Intracranial EEG seizure onset-patterns correlate with high-frequency oscillations in patients with drug-resistant epilepsy

Systematic review of seizure-onset patterns in stereo-electroencephalography: Current state and future directions

OBJECTIVE

Increasing evidence suggests that the seizure-onset pattern (SOP) in stereo-electroencephalography (SEEG) is important for localizing the "true" seizure onset. Specifically, SOPs with low-voltage fast activity (LVFA) are associated with seizure-free outcome (Engel I). However, several classifications and various terms corresponding to the same pattern have been reported, challenging its use in clinical practice.

METHOD

Following the Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guideline, we performed a systematic review of studies describing SOPs along with accompanying figures depicting the reported SOP in SEEG.

RESULTS

Of 1799 studies, 22 met the selection criteria. Among the various SOPs, we observed that the terminology for low frequency periodic spikes exhibited the most variability, whereas LVFA is the most frequently used term of this pattern. Some SOP terms were inconsistent with standard EEG terminology. Finally, there was a significant but weak association between presence of LVFA and seizure-free outcome.

CONCLUSION

Divergent terms were used to describe the same SOPs and some of these terms showed inconsistencies with the standard EEG terminology. Additionally, our results confirmed the link between patterns with LVFA and seizure-free outcomes. However, this association was not strong.

SIGNIFICANCE

These results underline the need for standardization of SEEG terminology.

Left Temporal Pole Encephalocele With Independent Hippocampal Seizures: Surgical Strategy and Relevance of Epileptic Biomarkers

SUMMARY

Temporal pole encephalocele (TE) is an increasingly recognized surgically treatable subtype of refractory temporal lobe epilepsy that rarely shows hippocampal involvement. A 27-year-old patient presented with medically intractable epilepsy because of a left temporal pole encephalocele with a normal hippocampus on MRI. Extraoperative electrocorticography showed independent seizure onset with distinct morphology of seizure onset pattern from the temporal pole encephalocele and hippocampus. Additional analysis of ictal and interictal fast ripples revealed different electrophysiological fast ripple profiles in the two seizure onset zones. The patient underwent temporopolar disconnection, eliminating the need for large dural repair and multiple hippocampal transections that helped preserve the intrahippocampal and extrahippocampal memory pathways. Herein, the authors report that independent hippocampal seizures can be observed in patients with temporal pole encephalocele. Features of the ictal and interictal fast ripples can differ depending on the morphology of the seizure onset pattern. The authors suggest that, the interpretation of fast ripples in clinical practice should take seizure onset patterns into consideration.

Distinct patterns of interictal intracranial EEG in focal cortical dysplasia type I and II

OBJECTIVE

Focal cortical dysplasia (FCD) is the most common malformation causing refractory focal epilepsy. Surgical removal of the entire dysplastic cortex is crucial for achieving a seizure-free outcome. Precise presurgical distinctions between FCD types by neuroimaging are difficult, mainly in patients with normal magnetic resonance imaging findings. However, the FCD type is important for planning the extent of surgical approach and counselling.

METHODS

This study included patients with focal drug-resistant epilepsy and definite histopathological FCD type I or II diagnoses who underwent intracranial electroencephalography (iEEG). We detected interictal epileptiform discharges (IEDs) and their recruitment into repetitive discharges (RDs) to compare electrophysiological patterns characterizing FCD types.

RESULTS

Patients with FCD type II had a significantly higher IED rate (p < 0.005), a shorter inter-discharge interval within RD episodes (p < 0.003), sleep influence on decreased RD periodicity (p < 0.036), and longer RD episode duration (p < 0.003) than patients with type I. A Bayesian classifier stratified FCD types with 82% accuracy.

CONCLUSION

Temporal characteristics of IEDs and RDs reflect the histological findings of FCD subtypes and can differentiate FCD types I and II.

SIGNIFICANCE

Presurgical prediction of FCD type can help to plan a more tailored surgical approach in patients with normal magnetic resonance findings.

Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators

Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.

Spatial and temporal profile of high-frequency oscillations in posttraumatic epileptogenesis

We studied the role of temporal and spatial changes in high-frequency oscillation (HFO, 80–500 Hz) generation in epileptogenesis following traumatic brain injury (TBI).

Experiments were conducted on adult male Sprague Dawley rats. For the TBI group, fluid percussion injury (FPI) on the left sensorimotor area was performed to induce posttraumatic epileptogenesis. For the sham control group, only the craniotomy was performed. After TBI, 8 bipolar micro-electrodes were implanted bilaterally in the prefrontal cortex, perilesional area and homotopic contralateral site, striatum, and hippocampus. Long-term video/local field potential (LFP) recordings were performed for up to 21 weeks to identify and characterize seizures and capture HFOs. The electrode tip locations and the volume of post TBI brain lesions were further estimated by ex-vivo MRI scans. HFOs were detected during slow-wave sleep and categorized as ripple (80–200 Hz) and fast ripple (FR, 250–500 Hz) events. HFO rates and the HFO peak frequencies were compared in the 8 recording locations and across 8-weeks following TBI.

Data from 48 rats (8 sham controls and 40 TBI rats) were analyzed. Within the TBI group, 22 rats (55%) developed recurrent spontaneous seizures (E+ group), at an average of 62.2 (+17.1) days, while 18 rats (45%) did not (E− group). We observed that the HFOs in the E+ group had a higher mean peak frequency than the E− group and the sham group (P < 0.05). Furthermore, the FR rate of the E+ group showed a significant increase compared to the E−group (P < 0.01) and sham control group (P < 0.01), specifically in the perilesional area, homotopic contralateral site, bilateral hippocampus, and to a lesser degree bilateral striatum. When compared across time, the increased FR rate in the E+ group occurred immediately after the insult and remained stable across the duration of the experiment. In addition, lesion size was not statistically different in the E+ and E− group and was not correlated with HFO rates.

Our results suggest that TBI results in the formation of a widespread epileptogenic network. FR rates serve as a biomarker of network formation and predict the future development of epilepsy, however FR are not a temporally specific biomarker of TBI sequelae responsible for epileptogenesis. These results suggest that in patients, future risk of post-TBI epilepsy can be predicted early using FR.

Extensions of Granger Causality Calculations on Brain Networks for Efficient and Accurate Seizure Focus Identification via iEEGs

Current epilepsy surgery planning protocol determines the seizure onset zone (SOZ) through resource-intensive, invasive monitoring of ictal events. Recently, we have reported that Granger Causality (GC) maps produced from analysis of interictal iEEG recordings have potential in revealing SOZ. In this study, we investigate GC maps’ network connectivity patterns to determine possible clinical correlation with patients’ SOZ and resection zone (RZ). While building understanding of interictal network topography and its relationship to the RZ/SOZ, we identify algorithmic tools with potential applications in epilepsy surgery planning. These graph algorithms are retrospectively tested on data from 25 patients and compared to the neurologist-determined SOZ and surgical RZ, viewed as sources of truth. Centrality algorithms yielded statistically significant RZ rank order sums for 16 of 24 patients with RZ data, representing an improvement from prior algorithms. While SOZ results remained largely the same, this study validates the applicability of graph algorithms to RZ/SOZ detection, opening the door to further exploration of iEEG datasets. Furthermore, this study offers previously inaccessible insights into the relationship between interictal brain connectivity patterns and epileptic brain networks, utilizing the overall topology of the graphs as well as data on edge weights and quantity of edges contained in GC maps.

The pilocarpine model of mesial temporal lobe epilepsy: Over one decade later, with more rodent species and new investigative approaches

Fundamental work on the mechanisms leading to focal epileptic discharges in mesial temporal lobe epilepsy (MTLE) often rests on the use of rodent models in which an initial status epilepticus (SE) is induced by kainic acid or pilocarpine. In 2008 we reviewed how, following systemic injection of pilocarpine, the main subsequent events are the initial SE, the latent period, and the chronic epileptic state. Up to a decade ago, rats were most often employed and they were frequently analysed only behaviorally. However, the use of transgenic mice has revealed novel information regarding this animal model. Here, we review recent findings showing the existence of specific neuronal events during both latent and chronic states, and how optogenetic activation of specific cell populations modulate spontaneous seizures. We also address neuronal damage induced by pilocarpine treatment, the role of neuroinflammation, and the influence of circadian and estrous cycles. Updating these findings leads us to propose that the rodent pilocarpine model continues to represent a valuable tool for identifying the basic pathophysiology of MTLE.

Focal Cortical Resection and Hippocampectomy in a Cat With Drug-Resistant Structural Epilepsy

Epilepsy surgery is a common therapeutic option in humans with drug-resistant epilepsy. However, there are few reports of intracranial epilepsy surgery for naturally occurring epilepsy in veterinary medicine. A 12-year-old neutered male domestic shorthair cat with presumed congenital cortical abnormalities (atrophy) in the right temporo-occipital cortex and hippocampus had been affected with epilepsy from 3 months of age. In addition to recurrent epileptic seizures, the cat exhibited cognitive dysfunction, bilateral blindness, and right forebrain signs. Seizures had been partially controlled (approximately 0.3–0.7 seizures per month) by phenobarbital, zonisamide, diazepam, and gabapentin until 10 years of age; however, they gradually became uncontrollable (approximately 2–3 seizures per month). In order to plan epilepsy surgery, presurgical evaluations including advanced structural magnetic resonance imaging and long-term intracranial video-electroencephalography monitoring were conducted to identify the epileptogenic zone. The epileptogenic zone was suspected in the right atrophied temporo-occipital cortex and hippocampus. Two-step surgery was planned, and a focal cortical resection of that area was performed initially. After the first surgery, seizures were not observed for 2 months, but they then recurred. The second surgery was performed to remove the right atrophic hippocampus and extended area of the right cortex, which showed spikes on intraoperative electrocorticography. After the second operation, although epileptogenic spikes remained in the contralateral occipital lobe, which was suspected as the second epileptogenic focus, seizure frequency decreased to <0.3 seizure per month under treatment with antiseizure drugs at 1.5 years after surgery. There were no apparent complications associated with either operation, although the original neurological signs were unchanged. This is the first exploratory study of intracranial epilepsy surgery for naturally occurring epilepsy, with modern electroclinical and imaging evidence, in veterinary medicine. Along with the spread of advanced diagnostic modalities and neurosurgical devices in veterinary medicine, epilepsy surgery may be an alternative treatment option for drug-resistant epilepsy in cats.

The accuracy of quantitative EEG biomarker algorithms depends upon seizure onset dynamics

OBJECTIVE

To compare the performance of different ictal quantitative biomarkers of the seizure onset zone (SOZ) across many seizures in a cohort of consecutive patients with a variety of seizure onset patterns.

METHODS

The Epileptogenicity Index (EI, a measure of fast activity) and Slow Polarizing Shift index (SPS, a measure of infraslow activity) were calculated for 212 seizures (22 patients). After stratification by onset pattern, median index values inside and outside the SOZ were compared in aggregate and for each of the onset patterns. Receiver Operating Characteristic (ROC) curves were constructed to compare the performance of each index.

RESULTS

Median values of EI (0.056 vs 0.0087), SPS (0.27 vs 0.19), and CI (0.21 vs 0.12) were significantly higher for contacts inside the SOZ, all p < 0.0001. Analysis of AUC showed variable performance of these indices across seizure types, although AUC for EI and SPS was generally greatest for seizures with fast activity at onset.

CONCLUSIONS

All indices were significantly higher for contacts inside the SOZ; however, the performance of these indices varied depending on the pattern of seizure onset.

SIGNIFICANCE

These findings suggest that future studies of quantitative biomarkers of the SOZ should account for seizure onset pattern.

High-frequency oscillations recorded with surface EEG in neonates with seizures

OBJECTIVE

Neonatal seizures are often the first symptom of perinatal brain injury. High-frequency oscillations (HFOs) are promising new biomarkers for epileptogenic tissue and can be found in intracranial and surface EEG. To date, we cannot reliably predict which neonates with seizures will develop childhood epilepsy. We questioned whether epileptic HFOs can be generated by the neonatal brain and potentially predict epilepsy.

METHODS

We selected 24 surface EEGs sampled at 2048 Hz with 175 seizures from 16 neonates and visually reviewed them for HFOs. Interictal epochs were also reviewed.

RESULTS

We found HFOs in thirteen seizures (7%) from four neonates (25%). 5025 ictal ripples (rate 10 to 1311/min; mean frequency 135 Hz; mean duration 66 ms) and 1427 fast ripples (rate 8 to 356/min; mean frequency 298 Hz; mean duration 25 ms) were marked. Two neonates (13%) showed interictal HFOs (285 ripples and 25 fast ripples). Almost all HFOs co-occurred with sharp transients. We could not find a relationship between neonatal HFOs and outcome yet.

CONCLUSIONS

Neonatal HFOs co-occur with ictal and interictal sharp transients.

SIGNIFICANCE

The neonatal brain can generate epileptic ripples and fast ripples, particularly during seizures, though their occurrence is not common and potential clinical value not evident yet.

High-Frequency Oscillations in the Scalp EEG of Intensive Care Unit Patients With Altered Level of Consciousness

PURPOSE

In comatose patients, distinguishing between nonconvulsive status epilepticus and diffuse structural or metabolic encephalopathies is often challenging. Both conditions can generate periodic discharges on EEG with similar morphology and periodicity. We investigated the occurrence of high-frequency oscillations-potential biomarkers of epileptogenesis-on scalp EEG of comatose patients with periodic discharges in the EEG.

METHODS

Fifteen patients were included. Patients were divided into three groups, according to underlying etiology: Group 1, seizure related; group 2, structural; group 3, nonstructural. EEG recordings were compared with respect to the presence and rates of gamma (30-80 Hz) and ripples (80-250 Hz).

RESULTS

Patients were 23 to 106 years old (median, 68 years); 60% were female. 206 channels were eligible for analysis (median, 15 channels/patient). Overall, 43% of channels showed gamma, and 24% had ripples. Group 2 showed the highest proportion of channels with gamma (47%), followed by group 1 (38%) and group 3 (36%). Mean gamma rates were higher in group 2 (4.65 gamma/min/channel) than in group 1 (1.52) and group 3 (1.44) (P < 0.001). Group 2 showed the highest proportion of channels with ripples (29.2%), followed by group 1 (15%) and group 3 (24.2%). Mean ripple rates were higher in group 2 (5.09 ripple/min/channel) than in group 1 (0.96) and group 3 (0.83) (P < 0.001).

CONCLUSIONS

Fast oscillations, including high-frequency oscillations, can be detected in scalp EEG of patients with altered consciousness. High rates of fast activity may suggest an underlying structural brain lesion. Future studies are needed to determine whether fast oscillations in the setting of acute/subacute brain lesions are a biomarker of subsequent development of human epilepsy.

A Translational Study on Acute Traumatic Brain Injury: High Incidence of Epileptiform Activity on Human and Rat Electrocorticograms and Histological Correlates in Rats

Background: In humans, early pathological activity on invasive electrocorticograms (ECoGs) and its putative association with pathomorphology in the early period of traumatic brain injury (TBI) remains obscure. Methods: We assessed pathological activity on scalp electroencephalograms (EEGs) and ECoGs in patients with acute TBI, early electrophysiological changes after lateral fluid percussion brain injury (FPI), and electrophysiological correlates of hippocampal damage (microgliosis and neuronal loss), a week after TBI in rats. Results: Epileptiform activity on ECoGs was evident in 86% of patients during the acute period of TBI, ECoGs being more sensitive to epileptiform and periodic discharges. A “brush-like” ECoG pattern superimposed over rhythmic delta activity and periodic discharge was described for the first time in acute TBI. In rats, FPI increased high-amplitude spike incidence in the neocortex and, most expressed, in the ipsilateral hippocampus, induced hippocampal microgliosis and neuronal loss, ipsilateral dentate gyrus being most vulnerable, a week after TBI. Epileptiform spike incidence correlated with microglial cell density and neuronal loss in the ipsilateral hippocampus. Conclusion: Epileptiform activity is frequent in the acute period of TBI period and is associated with distant hippocampal damage on a microscopic level. This damage is probably involved in late consequences of TBI. The FPI model is suitable for exploring pathogenetic mechanisms of post-traumatic disorders.

Expert consensus on clinical applications of high-frequency oscillations in epilepsy

Studies in animal models of epilepsy and pre-surgical patients have unanimously found a strong correlation between high-frequency oscillations (HFOs, > 80 Hz) and the epileptogenic zone, suggesting that HFOs can be a potential biomarker of epileptogenicity and epileptogenesis. This consensus includes the definition and standard detection techniques of HFOs, the localizing value of pathological HFOs for epileptic foci, and different ways to distinguish physiological from epileptic HFOs. The latest clinical applications of HFOs in epilepsy and the related findings are also discussed. HFOs will advance our understanding of the pathophysiology of epilepsy.

Identifying the Epileptogenic Zone With the Relative Strength of High-Frequency Oscillation: A Stereoelectroencephalography Study

Background

High-frequency oscillation (HFO) represents a promising biomarker of epileptogenicity. However, the significant interindividual differences among patients limit its application in clinical practice. Here, we applied and evaluated an individualized, frequency-based approach of HFO analysis in stereoelectroencephalography (SEEG) data for localizing the epileptogenic zones (EZs).

Methods

Clinical and SEEG data of 19 patients with drug-resistant focal epilepsy were retrospectively analyzed. The individualized spectral power of all signals recorded by electrode array, i.e., the relative strength of HFO, was computed with a wavelet method for each patient. Subsequently, the clinical value of the relative strength of HFO for identifying the EZ was evaluated.

Results

Focal increase in the relative strength of HFO in SEEG recordings were identified in all 19 patients. HFOs identified inside the clinically identified seizure onset zone had more spectral power than those identified outside (p < 0.001), and HFOs in 250–500 Hz band (fast ripples) seemed to be more specific identifying the EZ than in those in 80–250 Hz band (ripples) (p < 0.01). The resection of brain regions generating HFOs resulted in a favorable seizure outcome in 17 patients (17/19; 89.5%), while in the cases of other patients with poor outcomes, the brain regions generating HFOs were not removed completely.

Conclusion

The relative strength of HFO, especially fast ripples, is a promising effective biomarker for identifying the EZ and can lead to a favorable seizure outcome if used to guide epilepsy surgery.

Seizure onset location shapes dynamics of initiation

OBJECTIVE

Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-frequency interactions, can discriminate between seizure types.

METHODS

We analyzed temporal changes in low and high frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and ripple/fast ripple frequency bands at seizure onset.

RESULTS

Seizures of multiple electrographic patterns were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset.

CONCLUSIONS

Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism.

SIGNIFICANCE

To learn how seizures are initiated, researchers would do well to consider other aspects of their manifestation, in addition to their electrographic patterns. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.

Automated classification of five seizure onset patterns from intracranial electroencephalogram signals

OBJECTIVE

The electroencephalographic (EEG) signals contain information about seizures and their onset location. There are several seizure onset patterns reported in the literature, and these patterns have clinical significance. In this work, we propose a system to automatically classify five seizure onset patterns from intracerebral EEG signals.

METHODS

The EEG was segmented by clinicians indicating the start and end time of each seizure onset pattern, the channels involved at onset and the seizure onset pattern. Twelve features that represent the time domain characteristics and signal complexity were extracted from 663 seizures channels of 24 patients. The features were used for classification of the patterns with support vector machine - Error-Correcting Output Codes (SVM-ECOC). Three patient groups with a similar number of seizure segments were created, and one group was used for testing and the rest for training. This test was repeated by rotating the testing and training data.

RESULTS

The feature space formed by both time domain and multiscale sample entropy features perform well in classification of the data. An overall accuracy of 80.7% was obtained with these features and a linear kernel of SVM-ECOC.

CONCLUSIONS

The seizure onset patterns consist of varied time and complexity characteristics. It is possible to automatically classify various seizure onset patterns very similarly to visual classification.

SIGNIFICANCE

The proposed system could aid the medical team in assessing intracerebral EEG by providing an objective classification of seizure onset patterns.

Utility of stereo-electroencephalography recording guided by magnetoencephalography in the surgical treatment of epilepsy patients with negative magnetic resonance imaging results

Objective: It is challenging for neurosurgeons to perform surgeries on patients without detectable structural lesions. Therefore, this retrospective study aimed to explore the outcome of stereo-electroencephalography (SEEG) in suspicious areas guided by magnetoencephalography (MEG)-magnetic resonance imaging (MRI) reconstruction in MRI-negative epilepsy patients. Methods: This study included 47 patients with negative-MRI epilepsy. Seizure outcome at 24 months was assessed using a modified Engel's classification. Accordingly, class I and II were considered favorable outcomes, whereas classes III and IV were unfavorable. Furthermore, patients were classified into a consistent group if the results of MEG and SEEG indicated the same area of the brain. The relationship between surgical outcome and the concordance of MEG and SEEG was analyzed. Results: A complete seizure-free condition was achieved in 22 (47%) patients. Sex, handedness, age and duration of illness were not significantly associated with seizure-free outcome (p = .187 [Pearson chi-squared test]). The number of patients with favorable outcome (Engle I and II) was as high as 68% at the time of follow-up. Furthermore, more seizure-free patients were found in the SEEG and MEG consistent group. Conclusions: SEEG is a valuable tool in the pre-evaluation for resective epilepsy surgery, particularly in negative-MRI epilepsy patients; MEG greatly facilitates localization for SEEG electrode implantation. However, none of these tools are absolutely sensitive and reliable; therefore, collecting as much information as possible is necessary to achieve satisfactory results in epilepsy surgery.

DC shifts, high frequency oscillations, ripples and fast ripples in relation to the seizure onset zone

Efforts to improve epilepsy surgery outcomes have led to increased interest in the study of electroencephalographic oscillations outside the conventional EEG bands. These include fast activity above the gamma band, known as high frequency oscillations (HFOs), and infraslow activity (ISA) below the delta band, sometimes referred to as direct current (DC) or ictal baseline shifts (IBS). HFOs in particular have been extensively studied as potential biomarkers for epileptogenic tissue in light of evidence showing that resection of brain tissue containing HFOs is associated with good surgical outcomes. Not all HFOs are conclusively pathological, however, as they can be recorded in nonepileptic tissue and induced by cognitive, visual, or motor tasks. Consequently, efforts to distinguish between pathological and physiological HFOs have identified several traits specific to pathological HFOs, such as coupling with interictal spikes, association with delta waves, and stereotypical morphologies. On the opposite end of the EEG spectrum, sub-delta oscillations have been shown to co-localize with the seizure onset zones (SOZ) and appear in a narrower spatial distribution than activity in the conventional EEG frequency bands. In this report, we review studies that implicate HFOs and ISA in ictogenesis and discuss current limitations such as inter-observer variability and poor standardization of recording techniques. Furthermore, we propose that HFOs and ISA should be analyzed in addition to activity in the conventional EEG band during intracranial presurgical EEG monitoring to identify the best possible surgical margin.

Cortical Layer and Spectrotemporal Architecture of Epileptiform Activity in vivo in a Mouse Model of Focal Cortical Malformation

Our objective is to examine the layer and spectrotemporal architecture and laminar distribution of high-frequency oscillations (HFOs) in a neonatal freeze lesion model of focal cortical dysplasia (FCD) associated with a high prevalence of spontaneous spike-wave discharges (SWDs). Electrophysiological recording of local field potentials (LFPs) in control and freeze lesion animals were obtained with linear micro-electrode arrays to detect presence of HFOs as compared to changes in spectral power, signal coherence, and single-unit distributions during "hyper-excitable" epochs of anesthesia-induced burst-suppression (B-S). Result were compared to HFOs observed during spontaneous SWDs in animals during sleep. Micro-electrode array recordings from the malformed cortex indicated significant increases in the presence of HFOs above 100 Hz and associated increases in spectral power and altered LFP coherence of recorded signals across cortical lamina of freeze-lesioned animals with spontaneous bursts of high-frequency activity, confined predominately to granular and supragranular layers. Spike sorting of well-isolated single-units recorded from freeze-lesioned cortex indicated an increase in putative excitatory cell activity in the outer cortical layers that showed only a weak association with HFOs while deeper inhibitory units were strongly phase-locked to high-frequency ripple (HFR) oscillations (300-800 Hz). Both SWDs and B-S show increases in HFR activity that were phase-locked to the high-frequency spike pattern occurring at the trough of low frequency oscillations. The spontaneous cyclic spiking of cortical inhibitory cells appears to be the driving substrate behind the HFO patterns associated with SWDs and a hyperexcitable supragranular layer near the malformed cortex may play a key role in epileptogenesis in our model. These data, derived from a mouse model with a distinct focal cortical malformation, support recent clinical data that HFOs, particularly fast ripples, is a biomarker to help define the cortical seizure zone, and provide limited insights toward understanding cellular level changes underlying the HFOs.

Delta Brush Pattern Is Not Unique to NMDAR Encephalitis: Evaluation of Two Independent Long-Term EEG Cohorts

PURPOSE

Although its specificity has not previously been investigated in other cohorts, delta brush pattern (DBP) is increasingly reported in the EEGs of patients with anti- N-methyl-d-aspartate receptor (NMDAR) encephalitis.

METHODS

We aimed to investigate the DBP in the EEGs of 2 cohorts; patients with change in consciousness for various causes monitored in the intensive care unit (ICU) (n = 106) and patients with mesial temporal lobe epilepsy (MTLE) with or without antineuronal antibodies (n = 76).

RESULTS

These patients were investigated for the presence of DBP, defined as an EEG pattern characterized by delta activity at 1 to 3 Hz with superimposed bursts of rhythmic 12- to 30-Hz activity. Two investigators blindfolded for the clinical and immunological data independently analyzed the EEGs for recognition of this pattern. An EEG picture compatible with DBP was observed in 4 patients; only 1 of them (1.3%) belonged to the MTLE group. She did not bear any of the investigated autoantibodies and was seizure-free after epilepsy surgery. In the ICU group, there were 3 additional patients showing DBP with various diagnoses such as hypoxic encephalopathy, brain tumor, stroke, and metabolic derangements. All of them had died in 1-month period.

CONCLUSIONS

Our results underlined that DBP is not unique to NMDAR encephalitis; it may very rarely occur in MTLE with good prognosis after surgery and second, in ICU patients who have high mortality rate. Therefore, the presence of this pattern should alert the clinician for NMDAR encephalitis but other possible etiologies should not be ignored.

Progress and Remaining Challenges in the Application of High Frequency Oscillations as Biomarkers of Epileptic Brain

High-frequency oscillations (HFOs: 100 - 600 Hz) have been widely proposed as biomarkers of epileptic brain tissue. In addition, HFOs over a broader range of frequencies spanning 30 - 2000 Hz are potential biomarkers of both physiological and pathological brain processes. The majority of the results from humans with focal epilepsy have focused on HFOs recorded directly from the brain with intracranial EEG (iEEG) in the high gamma (65 - 100 Hz), ripple (100 - 250 Hz), and fast ripple (250 - 600 Hz) frequency ranges. These results are supplemented by reports of HFOs recorded with iEEG in the low gamma (30 - 65Hz) and very high frequency (500 - 2000 Hz) ranges. Visual detection of HFOs is laborious and limited by poor inter-rater agreement; and the need for accurate, reproducible automated HFOs detection is well recognized. In particular, the clinical translation of HFOs as a biomarker of the epileptogenic brain has been limited by the ability to reliably detect and accurately classify HFOs as physiological or pathological. Despite these challenges, there has been significant progress in the field, which is the subject of this review. Furthermore, we provide data and corresponding analytic code in an effort to promote reproducible research and accelerate clinical translation.

Physiological and pathological high-frequency oscillations have distinct sleep-homeostatic properties

OBJECTIVE

The stage of sleep is a known modulator of high-frequency oscillations (HFOs). For instance, high amplitude slow waves during NREM sleep and the subtypes of REM sleep were shown to contribute to a better separation between physiological and pathological HFOs. This study investigated rates and spatial spread of the different HFO types (physiological and pathological ripples in the 80-250 Hz frequency band, and fast ripples above 250 Hz) depending on time spent in sleep across the different sleep cycles.

METHODS

Fifteen patients with focal pharmaco-resistant epilepsy underwent one night of video-polysomnography during chronic intracranial EEG recording for presurgical epilepsy evaluation. The HFO rate and spread across the different sleep cycles were determined with an automatic HFO detector. We built models to explain the observed rate and spread based on time in sleep and other variables i.e. sleep stage, delta band and sigma band activity, and slow wave amplitude. Statistical significance of the different variables was determined by a model comparison using the Akaike information criterion.

RESULTS

The rate of HFOs depends significantly on the accumulated time of sleep. As the night advanced, the rate of pathological ripples and fast ripples decreased during NREM sleep (up to 15% per hour spent in the respective sleep stages), while the rate of physiological ripples increased during REM sleep (8% per hour spent in REM sleep). Interestingly, the stage of sleep but not the sleep cycle determined the extent of spread of HFOs, showing a larger field during NREM sleep and a more restricted field during REM sleep.

CONCLUSION

The different dependence with sleep time for physiological and pathological ripples is in keeping with their distinct underlying generating mechanisms. From a practical point of view, the first sleep cycle seems to be best suitable for studying HFOs in epilepsy, given that the contrast between physiological and pathological ripple rates is largest during this time.