D.N. Velis

Epilepsies as Dynamical Diseases of Brain Systems: Basic Models of the Transition Between Normal and Epileptic Activity

Summary:  Purpose: The occurrence of abnormal dynamics in a physiological system can become manifest as a sudden qualitative change in the behavior of characteristic physiologic variables. We assume that this is what happens in the brain with regard to epilepsy. We consider that neuronal networks involved in epilepsy possess multistable dynamics (i.e., they may display several dynamic states). To illustrate this concept, we may assume, for simplicity, that at least two states are possible: an interictal one characterized by a normal, apparently random, steady ‐state of ongoing activity, and another one that is characterized by the paroxysmal occurrence of a synchronous oscillations (seizure).

Dynamical diseases of brain systems: different routes to epileptic seizures

In this overview, we consider epilepsies as dynamical diseases of brain systems since they are manifestations of the property of neuronal networks to display multistable dynamics. To illustrate this concept we may assume that at least two states of the epileptic brain are possible: the interictal state characterized by a normal, apparently random, steady-state electroencephalography (EEG) ongoing activity, and the ictal state, that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called, in neurology, a seizure. The transition between these two states can either occur: 1) as a continuous sequence of phases, like in some cases of mesial temporal lobe epilepsy (MTLE); or 2) as a sudden leap, like in most cases of absence seizures. In the mathematical terminology of nonlinear systems, we can say that in the first case the systems attractor gradually deforms from an interictal to an ictal attractor. The causes for such a deformation can be either endogenous or external. In this type of ictal transition, the seizure possibly may be anticipated in its early, preclinical phases. In the second case, where a sharp critical transition takes place, we can assume that the system has at least two simultaneous interictal and ictal attractors all the time. To which attractor the trajectories converge, depends on the initial conditions and the systems parameters. An essential question in this scenario is how the transition between the normal ongoing and the seizure activity takes place. Such a transition can occur either due to the influence of external or endogenous factors or due to a random perturbation and, thus, it will be unpredictable. These dynamical changes may not be detectable from the analysis of the ongoing EEG, but they may be observable only by measuring the systems response to externally administered stimuli. In the special cases of reflex epilepsy, the leap between the normal ongoing attractor and the ictal attractor is caused by a well-defined external perturbation. Examples from these different scenarios are presented and discussed.

Nonlinear dynamics of epileptic seizures on basis of intracranial EEG recordings

SummaryPurpose: An understanding of the principles governing the behavior of complex neuronal networks, in particular their capability of generating epileptic seizures implies the characterization of the conditions under which a transition from the interictal to the ictal state takes place. Signal analysis methods derived from the theory of nonlinear dynamics provide new tools to characterize the behavior of such networks, and are particularly relevant for the analysis of epileptiform activity.Methods: We calculated the correlation dimension, tested for irreversibility, and made recurrence plots of EEG signals recorded intracranially both during interictal and ictal states in temporal lobe epilepsy patients who were surgical candidates.Results: Epileptic seizure activity often, but not always, emerges as a low-dimensional oscillation. In general, the seizure behaves as a nonstationary phenomenon during which both phases of low and high complexity may occur. Nevertheless a low dimension may be found mainly in the zone of ictal onset and nearby structures. Both the zone of ictal onset and the pattern of propagation of seizure activity in the brain could be identified using this type of analysis. Furthermore, the results obtained were in close agreement with visual inspection of the EEG records.Conclusions: Application of these mathematical tools provides novel insights into the spatio-temporal dynamics of “epileptic brain states”. In this way it may be of practical use in the localization of an epileptogenic region in the brain, and thus be of assistance in the presurgical evaluation of patients with localization-related epilepsy.

Combined use of subdural and intracerebral electrodes in preoperative evaluation of epilepsy.

For intracranial recording of partial seizures considered to originate from one of the temporal or frontal lobes, the team in the Utrecht Academic Hospital has used subdural, multicontact, flexible electrodes since 1972. These are introduced through bilateral, frontocentral trephine holes and are manipulated under fluoroscopy to cover most of the cerebral convexity. It became evident that in many patients, additional placements to record from intracerebral structures were indispensable. Therefore, using the same trephine holes, an additional 2 to 4 depth electrodes were stereotactically implanted in the mesial temporal and/or frontal structures, as appropriate. An extensive intra- and extracerebral spatial representation of the epileptogenic zone was thus obtained. We report here the methods for manufacturing and applying these electrodes and our clinical experience with 28 patients. The results obtained so far stress the value of combining subdural and depth electroencephalographic monitoring in the presurgical selection of patients suffering from medically refractory complex partial seizures. By miniaturizing the electrodes, extensive areas of the brain can be investigated without craniotomy or multiple burr holes.

Alpha rhythms: noise, dynamics and models

Alpha rhythms appear as sinusoidal-like oscillations in the electroencephalogram (EEG) within the frequency range 8-12 Hz that waxe and wane in a more or less irregular way. The irregularity may have various origins. It may be due to noise or the oscillations may have an intrinsic irregular character, e.g. they may be generated by chaotic processes [Jansen (1991) Quantitative analysis of electroencephalograms: is there chaos in the future? Int. J. Biomed. Comput., 27: 95-123; Pradham, N. and Dutt, D.N. (1993) A nonlinear perspective in understanding the neurodynamics of EEG. Comput. Biol. Med., 23: 425-442; Pritchard et al. (1995) Dimensional analysis of resting human EEG II: Surrogate-data testing indicates nonlinearily but not low-dimensional chaos. Psychophysiology. 32: 486]. The term noise is often used in neurophysiology with different connotations as pointed out by Bullock (1990), either meaning an unwanted signal from the point of view of the receiver of a message, or a signal with intrinsic random fluctuations, i.e. with a stochastic character. Here we consider noise in this sense, as random or quasi-random neural activity. In this overview, we concentrate on the question of whether alpha rhythms should be considered generated in neuronal networks (1) as forms of filtered noise, (2) as deterministic oscillations influenced by noise or (3) as the result of chaotic dynamics. A clear answer to this question can have theoretical value because it may lead to a general model of the generation of this important EEG signal. Such a model, of course, would be a macroscopic one, since it would primarily account for the properties of the alpha rhythms at the neuronal network level. A translation of these properties to the microscopic, i.e. neuronal, level will not be easy to achieve without more direct knowledge of the membrane and synaptic basic properties of the neurons involved. Here we consider the question formulated above by presenting some relevant experimental evidence and theoretical arguments. The consideration whether alpha rhythms may have noise or chaotic sources implies examining how and where such sources can occur in the neuronal networks of the brain. Therefore we present, first, some basic data regarding the possible origin of noise and of chaos in neuronal networks. Second, the signal analysis methods that have to be applied in order to discriminate between filtered noise activities and chaotic oscillations are introduced. Third, the implications of these signal analyses regarding the possible answer to the initial question are discussed.

Dynamics of epileptic phenomena determined from statistics of ictal transitions

In this paper, we investigate the dynamical scenarios of transitions between normal and paroxysmal state in epilepsy. We assume that some epileptic neural network are bistable i.e., they feature two operational states, ictal and interictal that co-exist. The transitions between these two states may occur according to a Poisson process, a random walk process or as a result of deterministic time-dependent mechanisms. We analyze data from animal models of absence epilepsy, human epilepsies and in vitro models. The distributions of durations of ictal and interictal epochs are fitted with a gamma distribution. On the basis of qualitative features of the fits, we identify the dynamical processes that may have generated the underlying data. The analysis showed that the following hold. 1) The dynamics of ictal epochs differ from those of interictal states. 2) Seizure initiation can be accounted for by a random walk process while seizure termination is often mediated by deterministic mechanisms. 3) In certain cases, the transitions between ictal and interictal states can be modeled by a Poisson process operating in a bistable network. These results imply that exact prediction of seizure occurrence is not possible but termination of an ictal state by appropriate counter stimulation might be feasible.

Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy

OBJECTIVE To explore and validate a novel stimulation and analysis paradigm proposed to monitor spatial distribution and temporal changes of the excitability state in patients with temporal lobe epilepsy (TLE). METHODS We use intermittent pulse stimulation in the frequency range 10-20Hz. A quantitative measure of spectral phase de-modulation, the relative phase clustering index (rPCI) was applied to the evoked EEG signals, measured from electrodes implanted in the hippocampal formation. RESULTS We found that in the interictal periods, high values of rPCI recorded from specific sites were correlated with the most probable seizure onset sites (SOS). Furthermore we found that high values of rPCI from certain locations correlated with shorter time intervals to the next seizure. CONCLUSIONS Our clinical findings indicate that although the precise moment of ictal transitions is in general unpredictable, it may be possible to estimate the probability of occurrence of some epileptic seizures. SIGNIFICANCE The use of the rPCI for probabilistic forecasting of upcoming epileptic seizures is warranted. rPCI measurements may be used to guide interventions with the aim of modifying local tissue excitability that ultimately might prevent ictal transitions.

Material-Specific Recognition Memory Deficits Elicited by Unilateral Hippocampal Electrical Stimulation

Although the medial temporal lobe is thought to be critical for recognition memory (RM), the specific role of the hippocampus in RM remains uncertain. We investigated the effects of transient unilateral hippocampal electrical stimulation (ES), subthreshold for afterdischarge, on delayed item RM in epilepsy patients implanted with bilateral hippocampal depth electrodes. RM was assessed using a novel computer-controlled test paradigm in which ES to left or right hippocampus was either absent (baseline) or synchronized with item presentation. Subsequent yes-no RM performance revealed a double dissociation between material-specific RM and the lateralization of ES. Left hippocampal ES produced word RM deficits, whereas right hippocampal ES produced face RM deficits. Our findings provide the first demonstration in humans that selective unilateral stimulation-induced hippocampal disruption is sufficient to produce impairments on delayed RM tasks and provide support for the material-specific laterality of hippocampal function with respect to RM.

Spike cluster analysis in neocortical localization related epilepsy yields clinically significant equivalent source localization results in magnetoencephalogram (MEG)

OBJECTIVE In magnetoencephalogram (MEG) recordings of patients with epilepsy several types of sharp transients with different spatiotemporal distributions are commonly present. Our objective was to develop a computer based method to identify and classify groups of epileptiform spikes, as well as other transients, in order to improve the characterization of irritative areas in the brain of epileptic patients. METHODS MEG data centered on selected spikes were stored in signal matrices of C channels by T time samples. The matrices were normalized and euclidean distances between spike representations in vector space R(CxT) were input to a Wards hierarchical clustering algorithm. RESULTS The method was applied to MEG data from 4 patients with localization-related epilepsy. For each patient, distinct spike subpopulations were found with clearly different topographical field maps. Inverse computations to selected spike subaverages yielded source solutions in agreement with seizure classification and location of structural lesions, if present, on magnetic resonance images. CONCLUSIONS With the proposed method a reliable categorization of epileptiform spikes is obtained, that can be applied in an automatic way. Computation of subaverages of similar spikes enhances the signal-to-noise ratio of spike field maps and allows for more accurate reconstruction of sources generating the epileptiform discharges.

Regional brain glucose metabolism in patients with complex partial seizures investigated by intracranial EEG

We performed interictal 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography (18FDG-PET) studies in 57 patients with complex partial epilepsy (CPE), not controlled by medical treatment and considered for surgical resection of their epileptic focus. A precise localization of the epileptic focus was obtained in 37 of these patients with a combination of subdural and depth electrodes. We visually inspected the metabolic images; we also measured glucose consumption in a number of brain regions and compared the values with those obtained in 17 normal controls. Eighty-two percent of the 57 patients had an area of glucose hypometabolism on the 18FDG-PET images. Six patients had a frontal epileptic focus, 3 of them had a frontal lobe hypometabolism. Twenty-six patients had a unilateral temporal lobe focus and all of them displayed a temporal lobe hypometabolism. The asymmetry was more pronounced in the lateral temporal cortex (-20%) than in the mesial part of the temporal lobe (-9.6%). In each cortical brain region on the side of the epileptic focus (except the sensorimotor cortex), glucose consumption rate was lower than in the contralateral region or than in controls. No differences could be found between patients with a seizure onset restricted to the hippocampus and patients with a seizure onset involving the hippocampus and the adjacent neocortex. Divergent metabolic patterns were obtained in 5 patients with bilateral temporal seizure foci. Combined with other non invasive techniques (EEG, neuroradiology), PET contributes increasingly to the selection of patients with CPE who could benefit from surgical treatment.(ABSTRACT TRUNCATED AT 250 WORDS)