Low entropy of interictal gamma oscillations is a biomarker of the seizure onset zone in focal cortical dysplasia type II

Interictal gamma oscillation regularity analysis and susceptibility-weighted imaging on focal epilepsy cases with alcohol use disorders

Background

There has been no clear consensus on the clinical markers to distinguish alcohol-related seizures (ARSs) from epileptic seizures. We have reported the usefulness of gamma oscillation (30-70 Hz) regularity (GOR) analysis using interictal electroencephalography (EEG) data to evaluate epileptogenic focus. We conducted interictal GOR analysis using scalp EEG and susceptibility-weighted imaging (SWI) to visualize the epileptogenic focus in two cases initially suspected to have ARS.

Case Description

In each case, a significantly high GOR area suggestive of epileptogenic focus was detected and that area was consistent with that where SWI showed hemosiderin deposit. In one patient, seizures were well controlled with the introduction of anti-seizure medication (ASM). In another patient, ASM was introduced but is refractory, and epilepsy surgery is being considered in the future.

Conclusion

The interictal GOR analysis and SWI can successfully contribute to identify the patients suspected to have ARS who may have epileptogenic focus and can be treated with ASM and epilepsy surgery.

Connectivity of high-frequency bursts as SOZ localization biomarker

For patients with refractory epilepsy, the seizure onset zone (SOZ) plays an essential role in determining the specific regions of the brain that will be surgically resected. High-frequency oscillations (HFOs) and connectivity-based approaches have been identified among the potential biomarkers to localize the SOZ. However, there is no consensus on how connectivity between HFO events should be estimated, nor on its subject-specific short-term reliability. Therefore, we propose the channel-level connectivity dispersion (CLCD) as a metric to quantify the variability in synchronization between individual electrodes and to identify clusters of electrodes with abnormal synchronization, which we hypothesize to be associated with the SOZ. In addition, we developed a specialized filtering method that reduces oscillatory components caused by filtering broadband artifacts, such as sharp transients, spikes, or direct current shifts. Our connectivity estimates are therefore robust to the presence of these waveforms. To calculate our metric, we start by creating binary signals indicating the presence of high-frequency bursts in each channel, from which we calculate the pairwise connectivity between channels. Then, the CLCD is calculated by combining the connectivity matrices and measuring the variability in each electrode's combined connectivity values. We test our method using two independent open-access datasets comprising intracranial electroencephalography signals from 89 to 15 patients with refractory epilepsy, respectively. Recordings in these datasets were sampled at approximately 1000 Hz, and our proposed CLCDs were estimated in the ripple band (80-200 Hz). Across all patients in the first dataset, the average ROC-AUC was 0.73, and the average Cohen's d was 1.05, while in the second dataset, the average ROC-AUC was 0.78 and Cohen's d was 1.07. On average, SOZ channels had lower CLCD values than non-SOZ channels. Furthermore, based on the second dataset, which includes surgical outcomes (Engel I-IV), our analysis suggested that higher CLCD interquartile (as a measure of CLCD distribution spread) is associated with favorable outcomes (Engel I). This suggests that CLCD could significantly assist in identifying SOZ clusters and, therefore, provide an additional tool in surgical planning for epilepsy patients.

Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach

OBJECTIVE

To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data.

METHODS

We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization.

RESULTS

FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes.

SIGNIFICANCE

We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

Generalisability of epileptiform patterns across time and patients

The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.

Rodents' visual gamma as a biomarker of pathological neural conditions

Neural gamma oscillations (indicatively 30-100 Hz) are ubiquitous: they are associated with a broad range of functions in multiple cortical areas and across many animal species. Experimental and computational works established gamma rhythms as a global emergent property of neuronal networks generated by the balanced and coordinated interaction of excitation and inhibition. Coherently, gamma activity is strongly influenced by the alterations of synaptic dynamics which are often associated with pathological neural dysfunctions. We argue therefore that these oscillations are an optimal biomarker for probing the mechanism of cortical dysfunctions. Gamma oscillations are also highly sensitive to external stimuli in sensory cortices, especially the primary visual cortex (V1), where the stimulus dependence of gamma oscillations has been thoroughly investigated. Gamma manipulation by visual stimuli tuning is particularly easy in rodents, which have become a standard animal model for investigating the effects of network alterations on gamma oscillations. Overall, gamma in the rodents' visual cortex offers an accessible probe on dysfunctional information processing in pathological conditions. Beyond vision-related dysfunctions, alterations of gamma oscillations in rodents were indeed also reported in neural deficits such as migraine, epilepsy and neurodegenerative or neuropsychiatric conditions such as Alzheimer's, schizophrenia and autism spectrum disorders. Altogether, the connections between visual cortical gamma activity and physio-pathological conditions in rodent models underscore the potential of gamma oscillations as markers of neuronal (dys)functioning.

Utilizing Excitatory and Inhibitory Activity Derived from Interictal Intracranial Electroencephalography as Potential Biomarkers for Epileptogenicity

Epileptogenic zones (EZs), where epileptic seizures cease after resection, are localized by assessing the seizure-onset zone using ictal electroencephalography (EEG). Owing to the difficulty in capturing unpredictable seizures, biomarkers capable of identifying EZs from interictal EEG are anticipated. Recent studies using intracranial EEG have identified several potential candidate biomarkers for epileptogenicity. High-frequency oscillation (HFO) was initially expected to be a robust biomarker of abnormal excitatory activity in the ictogenic region. However, HFO-guided resection failed to improve seizure prognosis. Meanwhile, the regularity of low-gamma oscillations (30-80 Hz) indicates inhibitory interneurons' hypersynchronization, which could be used to localize the EZ. Besides resting-state EEG assessments, evoked potentials elicited by single-pulse electrical stimulation, such as corticocortical evoked potentials (CCEP), became valuable tools for assessing epileptogenic regions. CCEP responses recorded in the cortex remote from the stimulation site indicate functional connectivity, revealing increased internal connectivity within the ictogenic region and elevated inhibitory input from the non-involved regions to the ictogenic region. Conversely, large responses close to the stimulation site reflect local excitability, manifesting as an increased N1 amplitude and overriding HFO. Further research is required to establish whether these novel electrophysiological methods, either individually or in combination, can function as robust biomarkers of epileptogenicity and hold promise for improving seizure prognosis.

Relative entropy is an easy-to-use invasive electroencephalographic biomarker of the epileptogenic zone

OBJECTIVE

High-frequency oscillations are considered among the most promising interictal biomarkers of the epileptogenic zone in patients suffering from pharmacoresistant focal epilepsy. However, there is no clear definition of pathological high-frequency oscillations, and the existing detectors vary in methodology, performance, and computational costs. This study proposes relative entropy as an easy-to-use novel interictal biomarker of the epileptic tissue.

METHODS

We evaluated relative entropy and high-frequency oscillation biomarkers on intracranial electroencephalographic data from 39 patients with seizure-free postoperative outcome (Engel Ia) from three institutions. We tested their capability to localize the epileptogenic zone, defined as resected contacts located in the seizure onset zone. The performance was compared using areas under the receiver operating curves (AUROCs) and precision-recall curves. Then we tested whether a universal threshold can be used to delineate the epileptogenic zone across patients from different institutions.

RESULTS

Relative entropy in the ripple band (80-250 Hz) achieved an average AUROC of .85. The normalized high-frequency oscillation rate in the ripple band showed an identical AUROC of .85. In contrast to high-frequency oscillations, relative entropy did not require any patient-level normalization and was easy and fast to calculate due to its clear and straightforward definition. One threshold could be set across different patients and institutions, because relative entropy is independent of signal amplitude and sampling frequency.

SIGNIFICANCE

Although both relative entropy and high-frequency oscillations have a similar performance, relative entropy has significant advantages such as straightforward definition, computational speed, and universal interpatient threshold, making it an easy-to-use promising biomarker of the epileptogenic zone.

Interictal High Gamma Oscillation Regularity as a Marker for Presurgical Epileptogenic Zone Localization

BACKGROUND

To ensure that epilepsy surgery is effective, accurate presurgical localization of the epileptogenic zone is essential. Our previous reports demonstrated that interictal high gamma oscillation (30-70 Hz) regularity (GOR) on intracranial electroencephalograms is related to epileptogenicity.

OBJECTIVE

To examine whether preoperative GOR analysis with interictal high-density electroencephalography (HD-EEG) improves the accuracy of epileptogenic focus localization and enhances postoperative seizure control.

METHODS

We calculated GOR from 20 seconds of HD-EEG data for 21 patients with refractory focal epilepsy (4 with nonlesional temporal lobe epilepsy) scheduled for epilepsy surgery. Low-resolution brain electromagnetic tomography was used to analyze the high GOR source. To validate our findings, we made comparisons with other conventional localization methods and postoperative seizure outcomes.

RESULTS

In all patients, the areas of interictal high GOR were identified and resected. All patients were seizure-free after the operation. The concordance between the results of interictal high GOR on HD-EEG and those of source estimation of interictal discharge was fully overlapping in 10 cases, partially overlapping in 8 cases, and discordant in 3 cases. The concordance between the results of interictal high GOR on HD-EEG and those of interictal 123 I-iomazenil single-photon emission computed tomography was fully overlapping in 8 cases, partially overlapping in 11 cases, and discordant in 2 cases. In 4 patients with nonlesional temporal lobe epilepsy, the interictal high GOR on HD-EEG was useful in confirming the epileptogenic zone.

CONCLUSION

The interictal high GOR on HD-EEG is an excellent marker for presurgical epileptogenic zone localization.

Visualization of ictal networks using gamma oscillation regularity correlation analysis in focal motor epilepsy: Illustrative cases

Background

Focal motor epilepsy is difficult to localize within the epileptogenic zone because ictal activity quickly spreads to the motor cortex through ictal networks. We previously reported the usefulness of gamma oscillation (30-70 Hz) regularity (GOR) correlation analysis using interictal electrocorticographic (ECoG) data to depict epileptogenic networks. We conducted GOR correlation analysis using ictal ECoG data to visualize the ictal networks originating from the epileptogenic zone in two cases - a 26-year-old woman with negative motor seizures and a 53-year-old man with supplementary motor area (SMA) seizures.

Case Description

In both cases, we captured several habitual seizures during monitoring after subdural electrode implantation and performed GOR correlation analysis using ictal ECoG data. A significantly high GOR suggestive of epileptogenicity was identified in the SMA ipsilateral to the lesions, which were connected to the motor cortex through supposed ictal networks. We resected the high GOR locations in the SMA and the patients' previously identified tumors were removed. The patients were seizure-free without any neurological deficits after surgery.

Conclusion

The GOR correlation analysis using ictal ECoG data could be a powerful tool for visualizing ictal networks in focal motor epilepsy.

Low EEG Gamma Entropy and Glucose Hypometabolism After Corpus Callosotomy Predicts Seizure Outcome After Subsequent Surgery

Background

Patients with generalized epilepsy who had lateralized EEG abnormalities after corpus callosotomy (CC) occasionally undergo subsequent surgeries to control intractable epilepsy.

Objectives

This study evaluated retrospectively the combination of EEG multiscale entropy (MSE) and FDG-PET for identifying lateralization of the epileptogenic zone after CC.

Methods

This study included 14 patients with pharmacoresistant epilepsy who underwent curative epilepsy surgery after CC. Interictal scalp EEG and FDG-PET obtained after CC were investigated to determine (1) whether the MSE calculated from the EEG and FDG-PET findings was lateralized to the surgical side, and (2) whether the lateralization was associated with seizure outcomes.

Results

Seizure reduction rate was higher in patients with lateralized findings to the surgical side than those without (MSE: p &lt; 0.05, FDG-PET: p &lt; 0.05, both: p &lt; 0.01). Seizure free rate was higher in patients with lateralized findings in both MSE and FDG-PET than in those without (p &lt; 0.05).

Conclusions

This study demonstrated that patients with lateralization of MSE and FDG-PET to the surgical side had better seizure outcomes. The combination of MSE and conventional FDG-PET may help to select surgical candidates for additional surgery after CC with good postoperative seizure outcomes.

Measuring the effects of sleep on epileptogenicity with multifrequency entropy

OBJECTIVE

We demonstrate that multifrequency entropy gives insight into the relationship between epileptogenicity and sleep, and forms the basis for an improved measure of medical assessment of sleep impairment in epilepsy patients.

METHODS

Multifrequency entropy was computed from electroencephalography measurements taken from 31 children with Benign Epilepsy with Centrotemporal Spikes and 31 non-epileptic controls while awake and during sleep. Values were compared in the epileptic zone and away from the epileptic zone in various sleep stages.

RESULTS

We find that (I) in lower frequencies, multifrequency entropy decreases during non-rapid eye movement sleep stages when compared with wakefulness in a general population of pediatric patients, (II) patients with Benign Epilepsy with Centrotemporal Spikes had lower multifrequency entropy across stages of sleep and wakefulness, and (III) the epileptic regions of the brain exhibit lower multifrequency entropy patterns than the rest of the brain in epilepsy patients.

CONCLUSIONS

Our results show that multifrequency entropy decreases during sleep, particularly sleep stage 2, confirming, in a pediatric population, an association between sleep, lower multifrequency entropy, and increased likelihood of seizure.

SIGNIFICANCE

We observed a correlation between lowered multifrequency entropy and increased epileptogenicity that lays preliminary groundwork for the detection of a digital biomarker for epileptogenicity.

Intraoperative epileptogenic network visualization using gamma oscillation regularity correlation analysis in epilepsy surgery

Background:

We have recently demonstrated that gamma oscillation (30–70 Hz) regularity (GOR) analysis accurately localized epileptogenic focus using intraoperative electrocorticographic data. In this report, we assessed whether GOR correlation analysis could depict epileptogenic networks intraoperatively. Dual foci in temporal lobe epilepsy without hippocampal structural abnormalities are difficult to diagnose. Using our GOR correlation analysis, we aimed to intraoperatively visualize such dual foci and epileptogenic networks.

Case Description:

A 56-year-old man suffered from pharmacoresistant focal impaired awareness seizures. Magnetic resonance imaging demonstrated an 8 × 12-mm cavernoma in the right inferior temporal gyrus without any structural changes in the hippocampus. Since ictal semiology indicated a high probability of epileptogenicity in the right hippocampus, we reached the hippocampus using a transsylvian approach and assessed intraoperative GOR correlation analysis in the lateral temporal lobe where the cavernoma was located and the hippocampus, simultaneously. High GORs suggestive of epileptogenicity were identified in both the lateral temporal lobe and the hippocampus. Furthermore, they were connected using GOR correlation networks. When the high GOR locations in the lateral temporal lobe and the cavernoma were removed, high GORs and those networks were found within the hippocampus only. After additional hippocampal transection, high GORs and these networks were absent. The patient became seizure-free after the surgery.

Conclusion:

Our GOR correlation analysis may be a powerful tool for intraoperative evaluation of epileptogenic networks in epilepsy surgery.

Transitional pattern as a potential marker of epileptogenic zone in focal epilepsy - Clinical observations from intracerebral recordings

OBJECTIVES

To investigate the characteristics of transition from interictal to ictal phase in intracranial recordings and further to determine the potential marker of epileptogenic zone.

METHODS

Eighteen patients with drug-refractory epilepsy who underwent stereo-electroencephalography (SEEG) evaluation and subsequent resective surgery were included. All patients were seizure-free post-operatively. The recorded seizures were retrospectively reviewed and time episodes including 5 min before electrographic onset were selected for further analysis to verify the presence of a transitional pattern in the transitional phase, which was distinct from interictal background and ictal onset. Besides, the components of transitional patterns which characterized by different pathological waveforms were identified by visual analysis and time-frequency analysis. The prevalence of transitional patterns between resection and non-resection, lesion and non-lesion sites were compared. In addition, the association between transitional patterns and types of epilepsy was explored.

RESULTS

Six transitional patterns characterized by different combinations of multiple pathological waveforms by visual analysis combined with time-frequency analysis were identified: spike/spike-waves/polyspikes; spike superimposed by HFOs; spike superimposed by gamma oscillations; spike followed by suppression; spike superimposed by HFOs and followed by suppression; and spike superimposed by gamma oscillations and followed by suppression. A higher prevalence of transitional patterns in resection than non-resection (p < 0.001) and in lesion than non-lesion contacts (p < 0.001). The pattern characterized by spike superimposed by HFOs and followed by suppression was more prevalent in resection than non-resection sites (p = 0.004). Further, there was an association between the complexity of transitional patterns and the location of contacts. Patterns with higher degree of complexity were more likely to be inside the resection area (p = 0.035). Besides, we found the pattern with spike superimposed by HFOs was associated more with limbic epilepsy than neocortical epilepsy (p < 0.001), whereas another 3 patterns, spike superimposed by gamma oscillation, spike followed by suppression and spike combined with HFOs and suppression, were observed more frequently in neocortical epilepsy than limbic epilepsy (p = 0.018, 0.011 and < 0.001, respectively).

CONCLUSION

Transitional patterns from interictal to ictal state were characterized by different combinations of multiple pathological waveforms, which may be a potential marker of epileptogenic zone. Our findings support that the interaction of different neuronal oscillations or waveforms generated by different neuronal populations may be the potential mechanism of seizure generation.

Epileptogenic zone localization using intraoperative gamma oscillation regularity analysis in epilepsy surgery for cavernomas: patient series

BACKGROUND

In epilepsy surgery for cavernoma with intractable focal epilepsy, removal of the cavernoma with its surrounding hemosiderin deposition and other extended epileptogenic zone has been shown to improve postsurgical seizures. However, there has been no significant association between such an epileptogenic zone and intraoperative electrocorticography (ECoG) findings. The authors recently demonstrated that high regular gamma oscillation (30–70 Hz) regularity (GOR) significantly correlates with epileptogenicity.

OBSERVATIONS

The authors evaluated the utility of intraoperative GOR analysis in epilepsy surgery for cavernomas. The authors also analyzed intraoperative ECoG data from 6 patients with cavernomas. The GOR was calculated using a sample entropy algorithm. In 4 patients, the GOR was significantly high in the area with the pathological hemosiderin deposition. In 2 patients with temporal cavernoma, the GOR was significantly high in both the hippocampus and the area with the pathological hemosiderin deposition. ECoG showed no obvious epileptic waveforms in 3 patients, whereas extensive spikes were observed in 3 patients. All patients underwent cavernoma removal plus resection of the area with significantly high GOR. The 2 patients with temporal cavernomas underwent additional hippocampal transection. All patients were seizure free after surgery.

LESSONS

The high GOR may be a novel intraoperative marker of the epileptogenic zone in epilepsy surgery for cavernomas.

Alterations of Neuronal Dynamics as a Mechanism for Cognitive Impairment in Epilepsy

Epilepsy is commonly associated with cognitive and behavioral deficits that dramatically affect the quality of life of patients. In order to identify novel therapeutic strategies aimed at reducing these deficits, it is critical first to understand the mechanisms leading to cognitive impairments in epilepsy. Traditionally, seizures and epileptiform activity in addition to neuronal injury have been considered to be the most significant contributors to cognitive dysfunction. In this review we however highlight the role of a new mechanism: alterations of neuronal dynamics, i.e. the timing at which neurons and networks receive and process neural information. These alterations, caused by the underlying etiologies of epilepsy syndromes, are observed in both animal models and patients in the form of abnormal oscillation patterns in unit firing, local field potentials, and electroencephalogram (EEG). Evidence suggests that such mechanisms significantly contribute to cognitive impairment in epilepsy, independently of seizures and interictal epileptiform activity. Therefore, therapeutic strategies directly targeting neuronal dynamics rather than seizure reduction may significantly benefit the quality of life of patients.

Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis

Accurate localization of the epileptogenic zone (EZ) is crucial for refractory focal epilepsy patients to achieve freedom from seizures following epilepsy surgery. In this study, ictal stereo-electroencephalography data from 35 patients with refractory focal epilepsy were analyzed. Effective networks based on partial directed coherence were analyzed, and a gray level co-occurrence matrix was applied to extract the time-varying features of the in-degree. These features, combined with the single-channel signal time-frequency features, including approximate entropy and line length, were used to localize the EZ based on a cluster algorithm. For all seizure-free patients (n = 23), the proposed method was effective in identifying the clinical-EZ-contacts and clinical-EZ-blocks, with an F1-score of 62.47 % and 72.18 %, respectively. The sensitivity was 96.00 % for the clinical-EZ-block identification, which provided the information for the decision-making of clinicians, prompting clinicians to focus on the identified EZ-blocks and their nearby contacts. The agreement between the EZ identified by the proposed method and the clinical-EZ was worse for non-seizure-free patients (n = 12) than for seizure-free patients. Furthermore, our method provided better results than using only brain network or single-channel signal features. This suggests that combining these complementary features can facilitate more accurate localization of the EZ.