Different EEG frequency band synchronization during nocturnal frontal lobe seizures

Different EEG frequency band synchronization during nocturnal

frontal lobe seizures

Raffaele Ferria,*, Cornelis J. Stamb, Bartolo Lanuzzaa, Filomena I.I. Cosentinoa,Maurizio Eliaa, Sebastiano A. Musumecia, Giovanni Pennisic

aOasi Institute for Research on Mental Retardation and Brain Aging (IRCCS), Sleep Research Center, Department of Neurology, Troina, ItalybVrije Universiteit University Medical Center, Department of Clinical Neurophysiology, Amsterdam, The Netherlands

cUniversity of Catania, Department of Neurological Sciences, Catania, Italy

Accepted 5 December 2003

Abstract

Objective: In this article we describe the course of synchronization between different EEG channels during nocturnal seizures in one

patient with nocturnal frontal lobe epilepsy (NFLE).

Methods: The functional interactions between the different EEG channels during the nocturnal seizures were analyzed by means of the so-

called synchronization likelihood (SL). SL is a measure of the dynamical interdependencies between a time series (EEG channel) and one or

more other time series. In contrast to coherence, SL measures linear as well as non-linear interdependencies and it can do so as a function of

time, making it suitable for non-stationary time series.

Results: The main result of our single-patient study is the demonstration of a significant hyper-synchronization during NFLE seizures in

the 8–12 Hz band which seems to be stopped by an increase in synchronization in the 0.5–4 Hz band, towards the end of each ictal episode.

Conclusions: We suggest that a self-inhibiting complex mechanism might be responsible for the termination of ictal episodes which might

take place at the level of the cortical layers and might involve mainly pyramidal neurons.

Significance: This study shows that advanced EEG analysis methods can help the current understanding of ictal manifestations of NFLE.

q 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Keywords: Epilepsy; Nocturnal frontal lobe epilepsy; Frequency band synchronization

1. Introduction

In the last years, particular attention has been paid to the

nocturnal frontal lobe epilepsy (NFLE), characterized by

sleep-related seizures arising from epileptic foci located

within the frontal lobe (Provini et al., 1999, 2000). This

condition is also characterized by the frequent absence of

clear-cut epileptic abnormalities on the scalp EEG even

during the ictal episodes (Provini et al., 1999; Wada and

Purves, 1984; Williamson et al., 1985). For these reasons it is

often very difficult to differentiate an epileptic seizure from a

parasomniac attack by means of the EEG recording alone

when the epileptic focus is located in the deep or mesial

frontal regions and the attacks are restricted to non-REM

(NREM) sleep; thus, the use of video-polysomnography has

become the gold-standard for the diagnosis of NFLE (Provini

et al., 1999).

On the basis of video-polysomnographic recordings,

Provini et al. (1999, 2000) have described 3 main clinical

aspects of this condition: (1) paroxysmal awakenings (PA)

consisting of recurrent arousals from NREM sleep,

associated with a stereotypic motor pattern, usually lasting

, 20 s; (2) nocturnal paroxysmal dystonia (NPD) charac-

terized by recurrent motor attacks with dystonic-dyskinetic

features arising from NREM sleep and usually lasting , 2

min, and (3) episodic nocturnal wanderings (ENW) (Pedley

and Guilleminault, 1977; Plazzi et al., 1995) with

somnambulic agitated behavior arising from NREM sleep.

Thus, NFLE seems to comprise a spectrum of distinct

phenomena, different in intensity but representing a

continuum of the same epileptic condition.

Despite its detailed clinical description, the EEG aspects

and the underlying brain dynamics of NFLE are still poorly

Clinical Neurophysiology 115 (2004) 1202–1211

www.elsevier.com/locate/clinph

1388-2457/$30.00 q 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/j.clinph.2003.12.014

* Corresponding author. Tel.: þ39-0935-936111; fax: þ39-0935-

653327.

E-mail address: [email protected] (R. Ferri).

http://www.elsevier.com/locate/clinph

understood. For this reason, we report here the results of the

study of the functional interactions which take place

between different EEG channels from different scalp areas

during nocturnal seizures in one patient with NFLE.

2. Case report

2.1. Clinical study

The patient, a 30-year-old male, was born from

nonconsanguineous parents. No family history for epilepsy

was reported; in particular, no other relatives were reported

to be affected by nocturnal seizures or other sleep disorders

which might have been interpreted as a misdiagnosis of

NFLE (nightmares, night terrors, other parasomnias such as

REM sleep behavior disorder or psychiatric disorders). His

clinical history was only characterized, during infancy, by a

sleep disorder resembling sleep-talking; moreover, after

puberty, his parents noticed the presence during sleep of

brief contractions of the limbs which, apparently, did not

cause awakening. At the age of 28 years, the patient

presented his first nocturnal seizure; the frequency of

seizures has progressively increased over the last 2 years

and the patient reported 10–15 episodes per night at the

time of our first examination. No other types of ictal

episodes were reported during sleep or wakefulness. The

ictal episodes were easily recognized by the patient because

they caused awakening and a severe disruption of his sleep

structure with consequent insomnia, non-restorative sleep

and daytime excessive sleepiness. The patient had been

taking benzodiazepines, in the past, without any effect on

the frequency and severity of attacks; at the time of our

examination he was drug free. The patient was neurologi-

cally and cognitively normal and gave informed consent for

the participation in this study. Magnetic reconance imaging

(MRI) scans were obtained from a 0.5 T superconducting

magnet, (Philips Gyroscan MR) which were normal. A

multisection spin-echo sequence (proton density, TR 1850

ms, TE 30 ms and T2-weighted, TR 1850 ms, TE 90 ms)

was performed in the axial and coronal planes, and a

multisection T1-weighted sequence (TR 520 ms, TE 30 ms)

in the sagittal plane centered at the midline. The section

thickness was 5 mm, with a gap of 1 mm between adjacent

sections.

2.2. Neurophysiological recording

Routine EEG showed, during drowsiness, very sporadic

dubious single spikes over the left hemisphere (Fig. 1);

subsequently, the patient underwent one overnight video-

polysomnographic recording which comprised EOG (two

channels), EEG (19 channels, electrodes placed according

to the 10–20 International System referred to linked

earlobes), EMG of the submentalis muscle and ECG. Both

recordings were carried out using a Brain Quick Micromed

System 98 recording machine and signals were sampled at

256 Hz and stored on hard disk for further analysis,

synchronized with the video recording. EEG signals, in

particular, were digitally bandpass filtered at 0.1–120 Hz,

12 bit A/D precision.

2.3. Video-polysomnographic study

The analysis of the video-polysomnographic recording

allowed us to detect 15 ictal episodes which appeared to be

stereotyped for both their clinical and polygraphic aspects.

All episodes (Fig. 2) occurred during NREM sleep stage 2

and lasted approximately 20 s; the patient suddenly opened

his eyes, lifted is head and then sat up in bed holding the

mattress with his hands, and this was followed by a dystonic

elevation of the right leg which was maintained in

hyperextension and presented tremor. Heart rate was

significantly increased during the attack. At the end, the

patient fell back onto the bed with a pained expression, and

making a moaning sound; all attacks were followed by

wakefulness of a variable duration. The EEG during the

attacks was uninformative for the presence of muscle

artifacts.

Even if the ictal EEG was uninformative, we hypothe-

sized that this patient was affected by NFLE because of the

presence of left interictal spikes in his EEG and of more than

one seizure with a stereotypic motor pattern, during the

polysomnography recording.

Based on this diagnostic hypothesis, antiepileptic drug

therapy was started with carbamazepine (15 mg/kg per day)

which induced a dramatic decrease in frequency of the

nocturnal attacks and, after 5 months of treatment, the

patient appeared to be seizure-free during a control night

polysomnography.

2.4. Synchronization likelihood

In order to analyze the functional interactions between

the different EEG channels during the nocturnal seizures of

this patient we utilized the so-called synchronization

likelihood (SL) recently introduced by Stam and van Dijk

(2002). SL is a measure of the dynamical interdependencies

between a time series (EEG channel) and one or more other

time series. In contrast to coherence, SL measures linear as

well as non-linear interdependencies and it can do so as a

function of time, making it suitable for non-stationary time

series. A detailed technical description of SL can be found

in the paper by Stam and van Dijk (2002). Here we give a

more intuitive description of the method. We consider two

dynamical systems, X and Y. These systems can be thought

of as neural networks underlying the EEG recorded at two

different electrode positions. Now we represent the

dynamics of X and Y by vectors Xi and Yi in their respective

state spaces; such state space vectors can be obtained from

the time series by time-delay embedding (Takens, 1981).

Now we can define synchronization as the likelihood that

R. Ferri et al. / Clinical Neurophysiology 115 (2004) 1202–1211 1203

the state of one system is a function F of the state of the

other system: X ¼ F(Y). The function F does not have to be

linear; the only requirement is that it is locally smooth. This

concept can be put into practice by SL which is simply

the chance that, if system X is in the same state at two

different times i and j, then system Y will also be in the same

state at time i and j. ‘Being in the same state’ is

operationalized by computing distances between vectors

Fig. 1. Routine EEG showing, during drowsiness, very sporadic dubious single spikes over the left hemisphere (arrow).

Fig. 2. Video-polygraphic study of one ictal episode occurring during nonREM sleep stage 2.

R. Ferri et al. / Clinical Neurophysiology 115 (2004) 1202–12111204

Xi and Xj; if this distance is smaller than a small critical

distance rcutoff, X is in the same state at time i and j. In the

case of complete synchronization, SL ¼ 1; in the case of

independent systems SL equals the chance that two random

vectors of Y will be closer than rcutoff. rcutoff is chosen

separately for X and Y such that the likelihood of random

vectors being close is fixed at a known low value which is

called pref (usually chosen to be 0.05). Thus when two

systems X and Y have independent dynamics, SL ¼ pref.

For the computation of SL, an average reference voltage

was used in order to minimize artifactual sources of

synchronization and signals were bandpass filtered (digital

off-line filter with no phase-shift) in order to analyze

separately SL in the following frequency bands: 0.5–4.5

Hz, 4.5–8 Hz, 8.0–12.0 Hz, 12.0–16.0 Hz, and 16.0–30.0

Hz. SL was then calculated by means of a moving window

(4010 data points); for the state space reconstruction,

an embedding dimension of 10 was used with vector data

lag 10; finally, in order to avoid the effect of the eventual

correlation between adjacent data points, the Theiler

correction was introduced with a value of 100 data points

(Theiler, 1986).

Fig. 3a shows the analysis of the EEG of the 30 s epoch

preceding another epoch containing one of the ictal

episodes. The polygraphic recording is shown on the

bottom; the upper 5 graphs show the time course of SL

averaged over all channels (average of SL values of each

EEG channel with all the remaining EEG channels), for

each frequency band. In this figure we can see that the

subject is in NREM sleep stage 2 and SL shows a relatively

small variability around an average low value, in all bands.

Fig. 3b shows the epoch containing one of the seizures

recorded. The polygraphy shows the presence of muscle

artifacts over the EEG channels which do not allow to

recognize clear-cut specific ictal epileptiform EEG activity.

On the contrary, SL in the 8.0–12.0 Hz band shows a clear

Fig. 3. (a) Analysis of the EEG of the 30 s epoch preceding another epoch containing one of the ictal episodes (the same episode shown in Fig. 2). The

polygraphic recording is shown on the bottom; the upper 5 graphs show the time course of SL averaged over all channels, for each frequency band. (b) Analysis

of the following 30 s EEG epoch containing the seizure. Graphs as in (a). (c) Analysis of the following 30 s EEG epoch. Graphs as in (a).


peak starting at the seizure onset and progressively

increasing for approximately 6 s; this is followed by a

stable high level for another period of approximately 6 s and

by a sudden decrease lasting approximately 1 s. SL in the

0.5–4.0 Hz band shows a different pattern with a wide peak

with onset approximately 2 s before the end of the 8.0–12.0

Hz band SL peak and a duration of about 8–9 s. No

important modifications of SL can be seen in the 3

remaining frequency bands. It is interesting and important

to see that the time course of activity in the EMG channel

does not correlate with the time course of SL in the

8.0–12.0 Hz and 0.5–4.0 Hz bands; also, the lack of

changes in the high-frequency (16.0–30.0 Hz) band argues

against muscle artifact being the cause of the SL changes.

Moreover, even the eventual artifactual effects of the

steepness of the digital filtering can be ruled out because

it would have been expected also in the 16.0–30.0 Hz band,

but no significant changes in SL were observed in this

frequency range. Finally, it must be noted that even if the

signals seems to saturate, this effects is only due to the screen

output of the program and the digitized signals, used for the

computation of SL, were not affected by saturation.

Fig. 3c shows the following 30 s epoch with a typical

wakefulness polygraphic recording and, again, SL shows a

relatively small variability around an average low value, in

all bands (only the 8.0–12.0 Hz band shows values of SL

slightly higher than those during sleep, as one might

expect).

Fig. 4 shows the time course of SL in the 8.0–12.0 Hz

and 0.5–4.0 Hz bands during all 15 ictal episodes recorded

(lower graphs); the upper graph reports the average time

course of SL in the 8.0–12.0 Hz (thick line) and 0.5–4.0 Hz

(thin line) bands. In this figure, we can see that the peak of

SL in the 8.0–12.0 Hz band is consistent and stable. Even at

a lower average amplitude, the peak of SL in the 0.5–4.0 Hz

band also seems to be an important feature of the seizures;

however, it shows a certain variability.

Fig. 5 shows the traditional power spectrum analysis

(fast Fourier transform) performed over all channels and

covering an epoch of 4 s (central part of the seizure)

(top) and of quiet wakefulness preceding sleep (bottom).

Fig. 3 (continued )


In this figure it is possible to see that the main power

peak is at around 9 Hz during the seizure with a

predominance over the left frontal areas. The same

analysis performed during quiet wakefulness shows a

much less evident power peak at around 11 Hz, more

evident over the parietal-temporal regions and symmetri-

cal. On the right, the corresponding topographic scalp

distribution of the main peak is also shown, for both the

seizure and the wakefulness.

Finally, Fig. 6 (top) shows the same seizure reported in

Fig. 3 after low-pass digital filtering (10 Hz) of the EEG

signals. In this figure it is possible to see the occurrence of a

clear 9 Hz rhythm which increases in amplitude in the first

part of the seizure and remains stable until the sudden end of

it. In particular, this rhythm appears shortly before the

tremor-like motor manifestation of the seizure and

decreases in amplitude shortly before the end of the same

tremor, involving the right lower limb; this, together with

the regional distribution of its power shown above, rules out

the possibility of an artifactual nature for this EEG

phenomenon.

3. Discussion

Among patients with NFLE, in the last decade a

particular genetic form has been identified with autosomal

dominant inheritance (Scheffer et al., 1995); this implies

that at least two affected individuals can be found in these

families, from two different generations. The chromosomal

defect, in these families, was assigned to the region 20q13.2

by linkage analysis (Steinlein et al., 1994). This region

contains the candidate gene CHRA4 which codes for the a4

subunit of the neuronal nicotinic acetylcholine receptor. All

genetic abnormalities identified so far in the CHRNA4 are

heterozygous mutations which are responsible only for a

very small number of cases with autosomal dominant NFLE

(Rozycka et al., 2003). Two other types of mutations have

also been found in single families involving chromosome 15

(Phillips et al., 1998) and chromosome 1 (Gambardella et al.,

2000). In our patient no other relatives were reported to be

affected by epilepsy or other sleep disorders which might

have been interpreted as a misdiagnosis of NFLE, such as

nightmares, night terrors, other parasomnias or psychiatric

Fig. 3 (continued )


disorders (Scheffer et al., 1994); for this reason, no

molecular genetic analyses were performed.

The relationships between different EEG signals are

commonly quantified with linear techniques, in particular

estimates of the coherence, which is a normalized

measure of linear correlation as a function of frequency

(Nunez et al., 1997; Nunez et al., 1999). This approach

has been widely used for the study of normal and

pathological brain conditions; however, it has a number

of limitations. First of all, coherence estimates are not

suitable to characterize non-stationary data with rapidly

changing interdependencies, as in our case. A more

important limitation is that methods such as coherence

only capture linear relations between time series, and

may fail to detect non-linear interdependencies between

the underlying dynamical systems. In this respect, there

is general agreement that the EEG during epileptic

seizures (Pijn et al., 1997) or prolonged epileptiform

discharges (Ferri et al., 2001) reflects highly nonlinear

dynamics. Even couplings between MEG and EEG

signals in a no-task eyes-closed state in health subjects

have been shown to be weakly non-linear (Stam et al.,

2003). Thus, the application of a technique only able to

detect linear relationships between the different EEG

signals would have been potentially unable to describe

such interdependencies during the seizures recorded in

our subject.

The main result of our study is the demonstration of a

clear increase in SL in the 8.0–12.0 Hz band during the

seizures recorded. It would be important to be able to

understand which are the structures responsible for this

increase. Synchronizing mechanisms are usually thought to

be subserved by thalamocortical pathways and, as an

example, there is experimental evidence that thalamocor-

tical neurons may oscillate either in the delta or sigma

frequency range depending on their membrane potential

(Steriade et al., 1993; Contreras and Steriade, 1995) during

NREM sleep.

However, in our case, the ictal increase in synchroniza-

tion is in the alpha range; thus, we should look at the

mechanisms generating alpha oscillations which are very

different from those cited above. In fact, alpha waves are

thought to be generated mainly within the cerebral cortex at

the level of the pyramidal neurons in layers IV and V (Lopes

da Silva and Storm van Leeuwen, 1977) and a system of

surface-parallel intracortical neurons should be involved in

Fig. 4. Time course of SL in the 0.5–4.0 Hz and 8.0–12.0 Hz bands during all the 15 ictal episodes recorded (lower graphs); the upper graph reports the average

time course of SL in the 8.0–12.0 Hz (thick line) and 0.5–4.0 Hz (thin line) bands.


its spread (Lopes da Silva and Storm van Leeuwen, 1978;

Lopes da Silva et al., 1980).

However, our analysis of the power spectrum of

8.0–12.0 Hz band during seizures and that of alpha waves

during wakefulness demonstrated clearly that these two

activities are different from each other both for frequency

and scalp distribution. Another possibility should then be

taken into account and our 9 Hz ictal rhythm might be

interpreted as being similar to the fast runs (10–20 Hz)

which characterize the Lennox-Gastaut syndrome and other

Fig. 5. Power spectrum analysis (fast Fourier transform) of one seizure performed over all channels and covering an epoch of 4 s (central part of the seizure)

(top) and of quiet wakefulness preceding sleep (bottom). Scale values are shown only for one channel and are the same for all the remaining channels shown in

upper and lower graphs. On the right, the scalp topographic distribution of the main power spectrum peak is also shown for both the seizure and the quiet

wakefulness preceding sleep.


types of syndromes which seem to be generated intracorti-

cally, mainly in pyramidal neurons, in animal models

(Steriade et al., 1998).

If these considerations are correct, our approach has been

able to reveal a significant hyper-synchronization during

NFLE seizures in the 8.0–12.0 Hz band which seems to be

stopped by the increase in synchronization in the 0.5–4.0

Hz band, towards the end of each ictal episode. In this

respect, it is interesting to note that also delta waves can be

generated at the level of the cortex and are not always under

the control of thalamocortical pathways. In fact, it is known

that delta waves can be seen in athalamic cats (Villablanca,

1974) and, even if this kind of experiments does not

represent final evidence of cortical generation for delta

potentials, it is believed that EEG delta waves can be

generated by summation of afterhyperpolarization produced

by different potassium currents in deep pyramidal neurons

(Steriade et al., 1990). Also in this case, there is another

possibility because in animal models it has been shown that,

in some cortically generated seizures during sleep, fast ictal

waves can develop towards the end of the seizure into lower

frequency potentials (Steriade et al., 1998).

Thus, our results seem to indicate that a self-inhibiting

complex mechanism might be responsible for the termina-

tion of ictal episodes which might take place at the level of

the cortical layers and might involve mainly pyramidal

neurons. Also the efficacy of carbamazepine treatment,

which was able to stop seizures in our patient, might be due

to its eventual ability to decrease abnormal synaptic

transmission in the cortex by affecting sodium channels in

pyramidal neurons (Macdonald, 1995).

Finally, our analysis has shown that, in our patient,

episodes which might have been classified as PA (Provini

et al., 1999) on the basis of its behavioral features are

characterized by neurophysiological signs of paroxysmal

increase in SL in the 8.0–12.0 Hz band (at approx. 9 Hz)

which, despite its frequency, showed profound differences

with the normal alpha rhythm recorded during wakefulness.

Fig. 6. Original (top) and 10 Hz low-pass filtered (bottom) 30 s EEG epoch containing one of the seizures.


This study shows that SL might be used to assess the

dynamics of brain electrical synchronization in other types

of seizures.

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Different EEG frequency band synchronization during nocturnal frontal lobe seizures

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