Laboratoire de Neurophysiologie, Faculté de Médecine, Université Laval, Quebec, Canada G1K 7P4
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ABSTRACT |
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Amzica, Florin and Mircea Steriade. Spontaneous and Artificial Activation of Neocortical Seizures. J. Neurophysiol. 82: 3123-3138, 1999. The aim of this study is to disclose the mechanisms underlying the recruitment of neocortical networks during slow-wave sleep oscillations evolving into spike-wave (SW) seizures. 1) We investigated the activation of SW seizures in a seizure-prone neocortex by means of electrical stimuli applied within the frequency range of spontaneous sleep oscillations. Stimuli were grouped in bursts of 10 Hz, similar to sleep spindles, and repeated every 2 s, to reproduce their rhythmic recurrence imposed by the slow (<1 Hz) sleep oscillation. Either cortical or thalamic stimuli, which were applied while the cortex displayed sleeplike activity, gradually induced paroxysmal responses in intracellularly recorded neocortical neurons, which were virtually identical to those of spontaneous seizures and consisted of a progressive buildup of paroxysmal depolarizing shifts (PDSs). 2) The ability of cortical networks to follow stimuli was tested at various stimulation frequencies (1-3 Hz) and quantified by calculating the entropy of the ensuing oscillation. Rhythmic PDSs were optimally induced, and the lowest entropy was generated, at a stimulation frequency around 1.5 Hz. Fast runs at 10-15 Hz, which often override PDSs, thus contributing to the polyspike-wave pattern of seizures, were induced by cortical stimuli, but were disturbed by thalamic stimuli. Spontaneous seizures generally evolved toward an accelerated discharge of PDSs. It is suggested that these accelerating trends during SW seizures act as protective mechanisms by provoking the uncoupling of cortical networks and eventually arresting the seizure.
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INTRODUCTION |
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Rhythmic sensory stimuli reaching a hyperexcitable
cerebral cortex may produce seizures. The starting hypothesis of the
present study was that focal seizures can be triggered by rhythmic
central stimuli delivered within the same frequency range as the
spontaneous low-frequency oscillations in corticothalamic networks. As
a consequence, naturally occurring synchronized sleep oscillations may
develop into seizures. It was indeed shown that sleep is a favorable
condition for the genesis of certain seizures, particularly those
characterized by spike-wave (SW) complexes at 2-4 Hz (Kellaway
1985; Steriade 1974
). During
electroencephalographic (EEG)-synchronized sleep, the cortex is
dominated by a slow oscillation (<1 Hz) that entrains and groups other
sleep rhythms, generated in thalamus or cortex (Amzica and
Steriade 1998b
; Contreras and Steriade 1995
;
Steriade et al. 1993b
,c
).
In the companion paper (Steriade and Amzica 1999) we
tested the excitability of cortical networks before, during, and after SW seizures. It resulted that a seizure-prone cortex has the ability to
transform incoming stimuli into paroxysmal responses and that the
excitability is modulated by the ongoing synaptic activity of the
network. It is therefore possible that synchronous activities in
corticothalamic networks, as they occur during EEG-synchronized sleep,
trigger pathological oscillations, which would spread over large
cortical territories. During spontaneous seizures, neurons are
subjected to the competitive influence from oscillating networks. The
mechanism generating the oscillatory behavior during seizures is still
unknown. However, in the class of cortically generated SW seizures,
their progressive development from sleep oscillations has been proposed
(Steriade and Amzica 1994
; Steriade and Contreras 1995
; Steriade et al. 1998
).
The present study attempted to modify the oscillating pace by replacing
it with a controlled source of stimulation, to measure the ability of
the network to adapt to the constraint. The stimulating frequencies
used in this study mimicked synchronous synaptic inputs during
slow-wave sleep or seizures. The study is restricted to the behavior of
cortical neurons because the priming role of the cortex in generating
SW seizures has recently been emphasized (Steriade and Contreras
1998; Steriade et al. 1998
).
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METHODS |
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Animal preparation and techniques of electrophysiological
recordings are described in the companion paper (Steriade and
Amzica 1999). The stimulating frequencies used for this study
belong to the natural frequencies occurring spontaneously in the
corticothalamic network during slow-wave sleep or epileptic seizures.
Thus we mainly stimulated at 0.5, 1-3, and 10 Hz. The pace of the
stimulation was either constant or with progressive change. Stimuli
were delivered as single shocks or in clusters (e.g., 5-8 shocks at 10 Hz, every 2 s, mimicking spindles recurring with the periodicity
of the slow oscillation).
The methods for calculating the latency and the surface area below
excitatory potentials have been described in the companion paper
(Steriade and Amzica 1999). Some additional analytic
tools were developed for this study. Because the oscillatory behavior displayed before, during, and sometimes after seizures is not perfectly
periodic, we calculated instantaneous frequencies as reciprocal values
of the time interval between two successive oscillatory events. In the
case of intracellular recordings, we considered the paroxysmal
depolarizing shifts (PDSs) or the fast runs as oscillatory events. To
each of these depolarizing events we associated a time stamp
corresponding to the moment where its rising slope was maximal. To
determine the maximum slope point, we calculated the first derivative
of the intracellular signal and detected its local maximum situated
between the onset of the depolarization and the moment where the first
action potential was triggered. Instant frequencies were plotted as a
function of real time, providing thus a dynamic measure of the
rhythmicity of the ongoing phenomena.
To quantify the oscillatory behavior, we have chosen to use the entropy
as a measure of the dispersion of instantaneous frequency values during
the various epochs related to a seizure. The instant frequencies of a
given period were distributed in a histogram. The coefficient
Ci of each bin (0.25 Hz for PDSs and 1 Hz for fast runs) was transformed into a probability
pi associated to the incidence of a
given frequency to occur
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RESULTS |
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First, we present the contribution of rhythmic stimulation in triggering PDSs and SW seizures. Next, we tested the ability of epileptic cortical networks to display PDSs in response to various stimulation frequencies. Finally, we studied the integration of fast runs and polyspikes within rhythmic activities generated in the corticothalamic network.
Database
A total of 98 regular-spiking neurons were recorded during
SW/polyspike-wave (PSW) seizures in 22 cats. The neurons were recorded for at least 15 min (some lasted for 90 min) and had membrane potentials more negative than 60 mV and overshooting action
potentials. In all, we recorded 267 electrically generated seizures and
149 spontaneous seizures. The typical pattern of the seizure has been previously described (Steriade et al. 1998
) (see also
Fig. 1). It consists of SW complexes at
frequencies between 1.5 and 3 Hz and fast runs of activity (10-15 Hz).
Sometimes these patterns appeared in alternation, and some other times
short sequences of fast runs followed PDSs, thus constituting PSWs.
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Induction of SW seizures with rhythmic stimuli
Slow-wave sleep is dominated by a slow (<1 Hz) cortical
oscillation consisting of periodic depolarizing-hyperpolarizing
sequences (Steriade et al. 1993b). The depolarizing
epochs generate action potentials that underlie the propagation and
synchronization of the slow oscillation through intracortical
(Amzica and Steriade 1995a
,b
) and corticothalamic
networks, thus triggering thalamically generated sleep spindles
(Contreras and Steriade 1995
; Steriade et al.
1993c
). Spindles (sequences of waves at 7-14 Hz, recurring rhythmically at the frequency of the slow oscillation, mainly 0.3-0.5
Hz) often appear superimposed on the depolarizing phase of the slow
oscillation. To reproduce this behavior, we delivered periodic (0.5 Hz)
trains of thalamic stimuli consisting of 5 stimuli spaced by 0.1 s
(Fig. 1). This procedure reliably induced SW seizures in 90 of the
recorded neurons (92%). They were expressed in the gyrus where
recordings were performed, and often in the neighboring gyri also.
Intracellularly, these seizures started with large amplitude (20-40
mV) depolarizations with the features of PDSs, with shape and duration
similar to those underlying interictal and ictal EEG spikes
(Ayala et al. 1973
; Johnston and Brown
1981
; Matsumoto and Ajmone-Marsan 1964a
,b
). Fast
runs at 15-20 Hz accompanied some of these PDSs.
The rhythmic stimulation of the thalamic lateral posterior nucleus produced a gradual onset of the seizure. The first train of stimuli (a in Fig. 1) elicited normal synaptic responses [excitatory postsynaptic potentials (EPSPs)] at each stimulus. The second train (b) had similar effects, but was followed by a sustained depolarization. For the following trains of stimuli (c-f), the response to the third shock in each train became progressively larger, as demonstrated by the depolarizing surface area underlying the EPSP, and the EPSP to the fourth shock triggered a full-blown PDS crowned by increasing numbers of fast runs. This is shown by the significant increase of the surface area of depolarizing responses after the fourth and fifth stimuli. After seven trains of stimuli, the seizure became self-sustained and the stimulation was stopped. Control responses (g) were again elicited after the complete arrest of the seizure.
From the 90 neurons displaying SW seizures after thalamic stimulation with trains of stimuli at 10 Hz, and considering only the first seizure after impalement, the first PDS appeared in most cases during the 3rd train of stimuli (69% of the cases). In 21 neurons, the first PDS was recorded during the 4th train of stimuli (23%), while the rest of the neurons (7, representing 8% of the cases) reacted with PDSs during the 2nd or the 5th train. We selected only the first seizure after impalement to avoid the uncontrolled effect that would have been played by frequent stimulation.
Thus rhythmic pulse trains at 10 Hz repeated with the frequency of the slow oscillation (0.5 Hz) are a precipitating factor for SW seizures. We tested the behavior of the cortical network under the continuous pressure of such a stimulation pattern in 25 neurons of those where the stimulation was triggering SW seizures efficiently (Fig. 2). In 22 of them, recurrent seizures were obtained with a rhythmicity of ~0.04 Hz. The average period of such seizures was 24 ± 3 s (mean ± SD) and the range was between 12 and 30 s. This pattern was either not seen in the absence of the stimulation, or it occurred at a different frequency (generally slower) and less regularly.
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All seizures had a stereotyped pattern (Fig. 2A, expanded period) with paroxysmal events occurring in close time-relation with the trains of stimuli. Some PDSs appeared spontaneously (preceding a train of stimuli) suggesting that the seizure attained a self-sustained development. In all cases, the onset of the seizure was marked by the transition from normal synaptic responses to the train of stimuli (Fig. 2B, sweeps 1-4) to the generation of giant synaptic potentials toward the end of the train of stimuli (sweeps 5-7). The onset of the seizure was accompanied by a gradual increase of the neuronal responsiveness to earlier shocks in the train (see the displacement of the PDSs latency in Fig. 2C, sweeps 8-11). Eventually, a phase was reached where PDSs were reliably evoked by the first shock in the train (sweeps 11-14). The end of the seizure was associated with a symmetrical reaction to stimulation: paroxysmal responses began to slide toward the end of the train and finally disappeared (Fig. 2D, sweeps 15-19). These results suggest that a seizure-prone cortex (e.g., a cortex that presents scars or chemical imbalance or has been stimulated repeatedly), which undergoes the continuous pressure of slow sleep oscillatory activities, may generate recurrent SW seizures.
Spontaneous and evoked PDS
Single cortical stimuli were also effective in triggering
paroxysmal responses (see Figs. 4, 6, 8, 9, and 11 in Steriade
and Amzica 1999). Here we deal with the ability of these
responses to follow various frequencies and regard them in relation
with the corresponding spontaneous oscillations. The experimental
paradigm is described in Fig. 3. The
spontaneous activity of neurons consisted of more or less regularly
occurring PDSs. Their reflections at the EEG level were spiky waves.
The instantaneous frequencies were situated between 0.5 and 1 Hz.
Rhythmic cortical stimulation at a fixed frequency (1 Hz) produced
faithful following by the neuron and by its environment (see
rhythmic EEG spikes at 1 Hz). The evoked PDSs had the same aspect as
the spontaneous ones (bottom of Fig. 3). The paroxysmal
responses to stimuli occurred with an alternative pattern
(inset with vertically expanded detail from the
instantaneous frequency curve). This pattern consisted of instantaneous
frequencies continuously alternating around the fixed stimulation
frequency. It was a constant finding in all tested cells and is the
consequence of a jittering PDS' latency. In spite of this small
variability of responses, sooner or later spontaneous activity
interfered with the ability of the cortical network to follow stimuli
at almost fixed latencies. This phenomenon generally coincided with the
onset of a self-sustained activity. Spontaneous PDSs preceding the
delivery of a stimulus provoked an acceleration and, consequently,
refractory periods (Hablitz 1984
; Steriade and
Amzica 1999
), which produced a certain disorder in the sequence
of instantaneous frequencies (see right side of the
inset in Fig. 3). In the case shown in Fig. 3, the
stimulation was stopped and the oscillation, after a short period with
faster frequencies, resumed control values (<0.8 Hz).
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In our experimental conditions, under ketamine-xylazine anesthesia,
spontaneous seizures had an electrographic pattern similar to that in
the Lennox-Gastaut syndrome (Steriade et al. 1998), with
SW complexes at relatively lower frequencies, 1-2.5 Hz (see Niedermeyer 1998b
). Thus the capacity of the network to
follow imposed rhythms was first tested by delivering stimuli at
various frequencies (Fig. 4). The evoked
PDSs optimally followed stimulation frequencies around 1.4-1.5 Hz
(Fig. 4B). Responses to lower frequency stimulation (
1
Hz) often interfered with the spontaneous PDSs of the network resulting
from accelerating activities (Fig. 4A). Faster stimulation
rates (2 Hz and higher) were followed only for limited periods of time
(Fig. 4C). They were able, however, to produce activities at
submultiples of the stimulation frequency (i.e., 1 and 0.5 Hz). In
other words, PDSs were triggered only every second or every fourth
stimuli. This "undertuning" was usually noticed before and at the
beginning of a seizure. Once self-sustained activities were promoted in
the network, the stimulation at 2 Hz appeared as a superior limit at
which neurons were able to respond with PDSs, and more failures were
evident.
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This phenomenon was associated with a certain variability. Initial attempts to test each cell with too many stimulation frequencies to obtain an optimal tuning frequency proved to be unrealistic because of the interference with spontaneous epileptic and nonepileptic activities. Besides, when more stimulation was delivered to the animal, more spontaneous (unwanted) seizures appeared, and the state of the cells degraded. Therefore only few discrete stimulation frequencies were used (0.5, 1, 1.5, 2, 2.5, and sometimes 3 Hz). We chose to quantify the ability of a seizure-prone network to follow different types of stimuli by calculating the entropy during periods with stimulation at a given frequency. The average entropy of 30 recorded cells during spontaneous SW discharges was 0.78 ± 0.36 (mean ± SD). It dropped to 0.45 ± 0.12 for 1-Hz stimulation, reached a minimum of 0.25 ± 0.07 for 1.5-Hz stimulation, and increased again to 0.8 ± 0.3 for 2-Hz stimulation frequency. These measures suggest that the cortical networks optimally follow evoked or spontaneous oscillations around 1.5 Hz.
In addition to test the responsiveness of the network with fixed
stimulation frequencies, we also investigated its behavior during
sequences of accelerating and decelerating stimuli (Fig. 5). Again, under these conditions neurons
ceased to follow stimuli beyond 2 Hz. Once the tuning was lost,
returning to lower stimulation frequencies, including those at which
reliable responses were previously obtained, did not immediately
produce the coupling of the network to the stimuli (n = 23). In the case illustrated in Fig. 5, two types of stimuli were
delivered: single stimuli at accelerating frequencies (left
part of Fig. 5) and series of stimuli at constant frequencies with
acceleration between the series. In both cases, the neuron reliably
followed up to 1.7 Hz and began to fail about that frequency. The
latency of the PDSs remained constant throughout this period (75
ms). No follow-up was possible above that value (1.7 Hz), although the
latency of some PDSs remained in the control range during the initial
period of the 2.5-Hz stimulation. The stimulation frequency at which the neuron resumed the following was lower (1.1 Hz) than the one at
which he had lost it (2 Hz). This phenomenon, suggesting a hysteresis-type behavior, was seen in a minority of cells
(n = 5). The neuron had, however, no difficulty to
adapt to the slowing of the stimulation frequency (right
part of Fig. 5), as long as the slowing intervened in a moment
when the neuron was still following the stimuli.
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The evolution of the entropy confirms that there is an optimum oscillation frequency, which the cortical networks follow more readily (Fig. 6). Both acceleration beyond and slowing below that frequency is accompanied by loss of coupling and by increased entropy values, thus by increased disorder in the oscillatory behavior of the system. The panel displaying the latencies of the PDSs emphasizes that the arrest of a seizure was preceded, in all cases in which stimulation outlasted the arrest of the seizure (n = 138), by a progressive increase of the latency of the evoked PDS.
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Interaction between spontaneous and evoked fast runs
Synchronized fast (10-15 Hz) oscillations, also called fast runs,
are often present during cortical SW seizures (Neckelmann et al.
1998; Steriade et al. 1998
). They consist of
stereotyped, clocklike, and rhythmic depolarizing potentials of 2- to
15-mV amplitude. These components showed constant features for a given neuron throughout the seizure, and their amplitude decreased with the
distance from the seizure focus. We asked whether this seizure pattern
could be obtained with electrical stimulation, i.e., if an artificially
created focus of fast runs would generate and spread like the
spontaneous one. In a first instance, during dual simultaneous
intracellular recordings, we delivered rhythmic (10 Hz) stimuli to a
cortical region close to the one where the neurons were impaled (Fig.
7). This pattern of stimulation was
applied only at a moment when the electrical activity of the brain was already displaying spontaneous paroxysmal potentials in at least one of
the recorded sites. Only those cases were retained for analysis where
cortical neurons showed synaptic responses to stimulation (n = 10). Synchronized activities were triggered in all
cases of cortical stimulation, and the neurons were faithfully
following the stimuli (Fig. 7). The neuron closer to the stimulation
site displayed more ample responses then the one situated further away, and with a corresponding time lag. The latency of the area 5 neuron was
6.3 ms, whereas the latency of the area 7 neuron was 13 ms. This
pattern of stimulation did not result in self-sustained oscillations with fast runs. It was, however, followed by SW seizures after the
cessation of the stimulation (Fig. 7, top panel). It results that the cortex plays a reinforcing role in the synchronization and
spreading of the fast runs during SW seizures.
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Stimulation of thalamic nuclei anatomically related with the cortical
recording area induced responses in which the priming epileptic event
was the rebound (see Figs. 8 and 9 in Steriade and Amzica
1999). As the thalamus reflects fast runs (Steriade and
Contreras 1998
), we thought that, through its reentrant
projections, it might modulate the fast runs generated in the cortex.
This aspect was studied by stimulating the thalamic centrolateral
intralaminar nucleus during fast runs (Fig.
8). The centrolateral nucleus has widespread projections to the suprasylvian association areas
(Jones 1985
). The stimulation frequency was tuned to the
frequency of the preceding spontaneous fast runs (Fig. 8B1).
Only those cases were considered where the cortical responses
demonstrated, through short-latency EPSPs, the synaptic linkage between
the stimulation and recording sites. In the majority of the cases (18 of 20), the thalamic stimulation disrupted the fast runs (Fig. 8).
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The spontaneous fast runs consisted of regular, stereotyped
depolarizing potentials at 10 Hz (Fig. 8B1). Under
control conditions, thalamic stimuli at 10 Hz elicited a low-amplitude
EPSP (Fig. 8B4), occasionally followed by a larger secondary
depolarizing potential (Fig. 8, B2 and B3). The
latter had similar features with the spontaneous fast run complex. As
shown by the instantaneous frequency diagram, and in contrast with the
cortical stimulation (Fig. 7), the thalamic stimulation interfered with
the spontaneous fast runs and disrupted the rhythmicity of the network
for most of the time. With the exception of the period depicted in Fig. 8B3, spontaneous fast runs appeared to compete with
secondary depolarizations triggered by the stimulation. It is difficult to determine which of the delayed potentials are true rebounds or
occasional spontaneous fast runs. The secondary response in the first
averaged trace of Fig. 8C has a longer duration (
65 ms)
and less steeper onset slopes than the average spontaneous complex
(
50 ms), because of the jittering of its onset latency.
The entropy values increased from 0.33 ± 0.03 during spontaneous fast runs to almost the double during the stimulated period by reaching 0.63 ± 0.12. We conclude that the thalamus has a rather desynchronizing and dampening contribution to the fast runs during SW seizures.
Finally, we tested the influence of tetanic stimulation (100 Hz) on the evolution of fast runs (n = 30). This stimulating frequency was chosen to produce a tonic synaptic pressure on cortical neurons, by simulating the effect of an activated network or the discharge pattern crowning PDSs. The trains of stimuli at 100 Hz were applied to the cortex or the related thalamic nuclei, and lasted for 0.5-1 s. In both cases, the frequency of fast runs decreased during tetanic stimulation (Fig. 9). The neuronal membrane depolarized by 4-7 mV (average 5.2 mV, n = 30) after cortical stimulation, and by 2-5 mV (average 3.4 mV, n = 30) after thalamic stimulation. In Fig. 9, cortical stimulation at 100 Hz produced a slowing of spontaneous fast runs from an average of 13.4 to 8 Hz, which was also associated with an increase in entropy from 0.13 to 0.52 (Fig. 9A). In all tested neurons (n = 30), there was a frequency decrease of 46.7% and an entropy increase of 256%. The effect of thalamic stimulation, although basically similar, was weaker (Fig. 9B): the average frequency dropped from 15 to 11 Hz, whereas the entropy increased from 0.21 to 0.62. For the 30 recorded neurons, the average frequency decrease was 22%, accompanied by an entropy increase of 195%.
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Thus we assume that tonic pressure exerted on cortical neurons during fast runs activity has the virtue of slowing and disorganizing the oscillation.
Seizures with PSW complexes
According to one of our previous studies (Steriade et
al. 1998), 70% of the seizures contained both rhythmic SW
complexes (1.5-3 Hz) and fast runs (10-15 Hz). Until now, we
investigated the interaction between paroxysmal activities in
corticothalamic networks and artificially created oscillatory foci.
Often, a PDS is accompanied by short sequences of fast runs, the whole
generating at the EEG level the PSW pattern (Fig.
10; see also Fig. 8 in the companion
paper). Therefore we tested the effect of rhythmic stimulation on such
seizures by focusing on PSWs.
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An example of a spontaneous seizure is shown in Fig. 10A (top panel). Considering only the time intervals between consecutive PDSs, and calculating the respective instantaneous frequencies, the spontaneous evolution of the seizure was associated with a progressive acceleration from 1.5 to 3 Hz. Such acceleration was present in most of the spontaneous seizures (88% of the 192 observed seizures). We also calculated the instantaneous frequencies of the polyspikes components of each PDS (bottom graph in Fig. 10A). The values are, with the exception of the two periods with continuous fast runs, randomly distributed between 10 and 25 Hz. During the two short episodes of fast runs, instantaneous frequencies polarize around 12 Hz.
Cortical stimulation within the frequency range of spontaneous PSW activity (1-3 Hz) produced the same effect as in seizures with poor or no PSW components (data not shown). An interesting outcome resulted, however, from continuous stimulation at the frequency of the fast runs (10 Hz in Fig. 10B). The parameters of the neuronal PDSs (duration of the depolarization, duration of the hyperpolarization) adjusted such that the sustained seizure assumed a rhythmic evolution. PDSs recurred at the beginning at 2.5 Hz (every 4 stimuli) and continued at 3.3 Hz (every 3 shocks). Later on, spontaneous activity interfered with the stimulation, and the coupling was lost. In spite of this, the instant frequency values remained within a constant range (2.5-4 Hz), and no accelerating trend was detected. Polyspikes were also affected by the 10-Hz stimulation. The distribution of their instant frequencies distributed, at least during the middle part of the seizure, around two modes: 14 and 27 Hz.
This increased oscillatory organization was also confirmed by a drop in the entropy averaged over 25 such seizures: PDS oscillations went from 0.48 to 0.3 (37.5%), whereas polyspikes decreased from 0.53 to 0.48 (13.2%). The individual values were calculated over equivalent periods of time of spontaneous and stimulated seizures, and include, in the latter case, also those periods where the spontaneous activity was interfering with the stimulation. Thus the above-calculated figures are conservative.
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DISCUSSION |
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We have shown that 1) periodic trains of stimuli mimicking natural sleep activities induce SW seizures, 2) the seizure-prone cortical networks optimally follow oscillatory activities at 1.5 Hz, 3) the cortex favors the development of fast runs while the thalamus disrupts them, and 4) during fast runs, tetanic stimulation plays a protective role by decreasing the discharge frequency and the synchronization.
Sleep oscillations and SW seizures
All-night sleep recordings in humans have demonstrated an increase
of paroxysmal discharges during light non-rapid eye movement sleep.
This sleep stage is marked by the appearance of K-complexes (initial
sharp component, followed by a slow component) (Roth et al.
1956) and sleep spindles. The role of K-complexes in
conjunction with paroxysmal potentials was emphasized
(Niedermeyer 1998a
). Recently, the cellular mechanisms
of the K-complex were described (Amzica and Steriade
1998a
), and it has been shown that K-complexes are the
electrographic expression of the slow oscillation in both humans
(Amzica and Steriade 1997
) and cats (Amzica and
Steriade 1998a
). Previous studies have shown that the
cortically generated slow oscillation is relayed to the thalamus, where
it triggers sequences of spindles (Contreras and Steriade
1995
; Steriade et al. 1993c
). The spreading and
synchronization of the slow oscillation (Amzica and Steriade
1995b
), as well as of epileptic discharges (Neckelmann
et al. 1998
), is supported by intracortical reciprocal connections (Avendaño et al. 1988
). Although SW
seizures of the type described in this study originate in the cortex
(Maquet et al. 1995
; Marcus and Watson
1968
; Marcus et al. 1968
) and are absent from
the decorticated thalamus (Steriade and Contreras 1998
),
intact networks undergo the concerted action of cortical and thalamic oscillations.
The interaction between oscillations generated within the cortex (the slow oscillation) and the thalamus (spindles) may lead, in a seizure-prone cortex, to paroxysmal developments (Figs. 1 and 2) of the SW/PSW type. This phenomenon occurs gradually, with paroxysmal responses arising from the previously normal K-complexes.
The K-complex has been repeatedly considered an arousing reaction
during sleep (see, for instance, Halasz 1998;
Niiyama et al. 1996
), which led to the idea that
microarousals are precursors of SW seizures occurring during sleep.
First, the K-complex is part of a cortical slow oscillation generated
during sleep (Amzica and Steriade 1997
,
1998a
,b
; Steriade et al. 1993b
), which is
abolished by the action of cholinergic and adrenergic activating
systems (Steriade et al. 1993a
). No K-complexes are
present during wakefulness. Second, sleep SW seizures develop in
parallel with increased synchrony within cortical networks
(Steriade and Amzica 1994
), which stands in contrast
with the diminished synchrony characterizing activated cortical
networks. The result is that the K-complex, as a precursor of the PDS
(Steriade et al. 1998
), is not associated with any arousing reaction.
The paroxysms investigated in this and the preceding paper are not
reflex epilepsy in the classical sense, as is the case of photogenic
(Killam et al. 1967; Naquet and Valin
1990
) or audiogenic (Faingold and Meldrum 1990
;
Krushinsky 1962
) epilepsy, where sensory signals trigger
seizures. By extrapolation, we propose that a hyperexcitable cortex
receiving periodic stimuli from naturally ongoing neuronal activities
may trigger reflex SW seizures. In other terms, similarly to the
seizures induced by recurrent sensory stimuli, oscillatory activities
intrinsically generated in the sleeping corticothalamic network may
play a similar role in triggering SW seizures. The hyperexcitablity of
the cortex may be due to a series of nonexclusive factors such as
chemical imbalance, impaired inhibition, hypersynchronized
oscillations, and kindling. Increased extracellular potassium
concentrations are known to contribute to epileptogenesis
(Moody et al. 1974
). Indeed, in baboons, seizure onset
was preceded by an increase in extracellular potassium concentration from 3.1 to 9.8 mM (Pumain et al. 1985
).
Coupling of cortical neurons during epileptiform oscillations
The response of a hyperexcitable cortex to cortical and thalamic
stimuli was analyzed in the companion paper (Steriade and Amzica
1999). It resulted that the amplitude of an evoked paroxysm is dependent on the time interval from the occurrence of a previous PDS
(evoked or spontaneous). This observation has particular implications for the results presented in this paper where we tested the ability of
an epileptic cortical network to follow an imposed rhythm. PDSs are
giant EPSPs lasting typically for a few hundreds of milliseconds (Ayala et al. 1973
; Johnston and Brown
1981
; Matsumoto and Ajmone-Marsan 1964a
,b
). A
relative refractoriness was already reported for hippocampal (Hablitz 1984
) and neocortical PDSs (Hwa and
Avoli 1991
). In the present study, the relative synaptic
refractoriness of cortical neurons is demonstrated by their decreasing
ability to reliably follow frequencies higher than 2 Hz (Figs. 4 and
5). This limitation is in agreement with the pattern of seizures
developed in our experimental condition (resembling the electrographic
pattern of the Lennox-Gastaut syndrome) in which the upper frequency
limit (2.5-3 Hz) is lower than in the classical SW (absence petit mal) discharges (3-4 Hz). The duration of PDSs is often prolonged by short
sequences of fast runs (PSWs in the EEG), and this may explain the
lower frequency range of these seizures (Figs. 1 and 10).
Together with the graded size of the evoked PDS as a function of the
time interval from the anterior one (Steriade and Amzica 1999), the relative refractoriness sets the scene for a
protective and adaptive behavior of the cortical network to incoming
stimuli. The protective feature refers to the fact that cortical
neurons respond to more frequent inputs with less ample and shorter
PDSs. These will produce, in turn, less excitatory pressure in the
network, thus contributing to the arrest of the seizure. Another
consequence of this protective behavior is the fact that the
depolarization surface area of the PDS determines the amount of
K+ expelled extracellularly (unpublished
observations). Because the concentration of extracellular
K+ regulates the neuronal excitability, the
reduction in this concentration will contribute to the dampening of the seizure.
The ability of cortical neurons to resonate at certain frequencies with extrinsic stimuli was quantified by means of the entropy. It resulted that the optimum coupling occurred at frequencies around 1.5 Hz, which corresponded to the highest order in the system (Figs. 4-6). The entropy values given in this study are relative and pertain to the conditions declared in METHODS. They proved useful for comparing spontaneous and triggered seizures (Fig. 10). In all cases the latter were associated with lower entropy values (higher order). The drop in entropy depended on the stimulation frequency, but could reach up to 68%. Electrical stimuli to the cortex or thalamus constitute hypersynchronous drives to the network and mimic an epileptic focus because the responses are virtually identical to the spontaneous PDSs (Fig. 3). Two nonexclusive reasons could explain the fact that spontaneous seizures have higher entropy values (less ordered behavior): 1) they develop from multiple competing foci, or 2) they originate in a single focus but encounter resistance from various pools of neurons.
In the present experiments, spontaneous activities interacted with, and disrupted the, imposed inputs. In the overwhelming majority of cases, it was an acceleration of the rhythm, eventually inducing the loss of coupling (Figs. 3 and 4). This suggests that other pools of neurons than the ones undergoing the stimulation became active and attempted to drive the neuron at the time of the stimulation. Thus it appears more probable that the relative increased entropy of spontaneous seizures is due to distributed epileptogenic foci.
The origin of fast runs (10-20 Hz) is not known (see, however,
Traub and Jefferys 1994). They survive in cortical slabs
(Timofeev et al. 1998
) thus being independent on
thalamic activities. The present data further suggest that the thalamus
may play a rather disturbing role for this seizure component (Fig. 8),
whereas the cortex tends to organize this oscillation (Fig. 7).
Spontaneous seizures with fast runs may create a rhythmic cortical
drive on thalamocortical neurons, which, in turn would convey the
signal back to the cortex. Our data show that, when an oscillatory
behavior is imposed in the thalamus by diffuse and intense stimulation at the natural frequency of the fast runs, the cortical fast runs are
disturbed. This further suggests that the thalamus does not play a
supportive role in the genesis of this oscillation. Although intrinsic
properties, such as a persistent sodium current (Llinás 1988
), may play a role in the genesis of fast runs, it is clear that cortical synaptic linkages are essential for its propagation and synchronization.
Tonic barrages of high-frequency stimuli at 100 Hz, applied either to
the thalamus or to the cortex, slow down fast runs (Fig. 9). They may
mimic activating inputs as well as spike bursts overriding PDSs.
Arousing stimuli may stop or diminish the incidence of absence seizures
(Kostopoulos et al. 1987). As to their effect on fast runs, they mainly produced a steady depolarization associated with a
deceleration of the discharge rate. The latter was not the mere
consequence of the stimulation on intrinsic properties because it was
reflected also at the population level (see EEG in Fig. 9), where
synchronizing mechanisms are essential. Without leading to conclusive
results for the influence of arousal on seizures, the tetanic
stimulation proved that fast runs are mainly generated through synaptic
coupling and that persistent stimulation reduced the order in the
network, thus contributing to the arrest of the oscillation.
Concluding remarks
Stimulating at fixed frequencies increases the order (decreases the entropy) in the system. It results that there is no restricted network able to impose a given oscillatory behavior of a given frequency. Oscillatory phenomena of the types investigated in this paper are rather the outcome of distributed networks and are assisted by the intrinsic properties of individual cells, which modulate the reactivity of the whole network by allowing him to follow or to favor certain frequencies and filtering other ones. Spontaneous sleep oscillations constitute in the seizure-prone brain a precipitating factor.
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ACKNOWLEDGMENTS |
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We thank P. Giguère and D. Drolet for technical assistance.
This work was supported by Grant MT-3689 from the Medical Research Council of Canada and Grant RG-81/96 from the Human Frontier Science Program to M. Steriade. F. Amzica was partially supported by the Fonds de la recherche en santé du Québec.
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FOOTNOTES |
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Address reprint requests to M. Steriade.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 23 April 1999; accepted in final form 23 August 1999.
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