Two types of remote propagation in mesial temporal epilepsy: analysis with scalp ictal EEG

Focal epilepsies and focal disorders

Electroencephalographic (EEG) investigations are crucial in the diagnosis and management of patients with focal epilepsies. EEG may reveal different interictal epileptiform discharges (IEDs: abnormal spikes, sharp waves). The EEG visibility of a spike depends on the surface area of cortex involved (>10cm²) and the brain localization of cortical generators. Regions generating IEDs (defining the "irritative zone") are not necessarily equivalent to the seizure onset zone. Focal seizures are dynamic processes originating from one or several brain regions (that generate fast oscillations and are called the epileptogenic zone) before spreading to other structures (that generate lower frequency oscillations and are called the propagation zone). Several factors limit the expression of seizures on scalp EEG, such as the area involved, degree of synchronization, and depth of the cortical generators. Different scalp EEG seizure onset patterns may be observed: fast discharge, background flattening, rhythmic spikes, sinusoidal discharge, or sharp activity. However, to a large extent EEG changes are linked to seizure propagation. Finally, in the context of presurgical evaluation, the combination of interictal and ictal EEG features is crucial to provide an optimal hypothesis concerning the epileptogenic zone.

Fast monitoring of epileptic seizures using recurrence time statistics of electroencephalography

Epilepsy is a relatively common brain disorder which may be very debilitating. Currently, determination of epileptic seizures often involves tedious, time-consuming visual inspection of electroencephalography (EEG) data by medical experts. To better monitor seizures and make medications more effective, we propose a recurrence time based approach to characterize brain electrical activity. Recurrence times have a number of distinguished properties that make it very effective for forewarning epileptic seizures as well as studying propagation of seizures: (1) recurrence times amount to periods of periodic signals, (2) recurrence times are closely related to information dimension, Lyapunov exponent, and Kolmogorov entropy of chaotic signals, (3) recurrence times embody Shannon and Renyi entropies of random fields, and (4) recurrence times can readily detect bifurcation-like transitions in dynamical systems. In particular, property (4) dictates that unlike many other non-linear methods, recurrence time method does not require the EEG data be chaotic and/or stationary. Moreover, the method only contains a few parameters that are largely signal-independent, and hence, is very easy to use. The method is also very fast—it is fast enough to on-line process multi-channel EEG data with a typical PC. Therefore, it has the potential to be an excellent candidate for real-time monitoring of epileptic seizures in a clinical setting.

Changing patterns of propagation in a super-refractory status of the temporal lobe. Over 900 seizures recorded over nearly one year

Objective

Our goals were to study the propagation models in a situation of persistent temporal epileptic seizures with varying degrees of bitemporal excitability and to analyze which propagation models were found at times of high temporal excitability and which occurred with lower levels of excitability.

Methods

A patient with super-refractory status arising from the temporal lobes was studied daily using video-electroencephalography (VEEG), with a large number of electroclinical seizures recorded. The analysis focused on the method and type of seizure propagation and classified them either according to the propagation models described in the literature or as undetermined.

Results

Video-EEG monitoring was carried out daily for 310 days. A total of 990 electroclinical seizures were recorded; 135 seizures were recorded during the first week, and 523 were recorded in the first month. From the beginning, the interictal recording showed independent discharges over both temporal lobes. The seizures showed independent onset in both temporal lobes. During periods of the highest number of seizures, certain models of propagation begin to predominate through switch of lateralization, temporal asynchrony, early remote propagation, total contralateral propagation, seizures with nonlocalized onset, or models that are difficult to classify. Conversely, when the condition was brought relatively under control, we observed fewer propagation models with predominantly simple patterns: only hemispheric propagation or graduated sequential propagation with a few nonlateralized onset seizures.

Conclusions

Upon analyzing the seizures, we found that the propagation models vary as the status evolved, with the change reflecting the degree of excitability in the mesial temporal–limbic network at a given time. In clinical practice, these changes in propagation models are more likely to be observed in temporal status that extends over time and with an onset of the seizures in both temporal lobes.

Significance

The analysis of the propagation models may provide information about the excitability of the mesial temporal–limbic network.

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Studies with ictal scalp electroencephalography (ISE) have identified different propagation models.

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Propagation models change, probably related to the excitability at different times of the mesial temporal–limbic network.

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During periods of poor seizure control, complex propagation models predominate.

Evaluation of corpus callosum morphometry in patients with mesial temporal lobe epilepsy with hippocampal sclerosis

PURPOSE

The purpose of the study was to evaluate the effects of mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) on corpus callosum (CC) morphometry in large and pathologically proven MTLE-HS patients.

METHODS

We measured certain CC dimensions in 103 patients and 33 healthy controls using in vivo magnetic resonance imaging. In particular, we compared the two groups in relation to the clinical (localization of the HS, duration of epilepsy, frequency of seizures and length of seizures) and demographical (age, gender, handedness) features. Students' t test, two-way ANOVA and Spearman test were used for statistical analysis.

RESULTS

There was no significant difference between CC morphometry with respect to age and handedness among patients. The differences between the genders, however, were significant favouring longer diameters in males. We found significant decrease in the dimensions of the genu, body, isthmus and splenium of the CC in the MTLE-HS group, but there was no reduction in the size of the rostrum.

CONCLUSIONS

This general reduction in the size of the CC except for the rostrum was thought to be the result of cortical atrophy secondary to the disease. Concerning the preserved rostral part of the CC, it was thought that the fibers of the frontal lobe pass through different pathways than the tracts in the rostrum.

Role of electroencephalography in presurgical evaluation of temporal lobe epilepsy

Surgery remains a therapeutic option for patients with medically refractory epilepsy. Comprehensive presurgical evaluation includes electroencephalography (EEG) and video EEG in identifying patients who are likely to benefit from surgery. Here, we discuss in detail the utility of EEG in presurgical evaluation of patients with temporal lobe epilepsy along with illustrative cases.

Complexity measures of brain wave dynamics

To understand the nature of brain dynamics as well as to develop novel methods for the diagnosis of brain pathologies, recently, a number of complexity measures from information theory, chaos theory, and random fractal theory have been applied to analyze the EEG data. These measures are crucial in quantifying the key notions of neurodynamics, including determinism, stochasticity, causation, and correlations. Finding and understanding the relations among these complexity measures is thus an important issue. However, this is a difficult task, since the foundations of information theory, chaos theory, and random fractal theory are very different. To gain significant insights into this issue, we carry out a comprehensive comparison study of major complexity measures for EEG signals. We find that the variations of commonly used complexity measures with time are either similar or reciprocal. While many of these relations are difficult to explain intuitively, all of them can be readily understood by relating these measures to the values of a multiscale complexity measure, the scale-dependent Lyapunov exponent, at specific scales. We further discuss how better indicators for epileptic seizures can be constructed.