Epileptic neuronal networks: methods of identification and clinical relevance

The main objective of this paper is to examine evidence for the concept that epileptic activity should be envisaged in terms of functional connectivity and dynamics of neuronal networks. Basic concepts regarding structure and dynamics of neuronal networks are briefly described. Particular attention is given to approaches that are derived, or related, to the concept of causality, as formulated by Granger. Linear and non-linear methodologies aiming at characterizing the dynamics of neuronal networks applied to EEG/MEG and combined EEG/fMRI signals in epilepsy are critically reviewed. The relevance of functional dynamical analysis of neuronal networks with respect to clinical queries in focal cortical dysplasias, temporal lobe epilepsies, and “generalized” epilepsies is emphasized. In the light of the concepts of epileptic neuronal networks, and recent experimental findings, the dichotomic classification in focal and generalized epilepsy is re-evaluated. It is proposed that so-called “generalized epilepsies,” such as absence seizures, are actually fast spreading epilepsies, the onset of which can be tracked down to particular neuronal networks using appropriate network analysis. Finally new approaches to delineate epileptogenic networks are discussed

Dynamic brain network states in human generalized spike-wave discharges.

Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level-dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus (P < 0.01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections (P < 0.01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 s after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in non-discharge epochs in patients) from 16 s to 10 s before discharges, then ramped up steeply to a significantly high level of synchrony 2 s before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls (P = 0.004). During 6 s prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 min) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events

Nonlinear EEG biomarker profiles for autism and absence epilepsy

Background Although autism and epilepsy are considered to be different disorders, epileptiform EEG activity is common in people with autism even when overt seizures are not present. The relatively high comorbidity between autism and all epilepsy syndromes suggests the possibility of common underlying neurophysiological mechanisms. Although many different epilepsies may be comorbid with autism, absence epilepsy is a generalized epilepsy syndrome with seizures that appear as staring spells, with no motor signs and no focal lesions, making it more difficult to diagnose. Application of nonlinear methods for EEG signal analysis may enable characterization of brain activity that can help to delineate neurophysiological commonalities and differences between autism and epilepsy. Multiscale entropy and recurrence quantitative analysis (RQA) were computed from EEG signals derived from children with autism or absence epilepsy and compared with the goal of finding significant and potentially clinically useful biomarkers neurophysiological differences between these two childhood disorders. Methods Multiscale entropy and a multiscale version of RQA were computed from EEG data obtained from 92 children were collected in two different settings at Boston Children’s Hospital. Short segments of alert resting state EEG were selected for analysis. A complexity index derived from entropy and RQA methods was computed from each of 19 standard EEG channels for all subjects using publicly available software. Statistical comparisons were made between the groups. Machine learning classifiers were also used to determine which derived features were most significantly different among the groups, and to determine classification specificity and sensitivity. Results Significant differences were found between absence, autism, and control groups in a number of different scalp locations and the values of complexity index. Autism values appeared to be intermediate between epilepsy and control in many locations, and differences between controls and absence patients were more widely distributed across scalp locations. Classification algorithms were able to distinguish absence epilepsy and autism cases from controls with high (\u3e95%) accuracy. Importantly, two independent control groups, although they were derived from different settings and with different equipment were statistically indistinguishable. Conclusions Signficant neurophysiological differences were found between absence, autism, and control cases. In most scalp regions, autism values were intermediate between the control values and absence values, suggesting several future research studies. Nonlinear EEG signal analysis, together with classification methods, may provide complementary information to visual EEG analysis and clinical assessment in epilepsy and autism, and may provide useful information for research on pediatric neurodevelopmental and neurological disorders. Additional research may enable neurophysiological biomarker profiles to be derived from these techniques for clinical use

Controversies in epilepsy: Debates held during the Fourth International Workshop on Seizure Prediction

Debates on six controversial topics were held during the Fourth International Workshop on Seizure Prediction (IWSP4) convened in Kansas City, KS, USA, July 4–7, 2009. The topics were (1) Ictogenesis: Focus versus Network? (2) Spikes and Seizures: Step-relatives or Siblings? (3) Ictogenesis: A Result of Hyposynchrony? (4) Can Focal Seizures Be Caused by Excessive Inhibition? (5) Do High-Frequency Oscillations Provide Relevant Independent Information? (6) Phase Synchronization: Is It Worthwhile as Measured? This article, written by the IWSP4 organizing committee and the debaters, summarizes the arguments presented during the debates

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well