Regional coherence and the transfer of ictal activity during seizure onset in the medial temporal lobe

doi:10.1016/0013-4694(92)90046-K

Electroencephalography and Clinical Neurophysiology

Volume 82, Issue 6, June 1992, Pages 415-422

https://doi.org/10.1016/0013-4694(92)90046-K Get rights and content

Abstract

Epileptiform activity requires that large aggregates of neurons act synchronously. The process of neuronal synchronization during seizure onset was studied in the human medial temporal lobe by measuring the coherence of EEG activity. Records were obtained from 10 consecutive patients with hippocampal depth electrodes being evaluated for possible resective surgery. Coherence and phase spectra were calculated from all possible pairs of contacts in the medial temporal lobe of seizure onset using the method of Gotman applied to successive 6.4 sec epochs. Signals derived from adjacent contacts within definable brain regions were coherent during both the preictal and ictal period. Transitions in the level of coherence were measured between contacts presumed to span the boundaries of these regions. Time delays were measured eaily in the development of the seizure discharge but were not sustained. These time delays spanned the borders of regions of differing coherence, especially in the posterior hippocampus, and were interpreted to represent a transient increase in the functional linkage between structural elements. We conclude that the process of neuronal entrainment during seizure onset involves a transient interaction between brain regions but the maintenance of this interaction is not required for sustained seizure activity.

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Cited by (91)

Epilepsy as a manifestation of a multistate network of oscillatory systems
2019, Neurobiology of Disease
Citation Excerpt :
These activities are compatible with the behaviour of coupled neuronal network oscillators. Duckrow and Spencer (1992), analyzed intracranial recordings from hippocampal depth electrodes implanted in epilepsy patients being evaluated for possible resective surgery, measuring regional coherence and the transfer of ictal activity. They concluded that “the process of neuronal entrainment during seizure onset involves a transient interaction between brain regions, but the maintenance of this interaction is not required for sustained seizure activity”.
The human brain, largely accepted as the most complex biological system known, is still far from being understood in its parts or as a whole. More specifically, biological mechanisms of epileptic states and state transitions are not well understood. Here, we explore the concept of the epilepsy as a manifestation of a multistate network composed of coupled oscillatory units. We also propose that functional coupling between neuroglial elements is a dynamic process, characterized by temporal changes both at short and long time scales. We review various experimental and modelling data suggesting that epilepsy is a pathological manifestation of such a multistate network – both when viewed as a coupled oscillatory network, and as a system of multistate stable state attractors. Based on a coupled oscillators model, we propose a significant role for glial cells in modulating hyperexcitability of the neuroglial networks of the brain. Also, using these concepts, we explain a number of observable phenomena such as propagation patterns of bursts within a seizure in the isolated intact hippocampus in vitro, postictal generalized suppression in human encephalographic seizure data, and changes in seizure susceptibility in epileptic patients. Based on our conceptual model we propose potential clinical applications to estimate brain closeness to ictal transition by means of active perturbations and passive measures during on-going activity.
The role of sub-hippocampal versus hippocampal regions in bitemporal lobe epilepsies
2016, Clinical Neurophysiology
We aimed at better delineating the functional anatomical organization of bitemporal lobe epilepsy.
We studied the epileptogenic zone (EZ) by quantifying the epileptogenicity of brain structures explored by depth electrodes in patients investigated by stereoelectroencephalography (SEEG). We compared 15 patients with bilateral mesial temporal lobe epilepsy (BTLE) and 15 patients with unilateral mesial temporal lobe epilepsy (UTLE). This quantification was performed using the ‘Epileptogenicity Index’ (EI).
Age at epilepsy onset, and epilepsy duration, were not statistically different in both groups. UTLE patients more frequently displayed maximal epileptogenicity in hippocampal structures, whereas BTLE patients had maximal values in subhippocampal areas (entorhinal cortex, temporal pole, parahippocampal cortex).
Our results suggest different organization of the EZ in the two groups.
BTLE was associated with more involvement of subhippocampal regions, a result in agreement with known anatomical connections between the two temporal lobes.
Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine
2016, Journal of Neuroscience Methods
Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this research is to develop and present a novel classification framework that is utilised to discriminate interictal and preictal brain activities via quantitative analysis of electroencephalogram (EEG) recordings.
Sample entropy-based features were extracted in parallel from 6 intracranial EEG channels, and then these features were fed to the extreme learning machine model for classification. Performance was evaluated on the basis of sensitivity and specificity and validation was performed using stratified cross-validation approach.
The proposed method can correctly distinguish interictal and preictal EEGs with a sensitivity of 86.75% and a specificity of 83.80%, on average, across 21 patients and 6 epileptic seizure origins.
Compared with traditional variance-based feature extraction, the proposed SampEn-based feature extraction method not only shows a significant improvement in the accuracy, but also has higher classification robustness and stability in terms of the much lower standard errors of classification accuracies across different evaluation periods. In addition, the proposed classification framework runs around 20 times faster than the support vector machine model during testing.
The high accuracy and efficiency of the proposed method makes it feasible to extend it to the development of a real-time EEG-based brain monitoring system for epileptic seizure prediction.
Cortical connectivity in fronto-temporal focal epilepsy from EEG analysis: A study via graph theory
2015, Clinical Neurophysiology
Citation Excerpt :
Within this theoretical framework, focal epilepsy is increasingly seen as a ‘network disorder’ (Kramer and Cash, 2012; Richardson, 2012; Engel et al., 2013). During the genesis of partial seizures (particularly temporal lobe seizures), it has been observed that the EEG rhythms from involved brain networks are characterized by increased synchronization culminating at the end with a clinical seizure (Lieb et al., 1987; Duckrow and Spencer, 1992; Gotman and Levtova, 1996; Le Van et al., 1998; Bartolomei et al., 2001, 2004, 2005; Schindler et al., 2007). In contrast, few studies have investigated network properties and functioning during the interictal period.
It is believed that effective connectivity and optimal network structure are essential for proper information processing in the brain. Indeed, functional abnormalities of the brain are found to be associated with pathological changes in connectivity and network structures. The aim of the present study was to explore the interictal network properties of EEG signals from temporal lobe structures in the context of fronto-temporal lobe epilepsy.
To complete this aim, the graph characteristics of the EEG data of 17 patients suffering from focal epilepsy of the fronto-temporal type, recorded during interictal periods, were examined and compared in terms of the affected versus the unaffected hemispheres. EEG connectivity analysis was performed using eLORETA software in 15 fronto-temporal regions (Brodmann Areas BAs 8, 9, 10, 11, 20, 21, 22, 37, 38, 41, 42, 44, 45, 46, 47) on both affected and unaffected hemispheres.
The evaluation of the graph analysis parameters, such as ‘global’ (characteristic path length) and ‘local’ connectivity (clustering coefficient) showed a statistically significant interaction among side (affected and unaffected hemisphere) and Band (delta, theta, alpha, beta, gamma). Duncan post hoc testing showed an increase of the path length in the alpha band in the affected hemisphere with respect to the unaffected one, as evaluated by an inter-hemispheric marker. The affected hemisphere also showed higher values of local connectivity in the alpha band. In general, an increase of local and global graph theory parameters in the alpha band was found in the affected hemisphere. It was also demonstrated that these effects were more evident in drug-free patients than in those undergoing pharmacological therapy.
The increased measures in the affected hemisphere of both functional local segregation and global integration could result from the combination of overlapping mechanisms, including reactive neuroplastic changes seeking to maintain constant integration and segregation properties.
This reactive neuroplastic mechanism seeking to maintain constant integration and segregation properties seems to be more evident in the absence of antiepileptic treatment.
Interictal network properties in mesial temporal lobe epilepsy: A graph theoretical study from intracerebral recordings
2013, Clinical Neurophysiology
Citation Excerpt :
In epilepsy, studies have focused on the characterization of seizure generation and network changes in patient with drug-resistant partial seizures. During the genesis of partial seizures (in particular temporal lobe seizures), it has been observed that involved brain networks are characterized by increased synchronization culminating at the end of the seizure (Gotman and Levtova, 1996, Duckrow and Spencer, 1992; Lieb et al., 1987; Le Van Quyen et al., 1998; Bartolomei et al., 2001, 2004, 2005, 1999; Schindler et al., 2007). In contrast, few studies have investigated network properties and functioning during the interictal period.
Graph theoretical analysis of functional connectivity data has demonstrated a small-world topology of brain networks. There is increasing evidence that the topology of brain networks is changed in epilepsy. Here we investigated the basal properties of epileptogenic networks by applying graph analysis to intracerebral EEG recordings of patients presenting with drug-resistant partial epilepsies during the interictal period.
Interictal EEG activity was recorded in mesial temporal lobe of 11 patients with mesial temporal lobe epilepsy (MTLE group) and compared with a “control” group of 8 patients having neocortical epilepsies (non MTLE group) in whom depth-EEG recordings eventually showed an ictal onset outside the MTL structures. Synchronization likelihood (SL) was calculated between selected intracerebral electrodes contacts to obtain SL-weighted graphs. Mean normalized clustering index, average path length and small world index S were calculated to characterize network organization.
Broadband SL values were higher in the MTLE group. Although a small-world pattern was found in the two groups, normalized clustering index and to a lesser extend average path length were higher in the MTLE group.
We demonstrated a trend toward a more regular (less random) configuration of interictal epileptogenic networks. In addition S index was found to correlate with epilepsy duration.
These topological alterations might be a surrogate marker of human focal epilepsy and disclose some changes over time.
Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG
2012, Clinical Neurophysiology
Citation Excerpt :
Early work (Viglione and Walsh, 1975) showed a gradual change of EEG patterns in the minutes before seizure onset in a few samples. Further studies were carried out mainly with spectral analysis in an attempt to identify seizure precursors (Wieser and Preictal, 1989; Duckrow and Spencer, 1992; Osorio et al., 1998; Salant et al., 1998). Characteristic changes were found in the seconds preceding onset.
Identification of consistent distinguishing features between preictal and interictal periods in the EEG is an essential step towards performing seizure prediction. We propose a novel method to separate preictal and interictal states based on the analysis of the high frequency activity of intracerebral EEGs in patients with mesial temporal lobe epilepsy.
Wavelet energy and entropy were computed in sliding window fashion from preictal and interictal epochs. A comparison of their organization in a 2 dimensional space was carried out using three features quantifying the similarities between their underlying states and a reference state. A discriminant analysis was then used in the features space to classify epochs. Performance was assessed based on sensitivity and false positive rates and validation was performed using a bootstrapping approach.
Preictal and interictal epochs were discriminable in most patients on a subset of channels that were found to be close or within the seizure onset zone.
Preictal and interictal states were separable using measures of similarity with the reference state. Discriminability varies with frequency bands.
This method is useful to discriminate preictal from interictal states in intracerebral EEGs and could be useful for seizure prediction.

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Regional coherence and the transfer of ictal activity during seizure onset in the medial temporal lobe

Abstract

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Neuroscience

Brain Res.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.