Electrophysiological analysis of the transition between normal brain activity and epileptic seizures: an overview of recent findings and new concepts

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1 Colloque de la FFRE Colloque de la FFRE Épilepsies 2009 ; 21 (3) : Electrophysiological analysis of the transition between normal brain activity and epileptic seizures: an overview of recent findings and new concepts Fernando H. Lopes da Silva Center of Neurosciences, Swammerdam Institute for Life sciences, University of Amsterdam, Spui 21, 1012 WX Amsterdam, The Netherlands <silva@science.uva.nl> Résumé. La contribution de Fernando Lopez da Silva est une revue des méthodes et des concepts qui soustendent la recherche sur les mécanismes électrophysiologiques de l équilibre instable entre activités intercritique et critique. Il montre comment l étude des corrélations entre les activités neuronales au sein d ensembles neuronaux et l analyse non linéaire des signaux électrophysiologiques permettent de proposer de nouvelles conceptions de l épileptogenèse. Mots clés : épilepsie humaine, électrophysiologie, méthodes This lecture is a contribution to the Colloque sur l épilepsie where the main focus was: Explorer la fonction du cerveau épileptique : comment et pourquoi?. One of the obvious replies to this question is that electro- (magneto-) physiological methods are most appropriate to explore brain functions with respect to epileptic conditions. The methodology of recording electrical (EEG) or magnetic (MEG) fields of brain activity has already a long history and has become an integral part of every day routines of the laboratories of clinical neurorophysiology, particularly those dedicated to epilepsy. This is not surprising taking into consideration that the activity of neurons in the brain takes place mainly through electrical signals, and that the recording of electrical signals of populations of neurons is easy to realize and relatively cheap to implement in practice. Furthermore these signals have the important property that they reflect the activity of neurons at the millisecond time scale. The latter corresponds to the time frame at which neurons communicate with each other and processing of sensory, motor and cognitive phenomena takes place. This property makes the EEG and MEG indispensable to identify quick changes of neuronal activity as occur in epilepsy. The pathological conditions that we describe with the collective term of epilepsies are typical dynamical diseases since an epileptic manifestation is characterized by the sudden occurrence of an increase in synchronous activity within relatively large neuronal net- doi: /epi Épilepsies, vol. 21, n 3, juillet-août-septembre

2 Electrophysiological analysis of the transition between normal brain activity and epileptic seizures works. This widespread synchronous state disturbs the normal working of the brain. It may be triggered by some changes in endogenous network s parameters and/or inputs, although this may not be evident to an observer. This is the essence of a paroxysmal or dynamical disorder. Why and how paroxysmal episodes occur is difficult to apprehend, based only on current knowledge of pathophysiology, because of the complexity of the factors that jointly are responsible for their occurrence. In this context, it is useful to apply concepts derived from the mathematics of non-linear complex systems to the analysis of the working of neuronal networks. In general, we may assume that in the epileptic brain, some neuronal networks can display different kinds of dynamical states because they possess an abnormal set of control parameters. In other words, they may have bi-(multi-) stable properties. This means that, in addition to a normal steady state, they also have an abnormal one characterized by widespread synchronous activity, and that the transition between these two states may occur abruptly. This accounts for the two main characteristics of epilepsy: that an epileptic brain can function apparently normally between seizures (i.e., during the interictal state); that the seizures occur in a paroxysmal way, thereby impairing brain functions. In this sense, epileptic disorders may be considered special cases of the large class of dynamical diseases, meaning those pathophysiologic states characterized by the occurrence of abnormal dynamics, a theoretical concept proposed by Glass and Mackey more than 20 years ago. Since epileptic phenomena are essential manifestations of abnormal dynamics of the brain it is not surprising that EEG or MEG signals are optimally adequate to catch these changes of dynamical states. Electrophysiological approach versus other approaches of studying brain functions In the last decade, the emergence of brain imaging techniques, such as positron emission tomography (PET) and functional magnetic resonance imaging (fmri), have made important contributions to our understanding of basic brain processes both under normal and pathological conditions, namely in epilepsy. Notwithstanding the fact that in particular fmri can provide maps of brain activity with millimeter spatial resolution, its temporal resolution is, as yet, limited. Indeed this resolution is in the order of seconds while epileptiform phenomena may take place within tens of milliseconds. Therefore, it is important to resort to electrophysiological techniques in order to achieve the desired temporal resolution in this kind of investigations, although at the cost of spatial resolution. The essential problem of electro/magnetoencephalography (MEG), however, is that the spatial distribution of the sources of neuronal activity cannot be derived in an unique way from the distribution of EEG or MEG activity at the scalp, since this inverse problem is an ill-posed problem that has no unique solution. However, recently new possibilities are emerging to solve this problem. A promising one is to obtain estimates of possible solutions of the inverse problem by imposing spatial constraints based on anatomical information provided by MRI and additional physiological information from metabolic or hemodynamic signals, derived from fmri, that are associated with the neuronal activity. The precise nature of this association, however, is not trivial and it is a matter of current investigation. This implies that we may expect that in the near future technical advances in how to combine fmri and EEG-MEG measures of brain activity will contribute to a better understanding of neurocognitive functions and pathologic conditions like epileptic phenomena. Basic properties of the EEG-MEG The EEG consists essentially of the summed electrical activity of populations of neurons, with a modest contribution of glial cells. Considering that the neurons are excitable cells with characteristic intrinsic electrical properties and that the interneuronal communication is essentially mediated by electrochemical processes at synapses, it follows that these cells can produce electrical and magnetic fields that may be recorded at some distance from the sources. These electrical fields are called the local EEG or local field potentials (LFPs); those recorded from the cortical surface constitute the Electrocorticogram or ECoG, and those recorded from the scalp constitute the EEG. The associated MEG is recorded usually by way of sensors placed at a short distance around the scalp. In order to understand how the electrical and magnetic signals of the brain are generated, it is necessary to examine how the activity of assemblies of neurons is organized both in time and in space, and which biophysical laws govern the generation of extracellular field potentials, or magnetic fields. Both the intrinsic properties of neuronal membranes and the fact that the neurons form highly interconnected networks with feed forward and feedback loops, give rise to characteristic oscillations at different frequency ranges. In general terms, the frequency of a brain oscillation depends both on the intrinsic membrane properties of the neuronal elements and on the properties of the networks to which they belong. Thus the EEG-MEG reflect the dynamical properties of the neuronal networks of the brain. A fundamental property of a neuronal network is the capacity of the neurons to work in synchrony. This depends essentially on the way the inputs are organized and on the network interconnectivity. General features of the electrophysiology of epileptic conditions In general terms, we may distinguish two main kinds of epileptiform activities: those occurring while no epileptic seizures are present, and those that are associated with epileptic seizures; The former are called inter-ictal and the latter ictal. In both cases, it is important to note that these epileptiform phenomena consist of changes of neuronal activity that are 283 Épilepsies, vol. 21, n 3, juillet-août-septembre 2009

3 F.H. Lopes da Silva directly recordable as changes of the electric activity of the brain, i.e.; they appear as typical changes in the EEG or MEG. The neuronal sources of these phenomena may appear as being static in space, as in the case of some kinds of inter-ictal events, but more generally these phenomena tend to propagate over the cortex. We may distinguish two main problems that arise when the propagation of epileptiform activity from a focal area must be analyzed: the first one is to determine whether, or not, there are multiple independent sources; the second one is to determine, in case that related sources exist, whether the activity of one source occurs in a fixed time sequence in relation to that of another source. In the latter case, a time delay may be expected between the signals, due to the finite propagation velocity of electrical activity in the brain. A precise analysis of these propagation patterns of activity is important in order to determine the localization of the so-called irritative zone, or more specifically the zone of ictal onset, what is essential to plan an eventual surgical removal of the epileptogenic zone. This analysis implies that the association and the time relations between EEG signals recorded from different brain sites have to be carried out. Associations and time delays between EEG signals in epilepsy A classic way to estimate the degree of association between two signals and the corresponding time delay is to calculate the correlation coefficient as a function of time shift between the two signals. This is called the cross-correlation function. Alternatively, we can deal with this problem in the frequency domain by estimating the coherence and phase functions. In short, the coherence function expresses how each frequency component of one signal is related to the corresponding frequency component of the other signal. A limitation of these methods is that they provide unambiguous results only if the relationship between the signals is linear. This is not necessarily the case for epileptic seizure activities. Therefore, methods have been proposed that are not restricted to the case of linear relationships. We introduced a method to estimate the relationship between two signals that always applies, regardless of whether or not it is linear. This is a form of non-linear regression analysis, as it describes the dependency of a signal on another one in a general way, independently of the type of relation between the two signals. This measure was called h 2.If the relationship is linear, h 2 approximates the crosscorrelation coefficient r 2. This method of analysis was useful to solve the question of whether the so-called Primary Generalized Epilepsies (PGE), such as the commonly occurring Absences, may have a focal onset or not. Absence-type seizures: generalized or not? Classically absence seizures or petit mal seizures are considered the paradigm of PGE. They are characterized by a sudden arrest of ongoing behavior and conscious awareness, while the EEG displays the abrupt onset and cessation of bilaterally synchronous 3/second spike-and-wave discharges (SWDs) over wide cortical areas. The generalized nature of the discharges led to the hypothesis of a common central (midline subcortical) pacemaker by Jasper and Kershman (1941). However, the relative contributions of thalamus and cortex to the pathophysiology of this kind of disorder are still a matter of debate among clinicians and experimental researchers. We approached this issue using a genetic model of absence epilepsy, the WAG/Rij rat. The basic question that we formulated was the following. In the intact brain of WAG/Rij rats is it possible to find out whether SWDs are initiated in the cortex or in the thalamus, and to estimate how these discharges evolve in the brain? This question was approached by a quantified analysis of the interrelations of local EEG signals recorded simultaneously from multiple cortical and thalamic sites, during SWDs in the freely moving WAG/Rij rat. The dynamics of the evolution of SWDs, both on the cortical surface and in the thalamus, was quantified using signal analysis methods based on the estimation of nonlinear signal associations (h 2 ). A cortical focus was found within the peri-oral sub-region of the somato-sensory cortex of the rat. This leading focus was found consistently when the first 500 ms of a SWD was analyzed. This timing was essential, since as the SWDs evolve in the course of time the relationships become more complex. Thalamic and other cortical sites were consistently found to lag behind this focal site, with time delays that increased with electrode distance, resulting in a whole seizure propagation velocity over the cortex of 1.4 m/s averaged over animals. These results show that the large-scale synchronization during the initial stage of generalized SWDs primarily arises from intracortical processes. After the initial period of 500 ms, the time relations between cortex and thalamus could switch directions in an unpredictable way, indicating that during the whole SWDs cortex and thalamus form one complex oscillatory network where a leading area cannot be identified any more. The leading role for the cortex is corroborated by our observation, and of others, that cortical spike-and-waves could sometimes occur without concomitant thalamic spikeand-waves, whereas the reverse was never observed. These results challenge two common assumptions. First, instead of being non-focal, absence-type seizures are of focal origin. The generalized and apparent synchronous appearance of the SWDs from the outset is caused by an extremely fast cortical spread of seizure activity. Secondly, the primary driving source for the rhythmic discharges is, in the WAG/Rij experimental model, not the thalamus but the cortex. However, after the oscillation has started, cortex and thalamus form one complex oscillatory network, in which oscillations in both structures are interlocked. The role of the thalamus probably lies in providing a resonant circuit to amplify and sustain the SWDs. The latter can be considered an emerging property of this thalamocortical neuronal circuit. Épilepsies, vol. 21, n 3, juillet-août-septembre

4 Electrophysiological analysis of the transition between normal brain activity and epileptic seizures The electrophysiological analysis of the genetic absence rat model led to new neurobiological findings The findings described above; regarding the identification of a focal area from which the spike-and-wave discharges arise, have already led to fruitful new investigations in order to determine whether the neuronal populations of the somato-sensory focal cortical area have particular properties that may differentiate them from other cortical areas, and make them more prone for the initiation of SWDs. Interestingly, bilateral injection of the anti-absence drug ethosuximide at multiple cortical and thalamic loci in GAERS, leads to a decrease of the incidence of SWDs only in case the drug is injected in the somato-motor cortex and not in other cortical or thalamic areas. In addition, this focal cortical area appears to have a number of other peculiarities that make it more epilepsy-prone, namely the following: increase of NMDA receptor mediated excitatory synaptic activity; alterations in the expression of voltage sensitive sodium channels mrna (quantitative PCR) and the corresponding protein (immunocytochemistry). Furthermore, Blumenfeld et al. investigated whether during SWDs the cortical activation had a focal or a generalized character, using blood oxygen level-dependent (bold) fmri measurements at 7T with simultaneous EEG recordings. During spontaneous SWDs in WAG/Rij rats they found increased fmri signals in focal regions including the peri-oral somato-sensory cortex, whereas the occipital cortex was spared. For comparison, this was not the case for bicuculline-induced generalized tonic-clonic seizures under the same conditions, where the fmri increases were larger and more widespread than during SWDs. These findings support the conclusion that SWDs have a focal cortical origin. Dynamics of EEG signals leading to epileptiform activity induced by visual stimulation, photosensitive epilepsy (PSE) PSE, or increased risk of seizures triggered by visual stimuli, appears in approximately 10% in a pediatric epilepsy population and in 5% of the adult patients representing the most common form of reflex epilepsy in humans. The condition is manifested in the EEG in the form of a paroxysmal spike/polyspike and wave discharges during the stimulation by means of intermittent photict stimulation (IPS), the so-called photoparoxysmal response (PPR). Despite that, this phenomenon has been already known for a long time, very little is known over the mechanisms of generation of the PPR, in particular over the relationships between the physiological response during IPS, the steady-state evoked activity or photic-following response and the pathological response, the PPR. We investigated whether changes in the dynamics of neuronal activity, as revealed in the EEG or MEG, may precede the transition to PPR. The detection of such changes might help to understand how the transition from a normal steady-state evoked activity into PPR takes place. For this purpose, we studied ten patients with idiopathic PSE, recording their responses to IPS using whole-head MEG, and compared them to control subjects. We used MEG in our study because this relatively new technique has potential advantages in this sort of studies. First, MEG provides a higher spatial density of recording points than that obtainable with EEG. Second, MEG offers theoretical advantages in studies of synchronization or coherency because it dispenses the use of a reference sensor. Finally, magnetic fields are less distorted than the electric ones by the smearing effect of the skull that acts like a low-pass filter, allowing, in principle, better conditions for the recording of high frequency components such as gamma band oscillations. A 15 Hz red and blue flicker stimulation was the most provocative stimulus. The stimulation trials where a PPR was not elicited whether from the same patients or from the patient who never displayed a PPR formed the non-ppr group. The phase clustering index (PCI) of the higher harmonics in the gamma band, related to that of the fundamental frequency of stimulation, was larger than the PCI at the fundamental frequency in the cases that the stimulation evolved into PPR. This resulted in that these patients presented larger relative phase clustering index (rpci) values in most of the trials, which elicited a PPR. This was the case for all the different paradigms of stimulation. When the stimulation did not provoke a PPR, the PCI in the gamma band harmonics did not exceed the PCI value for the fundamental frequency, resulting in rpci values similar to those found in normal controls or non-photosensitive epileptic patients. Interestingly, the rpci values at the beginning of a period of photic stimulation that is not followed by PPR may be similar to that of a similar corresponding period where PPR occurs. However, in the course of the IPS stimulation, the PCI decreases rapidly in the former case while it may either decrease only slightly and at a slower rate, stay at a high value or even increase further, in the latter. Until now, we have only considered values of rpci averaged over all sensors. It is important to examine how the rpci is distributed over the different areas of the head in order to identify the brain region where the change in rpci is most evident. Those sensors showing the largest rpci values were always located over the occipital, infero-temporal and parietal region, virtually sparing the fronto-polar sensors. The main finding of this study indicates that there exists an enhancement of the degree of phase clustering (rpci) of higher harmonics, in the MEG gamma frequency range in contrast to lower frequency bands, during IPS in cases where IPS is followed by PPR, but not in cases where the latter do not occur. This means that the value of the rpci may eventually be used to anticipate, the occurrence of the transition between a state cha- 285 Épilepsies, vol. 21, n 3, juillet-août-septembre 2009

5 F.H. Lopes da Silva racterized by a normal photic driving response to an abnormal state consisting of paroxysmal oscillations. These data are consistent with the interpretation that the enhancement of the gamma PCI in our experiments is related to the existence of natural non-linear resonance properties of the visual system. These findings may help in phenotyping patients for genetic studies. Furthermore, these results should be put in the wider context of the sensitivity of certain subjects for different kinds of visual stimuli; particularly video games, as revealed in the Pokemon incident, which caused an unprecedented number of seizures among Japanese children. The application of the PCI to assess the dynamics of the transition to spontaneously occurring temporal lobe seizures (TLE) In cases of TLE, one has not available a natural sensory stimulus that might be used to probe the excitability state of the neuronal networks of patients with TLE, as in the cases of photic sensitivity described above. As an alternative we used an intracerebral local stimulation procedure. We took advantage of the fact that some of these patients, with medically refractory TLE, are stereotaxically implanted with intracerebral electrodes in order to record seizures during presurgical evaluation. This enabled us to probe the excitability state of the local networks using intermittent electrical pulse trains applied through the indwelling electrodes. The pulse trains were applied to closely spaced electrodes placed in the hippocampus. Video/EEG recording was carried out in standard epilepsy monitoring unit environment. Anti-epileptic drug (AED) medication was tapered in order to record a sufficient number of habitual seizures for lateralization and localization of the seizure onset sites (SOS) in each patient. Stimulation protocols consisted of biphasic, current source generated electrical pulses of 500-1,000 microa and of 0.1 ms per phase, inter-phase delay 0.1 ms. The pulse sequence frequencies were mostly 20 Hz. The duration of the sequences was 5 s (100 pulses in 20 Hz sessions) and the interval between them was minimal 20 s. The pulse sequences were applied intermittently over long periods of time lasting many consecutive hours and even days. In this case, the maximum frequency component detected in the rpci analysis was 250 Hz due to the limitations of the recording system. The main results were that the rpci values measured from EEG signals recorded from the SOS containing hemisphere were larger than those computed from the other hemisphere in four out of four cases available. This was particularly conspicuous for the components in the frequency range from Hz. In all cases studied until now, the electrode contact with the largest rpci was the same contact, or one of the several contacts, where the SOS was identified by an ictal visual inspection. Most remarkable rpci was also shown to be usable for epileptic seizure anticipation. In three patients where it was possible to record for sufficiently long periods of time including an appreciable number of seizures, values of rpci superior to 0.6 indicated that the probability of a seizure occurring in less than two hours had an accuracy of 80%, while values between 0.1 and 0.3 indicated probable seizure expectancy within hours (> 80% accuracy). Measured values of rpc inferior to 0.1 statistically correlated with times to next seizure longer than 45 hours (> 80% accuracy). This implies that monitoring the value of rpci during presurgical evaluation can be applied to estimate the probability of seizures occurring, and may be useful to assess the effect of manipulations, either pharmacological or deep brain stimulation, aimed at decreasing the probability of their occurrence. Selected bibliography (own publications) Bouwman BM, Suffczynski P, Lopes da Silva FH, Maris E, van Rijn CM. GABAergic mechanisms in absence epilepsy: a computational model of absence epilepsy simulating spike and wave discharges after vigabatrin in WAG/Rij rats. Eur J Neurosci 2007 ; 25 : Kalitzin SN, Parra J, Velis DN, et al. Enhancement of phase clustering in the EEG-MEG gamma frequency band anticipates transitions to paroxysmal epileptiform activity in epileptic patients with known visual sensitivity. IEEE Trans Biomed Eng 2002 ; 49 : Kalitzin SN, Velis DN, Suffczynski P, et al. Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy. Clin Neurophysiol 2005 ; 116 : Kalitzin SN, Parra J, Velis DN, et al. Quantification of unidirectional nonlinear associations between multidimensional signals. IEEE Trans Biomed Eng 2007 ; 54 : Lopes da Silva FH, Blanes W, Kalitzin SN, et al. Dynamical diseases of brain systems: different routes to epileptic seizures. IEEE Trans Biomed Eng 2003 ; 50 : Lopes da Silva FH, Blanes W, Kalitzin SN, et al. Dynamical diseases of brain systems: different routes to epileptic seizures. IEEE Trans Biomed Eng 2003 ; 50 : Meeren HK, Pijn JP, Van Luijtelaar EL, et al. Cortical focus drives widespread cortico-thalamic networks during spontaneous absence seizures in rats. J Neurosci 2002 ; 22 : Meeren H, van Luijtelaar G, Lopes da Silva FH, et al. Evolving concepts on the pathophysiology of absence seizures: the cortical focus theory. Arch Neuro 2005 ; 62 : Niedermeyer E, Lopes da Silva FH. Electroencephalography: Basic principles, clinical applications and related fields. Urban and Schwarzenberg, Baltimore-Munich; 5 Editions: 1982, 1987, 1993, 1998, Parra J, Kalitzin SN, Iriarte J, et al. Gamma-band phase clustering and photosensitivity: is there an underlying mechanism common to photosensitive epilepsy and visual perception? Brain 2003; 126: Parra J, Kalitzin SN, Iriarte J, et al. Gamma-band phase clustering and photosensitivity: is there an underlying mechanism common to photosensitive epilepsy and visual perception? Brain 2003 ; 126 : Parra J, Kalitzin SN, Lopes da Silva FH. Photosensitivity and visually induced seizures. Curr Opin Neurol 2005 ; 18 : Suffczynski P, Lopes da Silva FH, Parra J, et al. Dynamics of epileptic phenomena determined from statistics of ictal transitions. IEEE Trans Biomed Eng 2006 ; 53 : Épilepsies, vol. 21, n 3, juillet-août-septembre