synchronization (see examples in Fig. 1, intra-cell 4, second period and Fig. 3.2 Intra-cell 1, second period).

We systematically analyzed the spatial synchronization of fast runs within suprasylvialn gyrus of cats. For this analysis we have chosen only periods of fast runs that revealed consistent delays of coherent activity over 10 or more consecutive cycles with variability of crosscorrelation maximums not exceeding 2 milliseconds (Fig. 3.5). We analyzed separately pairs of EEG recordings and pairs of intracellular recordings obtained at different distances. In most of cases, the activity in the more anterior electrodes preceded the activity in the more posterior electrodes. We found that the fast run activity could propagate with maximal velocity reaching 10 m/s; however, the mean velocity was estimated as 2.13±0.38 m/s from pairs of EEG electrodes and 2.23±0.60 m/s from pairs of cells, indicating the presence of similar delays detected with two different methods of recordings. During consecutive fast runs the same pair of either EEG or intracellular recordings could reveal propagation from -5 m/s (posterior to anterior) to 8 m/s (anterior to posterior). Thus, each period of fast runs was characterized by unique propagation velocity and direction of propagation. However, the recordings from closely located neurons (<0.2 mm lateral distance) revealed that the coherent oscillation between two neurons could be delayed by up to 20 ms. Thus, similar to studies in desinhibited slices, if propagation of activity takes place its varied manifold (Chervin et al. 1988).

The frequency of oscillation during the prolonged periods (>10 sec) of fast runs usually underwent a progressive decrease. To estimate the duration of a cycle during different periods of the same fast run, we obtained autocorrelations for consecutive periods lasting 5 sec (Fig. 3.6 A). In the extreme cases, the duration of oscillatory periods decreased almost twice, from 65 ms to 100 ms (Fig. 3.6 B).

In a sample of 115 periods of fast runs that lasted 5 sec or longer we analyzed the duration of oscillatory cycles (Fig.3.6). The frequency of oscillations composing fast runs decreased as the fast run progressed (Fig. 3.6 B). At the beginning of the prolonged fast runs the mean frequency of oscillation was 13.8±2.0 Hz and at the end it was slightly but significantly 11.3±2.1 Hz (paired t-test, p=0.007). The decrease in frequency by cycle was 0.036±0.016 Hz. Since the frequency decrease in a range of several cycles was low, we computed autocorrelations (Fig. 3.6 C) to evaluate (a) mean frequency of oscillation, measured by the time of the second peak occurrence, and (b) rhythm coefficient, measured as the amplitude of the second peak of autocorrelation, being 1 when all cycles had identical frequency and 0 when all cycles had different frequency. The duration of cycles varied from 62 ms to 124 ms (16 Hz to 8 Hz, Fig. 3.6, E 1) with the mean frequency 12.0±1.4 Hz. The autocorrelation obtained from the field potential recordings during fast runs was very high, and for 75% of cases it was above 0.8, suggesting a high periodicity of cycles (Fig. 3.6 E 2). The intracellular traces revealed slightly lower values of correlation (Fig. 3.6 D 2, E 2). This was likely because the variability of cycle duration for one neuron was larger than for field potential recordings; field potentials reflect the averaged activity of a set of neurons and glial cells and thus, they are much less sensitive to the variability in individual cycles of individual cells. As we reported above, the cycle duration in both the intracellular and the EEG recordings was usually similar (Fig. 3.6 D 1, 2). However, during some period of fast runs the difference could be as large as a double of the frequency.

All neurons that were recorded during both electrographic seizures and periods outside seizures (n=102), were classified by electrophysiological criteria as regular-spiking, fast-rhythmic-bursting, intrinsically-bursting (IB) and fast spiking (Connors and Gutnick 1990; Gray and McCormick 1996; Steriade et al. 1998a). The electrophysiological classification of neurons was performed during periods of normal (not paroxysmal) activity. In this set of experiments we identified 70.6 % of neurons as regular-spiking, 11.8 % as fast-rhythmic-bursting, 11.8 % as IB and the remaining 6 neurons were fast spiking. In each of these neurons we calculated the mean number of spikes per cycle generated during fast runs (Fig. 3.8). At least 100 cycles were counted to obtain a mean number of spikes generated by each neuron. Since in many instances the amplitude of spikes was significantly reduced as compared to spikes occurring outside seizures, we counted only spikes that were at least a half of amplitude of a mean spike recorded during normal activities. For example, in the displayed period in figure 6, the cell 1 had 3 spikes during each cycle, the cell 2 had 1 spike during 6 cycles and 2 spikes during one cycle and cell 3 had 1 spike during each cycle. Some neurons revealed a large variability in the number of spikes per cycle, which could fluctuate from 1 to 6 (Fig. 3.1, Intra – cell 1). Statistically, we found however that the number of spikes per cycle generated by IB and fast-rhythmic-bursting neurons was significantly higher (Tukey-Kramer HSD [honestly significant difference] test) than the number of spikes generated by other types of neurons (Fig. 3.8).

Since, in all recorded combinations, we observed either repeatable phase shifts or absence of synchrony, we hypothesized that fast paroxysmal runs are generated locally. We used a highly simplified network model to demonstrate how extremely variable phase relations can indeed develop between synaptically coupled, oscillating neurons.

We have previously shown that increases in [K+]o could, in principle, lead to slow 2-3 Hz oscillations and fast runs (Bazhenov et al. 2004). Here we explore phase relations between neurons in different oscillatory states. We start from single neuron analysis and then we show effect of synaptic interaction on network synchronization. An external stimulus (DC pulse of 10 sec duration) applied to a single cortical pyramidal (PY) neuron induced a high-frequency spiking in this cell (Fig. 3.9). A flow of K+ ions to the extracellular milieu overpowered effects of K+ pump and glial buffering, and led to [K+]o increase (see insert in Fig. 3.9 B). After stimulus termination, a neuron sustained periodic bursting in 2-4 Hz frequency range. For each cycle of oscillations the slow membrane potential depolarization between bursts (due to combined effects of Ih, IK(Ca) deactivation, and high [K+]o that depolarized reversal potentials of all K+ currents) activated the persistent sodium current and led to the new burst onset (see details in (Bazhenov et al. 2004)). Each burst started with a few spikes followed by spike inactivation and a depolarizing plateau that lasted 50-100 msec (see insert in Fig. 3.9 A). Progressive increase of the intracellular Ca2+ concentration during depolarized state increased activation of the Ca2+ dependent K+ current and the neuron switched back to the hyperpolarized state. Since K+ reversal potentials remained below -80 mV even when [K+]o elevation was maximal during slow bursting, the neuron stayed hyperpolarized below normal resting potential (-65 mV) between bursts. Deactivation of IK(Ca) determined the length of the hyperpolarized state and ultimately the frequency of slow bursting. During slow bursting, [K+]o gradually decreased and 5-6 sec later, bursting was replaced by faster oscillations at 10-15 Hz range. Decrease of [K+]o restored "normal" hyperpolarized K+ reversal potentials. This increased hyperpolarizing "force", so the neuron did not stay "locked" in depolarized state after burst onset, but quickly repolarized back to the resting potential. Such brief depolarization led to only minimal activation of the Ca2+ dependent K+ current, so the next spike (or short burst of spikes) occurred with much smaller delay. Therefore, the frequency of oscillations increased and, on average, the neuron stayed at more depolarized level of membrane potential than in slow bursting mode. Fast oscillations lasted 20-25 sec and eventually terminated when [K+]o decreased below a level, which was necessary to maintain spiking. Thus the change of the [K+]o can account for transitions between slow and fast paroxysmal oscillations and silent state in the cortical neuron model.

To study effect of [K+]o increase on the circuit dynamics, we included inhibitory interneuron (IN) receiving excitatory (AMPA-type) connection from PY cell and sending back inhibitory (GABAA-type) synapse. Strength of PY-IN synapse was adjusted so that IN remained silent during tonic firing of PY neuron (Fig. 3.9 B). When PY neuron started to burst, the excitatory drive from PY to IN cell was sufficient to trigger periodic IN spiking. In agreement with in vivo data (Timofeev et al. 2002), IN spiking stopped when PY neuron switched from slow bursting to the tonic firing. Two main factors contributed to the absence of IN spiking during fast runs. First, steady-state depression of excitatory coupling between PY and IN neurons was stronger during fast runs; during slow bursting short-term depression was significantly reduced by the end of hyperpolarized (interburst) state. Second, during slow bursting a first few spikes at the burst onset occurred at highest frequency, therefore promoting EPSPs summation.

To study synchrony of population oscillations during different oscillatory regimes, we simulated network model of 100 PY neurons and 25 INs. Upon DC stimulus termination, all the neurons displayed slow bursting followed by fast spiking. Because of random variability of the model parameters across neurons and different initial conditions, the neurons fired independently when synaptic coupling was turned off (Fig. 3.10 A, top). Slow bursting lasted less than 4 sec. When excitatory/inhibitory coupling between neurons was included, the slow paroxysmal bursting lasted much longer (~10 sec) and became synchronized across neurons (Fig. 3.10 A, bottom). Fig. 3.10 B shows cross-correlation between neighbor PY cells in the network; during slow paroxysmal oscillations these neurons fired with minimal phase delays. In agreement with single neuron study, progressive change of [K+]o triggered transition from slow to fast oscillations. In most cases neighbor neurons displayed this transition nearly simultaneously; however we found a few large clusters with very different transition times (compare neurons #1-50 and #51-100 in Fig. 3.10 A, bottom). Including long-range connections between PY neurons would likely increase the global synchrony of transitions between epochs of slow and fast oscillations. To test this hypothesis we included random long-range connections between PY neurons and varied probability of long-range coupling P . When P > 0.02-0.03, transition from slow bursting to fast runs occurred almost simultaneously with less than 200 msec variability across all neurons in the network (not shown). Including long-range connections with such low probability did not produce, however, any systematic effect on phase relations between neurons during fast oscillations.

In contrast to the slow bursting mode, during fast runs the degree of synchrony between neurons (even in close proximity) was significantly reduced. Typically, neighbor neurons fired with a phase shift which was consistent for a few cycles of network oscillations thus suggesting local spike propagation. Different cell pairs displayed phase delays of different signs (propagation in different directions). Phase relations between neurons could change from in-phase to out-phase oscillations or vice versa either gradually (see, e.g., cross-correlation plot for PY35 and PY36 in Fig. 3.10 B) or suddenly (see, e.g., cross-correlation plot for PY35 and PY38 in Fig. 3.10 B). These modeling results suggest that synaptic coupling may explain synchrony of slow bursting oscillations. During fast runs, however, local synaptic excitation influences phase relations between neighbor neurons but is not sufficient to arrange steady network synchronization. Random long-range connections increase synchrony of transitions between slow bursting and fast runs but can not enhance synchrony of fast oscillations on cycle-to-cycle basis.

In the present study, we analyzed the spatio-temporal properties of runs of fast spikes recorded during spontaneous seizures in cats anesthetized with ketamine-xylazine and in computational models. We found that (a) the runs of fast EEG spikes with frequency 7-16 Hz accompanied most of neocortical seizures; (b) the patterns of synchrony during fast runs were as following (i) synchronous, in phase, (ii) synchronous, with phase shift, (iii) patchy, repeated in phase/phase shift transitions and (iv) non-synchronous, slightly different frequencies in different recording sites or absence of oscillatory activity in one of the recording sites; the synchronous patterns were most common; (c) the runs of fast spikes appeared as quasi-independent oscillators even in neighboring cortical locations suggesting their focal origin; (d) for synchronous fast runs there was a tendency for propagation in anterior-posterior direction with velocity 2.1-2.2 m/s; (e) the membrane potential during fast runs was by ~9 mV less depolarized as compared to the depolarizing components of spike-wave complexes.

Most of the cats anesthetized with ketamine-xylazine anesthesia, followed by supplementary doses of ketamine-xylazine developed paroxysmal activities consisting of spike-wave discharges at 1-3 Hz and runs of fast spikes at 7-16 Hz. These seizures are generated neocortically as (a) they could be obtained in athalamic cats (Steriade and Contreras 1998), (b) small neocortical slabs (Timofeev et al. 1998) or (c) in the undercut cortex (Topolnik et al. 2003), and (d) most of thalamocortical neurons do not fire during this type of seizures (Pinault et al. 1998; Steriade and Contreras 1995; Timofeev et al. 1998). The cause of these seizures is unclear. A combination of two major factors could account for the development of those paroxysmal activities.

(a) Slow oscillations. As previously suggested, spontaneously occurring, compound seizures consisting of spike-wave complexes at 2–4 Hz and fast runs at 7–16 Hz, developed without discontinuity from the slow (mainly 0.5–0.9 Hz), cortically generated oscillation (Steriade and Contreras 1995; Steriade et al. 1998b). Long-lasting periods of disfacilitation accompanying sleep oscillations could likely activate a large number of intrinsic and synaptic factors leading to the development of seizures (see (Timofeev and Steriade 2004) for the detailed discussion). However, the occurrence of seizures during sleep is far lower than the occurrence of seizures in cats anesthetized with ketamine-xylazine.

(b) Effects of ketamine-xylazine. Ketamine at anesthetic doses blocks NMDA dependent synaptic events (MacDonald et al. 1991). An activation of NMDA receptors contributes, but is not essential in the generation of paroxysmal discharges (Barkai et al. 1994; Traub et al. 1996). Thus, the NMDA-dependent Ca2+ contribution in the generation of paroxysmal discharges in our experiments was impaired, which should decrease the propensity to the seizure generation and not to be a seizure promoting factor. Additional blockage of N-cholinoreceptors by ketamine (Rudolph and Antkowiak 2004) might have some impact, because, the blockage of these receptors should remove their depolarizing action on thalamocortical neurons (McCormick 1992), low-threshold spike cortical interneurons (Xiang et al. 1998), and should reduce the efficacy of excitatory synapses formed by thalamocortical neurons on cortical neurons (Gil et al. 1997).

Xylazine is the agonist of alpha-2 adrenoreceptors, heavily present in the neocortex (Nicholas et al. 1993), likely, on presynaptic terminals (Hedler et al. 1981). Administration of low doses of xylazine favors the development of seizures (Joy et al. 1983). The role of thalamus in the effects of xylazine on those seizures remains unclear. It has been proposed that administration of xylazine promotes oscillatory behavior of thalamocortical system via agonistic action on alpha-2 receptors (Buzsaki et al. 1991), but clonidine, a potent agonist of alpha-2 receptors inhibited action potential generation of thalamocortical neurons in a near dose-dependent manner (Funke et al. 1993), and finally norepinephrine application to slices, depolarize thalamocortical neurons, suppressed their burst firing, and promoted the occurrence of single spike activity (McCormick and Prince 1988). Thus, we suggest that xylazine actions in neocortex could be essential factor in the generation of paroxysmal activity induced by ketamine-xylazine anesthesia.

The amplitude of field potentials during spike-wave components of electrographic seizures is higher than the amplitude of slow oscillation (Figs. 3.2 and 3.4). This suggests that the focal synchronization during seizures is higher than during the normal brain activities. The long-range synchronization during seizures is reduced (Neckelmann et al. 1998). Our modeling data demonstrate that seizure-like activity could be obtained in a single cortical neuron (Fig. 3.9), if some extracellular conditions (primarily increased [K]o) are present. These results question the role of synaptic interactions in the generation of seizures. Multiple data suggest that synaptic activities mediated by chemical synapses during seizures play a little role. (a) Paroxysmal activities are associated with decreased [Ca2+]o (Amzica et al. 2002; Heinemann et al. 1977), and as a consequence, the effectiveness of synaptic strength decreases, but the intrinsic neuronal excitability increases (Hille 2001). (b) The use of low or even 0 mM [Ca2+]o in vitro results in the development of epileptiform discharges (Bikson et al. 1999; Leschinger et al. 1993; Pan and Stringer 1997). (c) The synaptic responsiveness during electrographic seizures in vivo decreases (Cisse et al. 2004; Steriade and Amzica 1999), (d) the long-range synchronization during seizures, particularly during fast runs, is low or absent (Fig. 3.4), and finally (e) the neuronal firing dramatically reduces toward the end of the seizure, while intracellular and field potential activities are ampler as compared to the beginning of the seizure (Bazhenov et al. 2004; Timofeev and Steriade 2004). In a condition of reduced efficiency of chemical synaptic transmission, the focal neuronal synchronization could be achieved either via electrical coupling between different groups of neurons (Galarreta and Hestrin 1999; Gibson et al. 1999; Perez Velazquez and Carlen 2000; Schmitz et al. 2001), glial cells (Amzica et al. 2002), or via ephaptic interactions (Grenier et al. 2003; Taylor and Dudek 1982, 1984a, b). These mechanisms have very reduced, if any, efficacy for the long-range synchronization. During fast runs, the activity of fast spiking interneurons is significantly reduced (Timofeev et al. 2002), and consequently the efficiency of synchronization via electrically coupled interneuronal network (Galarreta and Hestrin 1999; Gibson et al. 1999; Perez Velazquez and Carlen 2000) is diminished; the amplitude of field potentials is also reduced (Figs. 3.1-3.5) and as a result, its synchronizing efficiency is impaired. As consequence, the runs of fast EEG spikes are accompanied with a remarkable loss of synchrony.

Commonly, the onset and the end of fast runs occurred almost simultaneously at a large cortical distances (Figs. 3.1, 3.2). The model suggests that long-range excitatory connections between pyramidal cells may account for this synchrony if the density of long-range connections is high enough. Another possibility includes existence of some synchronizing input arriving to the different cortical loci and droving both the onset and the end of fast runs. The source of this input is unknown. Theoretically, the thalamocortical neurons from non-specific nuclei, which project to wide cortical areas, could provide such a synchronous drive. However, the majority of thalamocortical neurons are silent during cortically generated seizures (Pinault et al. 1998; Steriade and Contreras 1995; Timofeev et al. 1998) and that would limit the implementation of such a mechanism. The other possibility is that the activating pathways would direct the switch to and from fast runs. Indeed, the fast spiking neurons fire many spikes during EEG spike-wave complexes and become silent during the fast runs (Timofeev et al. 2002). One of the possibilities is that cholinergic activities would activate muscarinic receptors on fast-spiking interneurons (Xiang et al. 1998), which would stop their firing and switch spike-wave discharges to the fast runs. The model suggests that the global change of extracellular K+ concentration may play a role in transitions between fast and slow oscillatory modes. K+ diffusion in the extracellular space tends to even K+ concentration between remote foci, thus, the concentration change may serve as a global signal for state transitions. At the same time, K+ diffusion is too slow to synchronize spiking of individual neurons during fast runs.

Our finding that both IB and fast-rhythmic-bursting neurons fired more spikes during fast runs than the other types of neurons (Fig. 3.8), imply that both types of bursting neurons could generate the fast runs. The intrinsic tuning of intraburst frequency for fast-rhythmic-bursting neurons is in the range from 20 Hz to 60 Hz (Gray and McCormick 1996; Steriade et al. 1998a), thus their frequency is much higher than the frequency of paroxysmal fast runs (Fig. 3.8), and their leading role in the generation of fast runs is doubtful. The intraburst frequency of IB neurons in vivo during normal network activities is around 8 Hz (Nuñez et al. 1993) (see also Fig. 3.8), which is close to the frequency of fast runs. Seizure related changes in extracellular Ca2+ and K+ concentration (Heinemann et al. 1977) could slightly modulate the intraburst frequency of IB neurons and thus to cover all range of fast run frequencies. Thus, we suggest that cortical IB neurons could play a role of pacemakers during paroxysmal fast runs.

We conclude that the runs of fast EEG spikes, which accompany cortically generated seizures, are generated as quasi-independent oscillators, with very little synaptic communication within cortical network. Our data suggest that the onset and the end of fast runs have global (possibly extracortical) origin.

Figure 3.1

Figure 3.1: Synchronization of field potential and intracellular activities during paroxysmal fast runs.

A. Simultaneous depth-EEG and quadruple intracellular recordings. The electrodes were equally distributed from anterior to posterior parts of suprasylvian gyrus. The intra-cell 1 was in the anterior part of area 5 and the intra-cell 4 was in the posterior part of area 21. Encircled fragments are expanded below as indicated by arrows. B. Fifteen cycles during the first and the second fast run are color coded (dark brown: -70 mV, yellow: -50 mV). The maximum of field potential depth negativity is taken as zero time. Note that in both periods, the first maximum of depolarization in neuron 1 and 2 preceded the maximum in the field potential, the neuron 3 slightly preceded the field potential during the first shown period and followed the field during the second period and finally the neuron 4 followed the field with constant delay during the first period and showed "patchy" pattern during the second period, during which, some cycles had similar delays, the next group of cycles was either not involved in the activity or had another delay in respect to the field. C. Summary data showing histograms of distribution of seizures duration, individual fast run duration and the number of fast runs per seizure.

Figure 3.2

Figure 3.2: Dynamics of cross-correlation during paroxysmal fast runs.

A. Depth-EEG and dual intracellular recording during a seizure containing two periods of fast runs. B , the consecutive cross-correlations between EEG and neurons. The running correlogram was calculated as following: for a time interval 1.0 s the correlation function between two channels was calculated with correlation length ±0.2 s. Frame was then moved with a step 0.5 s, correlation calculated, color coded, plotted, and so on. Each of correlated periods is represented as single color strip in the bottom panels. Note that during spike-wave discharges the correlation between neuron and EEG was negative, as expected. During the first period of fast runs, the neuron 1 had reversed phase relations with EEG, while during the second period of fast runs the neuron revealed a patchy pattern. It oscillated several cycles in phase and another several cycles in counter phase; the neuron 2 oscillated in phase with the EEG during both spike-wave complexes and during both periods of fast runs.

Figure 3.3

Figure 3.3: Progressive involvement and variability of synchronous patterns during fast runs.

A. Seven depth-EEG recordings were obtained from suprasylvian gyrus with inter-electrode distance of 1.5 mm. The seizure started with the runs of fast spikes recorded with electrode 7 (most anterior), the other electrodes reveal a progressive involvement of surpasylvian gyrus in the fast run. After several spike-wave/polyspike-wave complexes, the next period of fast runs was accompanied with perfect synchrony over suprasylvian gyrus. At the end of the seizure, the oscillatory activity with frequency around 10 Hz was found at electrode 1 and progressively declined to the electrode 7. B. Correlation analysis. Cross-correlations were obtained from periods of 2 seconds as indicated by horizontal bars and numbers in the panel A. Electrode 7 was taken as reference. At the beginning (1) and at the end (4) of seizure, the correlation between anterior and posterior electrodes was low. During periods indicated as 2 and 3 the correlation between different electrodes was high; the activity was propagating, but the propagation occurred in two different directions.

Figure 3.4

Figure 3.4: Variability in neuron – field synchronization during fast runs.

A. Depth-EEG and simultaneous intracellular recordings during an electrographic seizure. The distance between field potential electrode and intracellularly recorded neuron was 2 mm. B. A superposition of field potential (upper panels) and intracellular recordings (lower panels) during fast runs for the two consecutive periods of fast runs. Note the different frequencies of oscillations in the EEG and intracellularly recorded neuron during the first period and in phase synchronization during the second period. C. the distribution of patterns of synchronization for 312 periods of fast runs. The synchronous patterns (in phase or with phase shift) constituted 70 % of cases. Arrhythmic stands for periods of fast runs recorded at one electrode, while the activity in another electrode was not rhythmic.

Figure 3.5

Figure 3.5: Propagation of fast run activity during coherent oscillations.

A. Upper left – a fragment of field potential recordings during fast runs; upper right – cross-correlation for 10 consecutive cycles of highly coherent activity. Lower panels – examples of field potential activity and cross-correlation in which the frequency of activity was slightly different in the two electrodes. Such periods were not included in the analysis of propagation shown in C . B and D . The same arrangement as in A and C , but for dual intracellular recordings.

Figure 3.6

Figure 3.6: Frequency of fast runs and their modulation during seizure.

A . An example of electrographic seizure containing a prolonged period of fast runs.

B . Instantaneous frequency of oscillation during fast runs: filed symbols for field potential and neuron shown in panel A, empty symbols - examples from two other recordings. Note a progressive decrease in the frequency of the fast run. C . Autocorrelation of EEG and intracellular activities from 5 sec period indicated in the panel A . Note, lower values of maximal correlation obtained from intracellular traces as compared to EEG traces. D 1 . The frequency relation between EEG and intracellular recordings. and D 2 . Maximal autocorrelation relation between EEG and intracellular recordings. Note that in the majority of cases the frequency of fast runs at EEG and intracellular levels was similar. In the vast majority of cases the amplitude of autocorrelation for EEG traces was higher then for intracellular traces. E 1 . Histograms of cycle duration for EEG (left) and intracellular recordings (right). E 2 . Maximal correlation values for EEG (left) and intracellular (right) recordings.

Figure 3.7

Figure 3.7: Membrane potential of cortical neurons during spike-wave and fast run components of seizures.

A . Field potential and intracellular recordings during a fragment of electrographic seizure containing spike-wave complexes and a fast run period. B . Histogram of membrane potential during spike-waves (transparent bars) and fast run (grey bars) from the neuron shown in A . Note the presence of two peaks during spike-wave complexes and one peak during fast runs. C . Distribution of membrane potential modes for hyperpolarizing (black bars),

Figure 3.8

Figure 3.8: Discharge patterns of variable electrophysiological types of neurons during fast runs.

Left column – field potential and simultaneous triple intracellular recording during a period of fast run. Histogram at upper right corner displays a mean number of spikes generated by neurons of different electrophysiological types during each cycle of paroxysmal fast runs. Electrophysiological identification of neurons from left column is indicated at right. IB – intrinsically-bursting, FRB – fast-rhythmic-bursting, RS – regular spiking neuron and FS fast – spiking neurons. Statistically significant difference (alpha 0.01) is indicated by asterisks.

Figure 3.9

Figure 3.9: Modeled neuron oscillatory activity induced by a current pulse.

DC pulse (10 sec in duration, bar) was applied to the model PY neuron. Following high frequency firing, [K+]o increased and maintained oscillations in PY neuron after DC pulse was removed. (A) Single neuron. Insert shows typical burst of spikes. (B) Reciprocally connected PY-IN pair. Insert shows [K+]o evolution for PY neuron. Change of the [K+]o induced transition from slow (2-3 Hz) to fast (10-15 Hz) oscillations. Bursts of spikes (but not tonic spiking) in PY neuron induced spikes in the inhibitory interneuron.

Figure 3.10

Figure 3.10: Oscillations in the network model (100 PY and 25 IN neurons).

DC stimulus was presented for 10 sec (from t=8 sec to t=18 sec) to all PY neurons and was followed by slow bursting and then by fast oscillations. (A) Network activity near the transition from slow bursting to fast runs. Top, no coupling (gPY-PY=0, gPY-IN=0, gIN-PY=0), time interval from t=19 sec to t=26 sec. Bottom, coupled network (gPY-PY=0.07 μS, gPY-IN=0.07 μS, gIN-PY=0.05 μS), time interval from t=26 sec to t=33 sec. (B) Cross-correlations (sliding window, 500 msec duration, 100 msec time steps) between one PY neuron (PY35, shown by arrow in panel A) and its neighbors (PY36-PY39). Vertical arrows indicate transition from slow bursting to fast runs. Oscillations were highly synchronized with zero phase shift in the bursting mode. During fast run, oscillations in the neighbor PY cells displayed variable phase shift (local activity propagation) with sudden phase changes.

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