Conundrums of high-frequency oscillations (80-800 Hz) in the epileptic brain

Net synaptic drive of fast-spiking interneurons is inverted towards inhibition in human FCD I epilepsy

Focal cortical dysplasia type I (FCD I) is the most common cause of pharmaco-resistant epilepsy with the poorest prognosis. To understand the epileptogenic mechanisms of FCD I, we obtained tissue resected from patients with FCD I epilepsy, and from tumor patients as control. Using whole-cell patch clamp in acute human brain slices, we investigated the cellular properties of fast-spiking interneurons (FSINs) and pyramidal neurons (PNs) within the ictal onset zone. In FCD I epilepsy, FSINs exhibited lower firing rates from slower repolarization and action potential broadening, while PNs had increased firing. Importantly, excitatory synaptic drive of FSINs increased progressively with the scale of cortical activation as a general property across species, but this relationship was inverted towards net inhibition in FCD I epilepsy. Further comparison with intracranial electroencephalography (iEEG) from the same patients revealed that the spatial extent of pathological high-frequency oscillations (pHFO) was associated with synaptic events at FSINs.

A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species

The study of sharp-wave ripples has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy is considered a biomarker of dysfunction. Sharp-wave ripples exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine-learning models for automatic detection and analysis of these events. The machine-learning architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of ripple features recorded in the dorsal hippocampus of mice across awake and sleep conditions. When applied to data from the macaque hippocampus, these models are able to generalize detection and reveal shared properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize analysis of sharp-wave ripples, lowering the threshold for its adoption in biomedical applications.

Exploring Ripple Waves in the Human Brain

Ripples are brief (<150 ms) high-frequency oscillatory neural activities in the brain with a range of 140 to 200 Hz in rodents and 80 to 140 Hz in humans. Ripples are regarded as playing an essential role in several aspects of memory function, mainly in the hippocampus. This type of ripple generally occurs with sharp waves and is called a sharp-wave ripple (SPW-R). Extensive research of SPW-Rs in the rodent brain while actively awake has also linked the function of these SPW-Rs to navigation and decision making. Although many studies with rodents unveiled SPW-R function, research in humans on this subject is still sparse. Therefore, unveiling SPW-R function in the human hippocampus is warranted. A certain type of ripples may also be a biomarker of epilepsy. This type of ripple is called a pathological ripple (p-ripple). p-ripples have a wider range of frequency (80-500 Hz) than SPW-Rs, and the range of frequency is especially higher in brain regions that are intrinsically linked to epilepsy onset. Brain regions producing ripples are too small for scalp electrode recording, and intracranial recording is typically needed to detect ripples. In addition, SPW-Rs in the human hippocampus have been recorded from patients with epilepsy who may have p-ripples. Differentiating SPW-Rs and p-ripples is often not easy. We need to develop more sophisticated methods to record SPW-Rs to differentiate them from p-ripples. This paper reviews the general features and roles of ripple waves.

Detection of pathological high-frequency oscillations in refractory epilepsy patients undergoing simultaneous stereo-electroencephalography and magnetoencephalography

BACKGROUND

Stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) have generally been used independently as part of the pre-surgical evaluation of drug-resistant epilepsy (DRE) patients. However, the possibility of simultaneously employing these recording techniques to determine whether MEG has the potential of offering the same information as SEEG less invasively, or whether it could offer a greater spatial indication of the epileptogenic zone (EZ) to aid surgical planning, has not been previously evaluated.

METHODS

Data from 24 paediatric and adult DRE patients, undergoing simultaneous SEEG and MEG as part of their pre-surgical evaluation, was analysed employing manual and automated high-frequency oscillations (HFOs) detection, and spectral and source localisation analyses.

RESULTS

Twelve patients (50%) were included in the analysis (4 males; mean age=25.08 years) and showed interictal SEEG and MEG HFOs. HFOs detection was concordant between the two recording modalities, but SEEG displayed higher ability of differentiating between deep and superficial epileptogenic sources. Automated HFO detector in MEG recordings was validated against the manual MEG detection method. Spectral analysis revealed that SEEG and MEG detect distinct epileptic events. The EZ was well correlated with the simultaneously recorded data in 50% patients, while 25% patients displayed poor correlation or discordance.

CONCLUSION

MEG recordings can detect HFOs, and simultaneous use of SEEG and MEG HFO identification facilitates EZ localisation during the presurgical planning stage for DRE patients. Further studies are necessary to validate these findings and support the translation of automated HFO detectors into routine clinical practice.

Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy

Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.

Robust chronic convulsive seizures, high frequency oscillations, and human seizure onset patterns in an intrahippocampal kainic acid model in mice

Intrahippocampal kainic acid (IHKA) has been widely implemented to simulate temporal lobe epilepsy (TLE), but evidence of robust seizures is usually limited. To resolve this problem, we slightly modified previous methods and show robust seizures are common and frequent in both male and female mice. We employed continuous wideband video-EEG monitoring from 4 recording sites to best demonstrate the seizures. We found many more convulsive seizures than most studies have reported. Mortality was low. Analysis of convulsive seizures at 2-4 and 10-12 wks post-IHKA showed a robust frequency (2-4 per day on average) and duration (typically 20-30 s) at each time. Comparison of the two timepoints showed that seizure burden became more severe in approximately 50% of the animals. We show that almost all convulsive seizures could be characterized as either low-voltage fast or hypersynchronous onset seizures, which has not been reported in a mouse model of epilepsy and is important because these seizure types are found in humans. In addition, we report that high frequency oscillations (>250 Hz) occur, resembling findings from IHKA in rats and TLE patients. Pathology in the hippocampus at the site of IHKA injection was similar to mesial temporal lobe sclerosis and reduced contralaterally. In summary, our methods produce a model of TLE in mice with robust convulsive seizures, and there is variable progression. HFOs are robust also, and seizures have onset patterns and pathology like human TLE. SIGNIFICANCE: Although the IHKA model has been widely used in mice for epilepsy research, there is variation in outcomes, with many studies showing few robust seizures long-term, especially convulsive seizures. We present an implementation of the IHKA model with frequent convulsive seizures that are robust, meaning they are >10 s and associated with complex high frequency rhythmic activity recorded from 2 hippocampal and 2 cortical sites. Seizure onset patterns usually matched the low-voltage fast and hypersynchronous seizures in TLE. Importantly, there is low mortality, and both sexes can be used. We believe our results will advance the ability to use the IHKA model of TLE in mice. The results also have important implications for our understanding of HFOs, progression, and other topics of broad interest to the epilepsy research community. Finally, the results have implications for preclinical drug screening because seizure frequency increased in approximately half of the mice after a 6 wk interval, suggesting that the typical 2 wk period for monitoring seizure frequency is insufficient.

Characterizing Hippocampal Oscillatory Signatures Underlying Seizures in Temporal Lobe Epilepsy

Temporal Lobe Epilepsy (TLE) is a neurological condition characterized by focal brain hyperexcitability, resulting in abnormal neuronal discharge and uncontrollable seizures. The hippocampus, with its inherently highly synchronized firing patterns and relatively high excitability, is prone to epileptic seizures, and it is usually the focus of TLE. Researchers have identified hippocampal high-frequency oscillations (HFOs) as a salient feature in people with TLE and animal models of this disease, arising before or at the onset of the epileptic event. To a certain extent, these pathological HFOs have served as a marker and a potential target for seizure attenuation using electrical or optogenetic interventions. However, many questions remain about whether we can reliably distinguish pathological from non-pathological HFOs and whether they can tell us about the development of the disease. While this would be an arduous task to perform in humans, animal models of TLE provide an excellent opportunity to study the characteristics of HFOs in predicting how epilepsy evolves. This minireview will (1) summarize what we know about the oscillatory disruption in TLE, (2) summarize knowledge about oscillatory changes in the latent period and their role in predicting seizures, and (3) propose future studies essential to uncovering potential treatments based on early detection of pathological HFOs.

HFOApp: A MATLAB Graphical User Interface for High-Frequency Oscillation Marking

Epilepsy affects 3.4 million people in the United States, and, despite the availability of numerous antiepileptic drugs, 36% of patients have uncontrollable seizures, which severely impact quality of life. High-frequency oscillations (HFOs) are a potential biomarker of epileptogenic tissue that could be useful in surgical planning. As a result, research into the efficacy of HFOs as a clinical tool has increased over the last 2 decades. However, detection and identification of these transient rhythms in intracranial electroencephalographic recordings remain time-consuming and challenging. Although automated detection algorithms have been developed, their results are widely inconsistent, reducing reliability. Thus, manual marking of HFOs remains the gold standard, and manual review of automated results is required. However, manual marking and review are time consuming and can still produce variable results because of their subjective nature and the limitations in functionality of existing open-source software. Our goal was to develop a new software with broad application that improves on existing open-source HFO detection applications in usability, speed, and accuracy. Here, we present HFOApp: a free, open-source, easy-to-use MATLAB-based graphical user interface for HFO marking. This toolbox offers a high degree of intuitive and ergonomic usability and integrates interactive automation-assist options with manual marking, significantly reducing the time needed for review and manual marking of recordings, while increasing inter-rater reliability. The toolbox also features simultaneous multichannel detection and marking. HFOApp was designed as an easy-to-use toolbox for clinicians and researchers to quickly and accurately mark, quantify, and characterize HFOs within electrophysiological datasets.

Microscale dynamics of electrophysiological markers of epilepsy

OBJECTIVE

Interictal discharges (IIDs) and high frequency oscillations (HFOs) are established neurophysiologic biomarkers of epilepsy, while microseizures are less well studied. We used custom poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) microelectrodes to better understand these markers' microscale spatial dynamics.

METHODS

Electrodes with spatial resolution down to 50 µm were used to record intraoperatively in 30 subjects. IIDs' degree of spread and spatiotemporal paths were generated by peak-tracking followed by clustering. Repeating HFO patterns were delineated by clustering similar time windows. Multi-unit activity (MUA) was analyzed in relation to IID and HFO timing.

RESULTS

We detected IIDs encompassing the entire array in 93% of subjects, while localized IIDs, observed across < 50% of channels, were seen in 53%. IIDs traveled along specific paths. HFOs appeared in small, repeated spatiotemporal patterns. Finally, we identified microseizure events that spanned 50-100 µm. HFOs covaried with MUA, but not with IIDs.

CONCLUSIONS

Overall, these data suggest that irritable cortex micro-domains may form part of an underlying pathologic architecture which could contribute to the seizure network.

SIGNIFICANCE

These results, supporting the possibility that epileptogenic cortex comprises a mosaic of irritable domains, suggests that microscale approaches might be an important perspective in devising novel seizure control therapies.

Identification of clinically relevant biomarkers of epileptogenesis - a strategic roadmap

Onset of many forms of epilepsy occurs after an initial epileptogenic insult or as a result of an identified genetic defect. Given that the precipitating insult is known, these epilepsies are, in principle, amenable to secondary prevention. However, development of preventive treatments is difficult because only a subset of individuals will develop epilepsy and we cannot currently predict which individuals are at the highest risk. Biomarkers that enable identification of these individuals would facilitate clinical trials of potential anti-epileptogenic treatments, but no such prognostic biomarkers currently exist. Several putative molecular, imaging, electroencephalographic and behavioural biomarkers of epileptogenesis have been identified, but clinical translation has been hampered by fragmented and poorly coordinated efforts, issues with inter-model reproducibility, study design and statistical approaches, and difficulties with validation in patients. These challenges demand a strategic roadmap to facilitate the identification, characterization and clinical validation of biomarkers for epileptogenesis. In this Review, we summarize the state of the art with respect to biomarker research in epileptogenesis and propose a five-phase roadmap, adapted from those developed for cancer and Alzheimer disease, that provides a conceptual structure for biomarker research.

Alterations in coordinated EEG activity precede the development of seizures in comatose children

OBJECTIVE

We aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children.

METHODS

In a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring.

RESULTS

Children who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results.

CONCLUSIONS

These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures.

SIGNIFICANCE

If confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children.

Laminar distribution of electrically evoked hippocampal short latency ripple activity highlights the importance of the subiculum in vivo in human epilepsy, an intraoperative study

OBJECTIVE

The goal of this study was to define the pathology and anesthesia dependency of single pulse electrical stimulation (SPES) dependent high-frequency oscillations (HFOs, ripples, fast ripples) in the hippocampal formation.

METHODS

Laminar profile of electrically evoked short latency (<100 ms) high-frequency oscillations (80-500 Hz) was examined in the hippocampus of therapy-resistant epileptic patients (6 female, 2 male) in vivo, under general anesthesia.

RESULTS

Parahippocampal SPES evoked HFOs in all recorded hippocampal subregions (Cornu Ammonis 2-3, dentate gyrus, and subiculum) were not uniform, rather the combination of ripples, fast ripples, sharp transients, and multiple unit activities. Mild and severe hippocampal sclerosis (HS) differed in the probability to evoke fast ripples: it decreased with the severity of sclerosis in CA2-3 but increased in the subiculum. Modulation in the ripple spectrum was observed only in the subiculum with increased fast HFO rate and frequency in severe HS. Inhalational anesthetics (isoflurane) suppressed the chance to evoke HFOs compared to propofol.

CONCLUSION

The presence of early HFOs in the dentate gyrus and early fast HFOs (>250 Hz) in the other subregions indicate the pathological nature of these evoked oscillations. Subiculum was found to be active producing HFOs in parallel with the cell loss in the hippocampus proper, which emphasize the role of this region in the generation of epileptic activity.

High frequency oscillations in epileptic and non-epileptic human hippocampus during a cognitive task

Hippocampal high-frequency electrographic activity (HFOs) represents one of the major discoveries not only in epilepsy research but also in cognitive science over the past few decades. A fundamental challenge, however, has been the fact that physiological HFOs associated with normal brain function overlap in frequency with pathological HFOs. We investigated the impact of a cognitive task on HFOs with the aim of improving differentiation between epileptic and non-epileptic hippocampi in humans. Hippocampal activity was recorded with depth electrodes in 15 patients with focal epilepsy during a resting period and subsequently during a cognitive task. HFOs in ripple and fast ripple frequency ranges were evaluated in both conditions, and their rate, spectral entropy, relative amplitude and duration were compared in epileptic and non-epileptic hippocampi. The similarity of HFOs properties recorded at rest in epileptic and non-epileptic hippocampi suggests that they cannot be used alone to distinguish between hippocampi. However, both ripples and fast ripples were observed with higher rates, higher relative amplitudes and longer durations at rest as well as during a cognitive task in epileptic compared with non-epileptic hippocampi. Moreover, during a cognitive task, significant reductions of HFOs rates were found in epileptic hippocampi. These reductions were not observed in non-epileptic hippocampi. Our results indicate that although both hippocampi generate HFOs with similar features that probably reflect non-pathological phenomena, it is possible to differentiate between epileptic and non-epileptic hippocampi using a simple odd-ball task.

Long-range phase synchronization of high-frequency oscillations in human cortex

Inter-areal synchronization of neuronal oscillations at frequencies below ~100 Hz is a pervasive feature of neuronal activity and is thought to regulate communication in neuronal circuits. In contrast, faster activities and oscillations have been considered to be largely local-circuit-level phenomena without large-scale synchronization between brain regions. We show, using human intracerebral recordings, that 100–400 Hz high-frequency oscillations (HFOs) may be synchronized between widely distributed brain regions. HFO synchronization expresses individual frequency peaks and exhibits reliable connectivity patterns that show stable community structuring. HFO synchronization is also characterized by a laminar profile opposite to that of lower frequencies. Importantly, HFO synchronization is both transiently enhanced and suppressed in separate frequency bands during a response-inhibition task. These findings show that HFO synchronization constitutes a functionally significant form of neuronal spike-timing relationships in brain activity and thus a mesoscopic indication of neuronal communication per se.

High-frequency oscillations (HFOs) are common in mammalian brains and have been assumed to be strictly local. Using human intracerebral recordings, the authors find that HFOs can be phase synchronized across long distances between active cortical sites during resting and task states, which may reflect neuronal communication.

Association of fast ripples on intracranial EEG and outcomes after epilepsy surgery

OBJECTIVE

To examine whether fast ripples (FRs) are an accurate marker of the epileptogenic zone, we analyzed overnight stereo-EEG recordings from 43 patients and hypothesized that FR resection ratio, maximal FR rate, and FR distribution predict postsurgical seizure outcome.

METHODS

We detected FRs automatically from an overnight recording edited for artifacts and visually from a 5-minute period of slow-wave sleep. We examined primarily the accuracy of removing ≥50% of total FR events or of channels with FRs to predict postsurgical seizure outcome (Engel class I = good, classes II-IV = poor) according to the whole-night and 5-minute analysis approaches. Secondarily, we examined the association of low overall FR rates or absence or incomplete resection of 1 dominant FR area with poor outcome.

RESULTS

The accuracy of outcome prediction was highest (81%, 95% confidence interval [CI] 67%-92%) with the use of the FR event resection ratio and whole-night recording (vs 72%, 95% CI 56%-85%, for the visual 5-minute approach). Absence of channels with FR rates >6/min (p = 0.001) and absence or incomplete resection of 1 dominant FR area (p < 0.001) were associated with poor outcome.

CONCLUSIONS

FRs are accurate in predicting epilepsy surgery outcome at the individual level when overnight recordings are used. Absence of channels with high FR rates or absence of 1 dominant FR area is a poor prognostic factor that may reflect suboptimal spatial sampling of the epileptogenic zone or multifocality, rather than an inherently low sensitivity of FRs.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that FRs are accurate in predicting epilepsy surgery outcome.

Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy

High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.

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.

MEEGIPS-A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations

High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also related to pathological processes in patients with epilepsy. Localization of the seizure onset zone, and, more specifically, of the to-be resected area in patients with refractory epilepsy seems to be supported by the detection of HFOs. The visual identification of HFOs is very time consuming with approximately 8 h for 10 min and 20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no software for visual marking or automated detection of HFOs meets the needs of everyday clinical practice and research. In the context of the currently available tools and for the purpose of related local HFO study activities we aimed at converging the advantages of clinical and experimental systems by designing and developing a comprehensive and extensible software framework for HFO analysis that, on the one hand, focuses on the requirements of clinical application and, on the other hand, facilitates the integration of experimental code and algorithms. The development project included the definition of use cases, specification of requirements, software design, implementation, and integration. The work comprised the engineering of component-specific requirements, component design, as well as component- and integration-tests. A functional and tested software package is the deliverable of this activity. The project MEEGIPS, a Modular EEG Investigation and Processing System for visual and automated detection of HFOs, introduces a highly user friendly software that includes five of the most prominent automated detection algorithms. Future evaluation of these, as well as implementation of further algorithms is facilitated by the modular software architecture.

Harmonization of pipeline for detection of HFOs in a rat model of post-traumatic epilepsy in preclinical multicenter study on post-traumatic epileptogenesis

Studies of chronic epilepsy show pathological high frequency oscillations (HFOs) are associated with brain areas capable of generating epileptic seizures. Only a few of these studies have focused on HFOs during the development of epilepsy, but results suggest pathological HFOs could be a biomarker of epileptogenesis. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy" (EpiBioS4Rx) is a multi-center project designed to identify biomarkers of epileptogenesis after a traumatic brain injury (TBI) and evaluate treatments that could modify or prevent the development of post-traumatic epilepsy. One goal of the EpiBioS4Rx project is to assess whether HFOs could be a biomarker of post-traumatic epileptogenesis. The current study describes the work towards this goal, including the development of common surgical procedures and EEG protocols, an interim analysis of the EEG for HFOs, and identifying issues that need to be addressed for a robust biomarker analysis. At three participating sites - University of Eastern Finland (UEF), Monash University in Melbourne (Melbourne) and University of California, Los Angeles (UCLA) - TBI was induced in adult male Sprague-Dawley rats by lateral fluid-percussion injury. After injury and in sham-operated controls, rats were implanted with screw and microwire electrodes positioned in neocortex and hippocampus to record EEG. A separate group of rats had serial magnetic resonance imaging after injury and then implanted with electrodes at 6 months. Recordings 28 days post-injury were available from UEF and UCLA, but not Melbourne due to technical issues with their EEG files. Analysis of recordings from 4 rats - UEF and UCLA each had one TBI and one sham-operated control - showed EEG contained evidence of HFOs. Computer-automated algorithms detected a total of 1,819 putative HFOs and of these only 40 events (2%) were detected by all three sites. Manual review of all events verified 130 events as HFO and the remainder as false positives. Review of the 40 events detected by all three sites was associated with 88% agreement. This initial report from the EpiBioS4Rx Consortium demonstrates the standardization of EEG electrode placements, recording protocol and long-term EEG monitoring, and differences in detection algorithm HFO results between sites. Additional work on detection strategy, detection algorithm performance, and training in HFO review will be performed to establish a robust, preclinical evaluation of HFOs as a biomarker of post-traumatic epileptogenesis.

High-frequency oscillations are not better biomarkers of epileptogenic tissues than spikes

OBJECTIVE

High-frequency oscillations (HFOs) in intracerebral EEG (stereoelectroencephalography; SEEG) are considered as better biomarkers of epileptogenic tissues than spikes. How this can be applied at the patient level remains poorly understood. We investigated how well HFOs and spikes can predict epileptogenic regions with a large spatial sampling at the patient level.

METHODS

We analyzed non-REM sleep SEEG recordings sampled at 2,048Hz of 30 patients. Ripples (Rs; 80-250Hz), fast ripples (FRs; 250-500Hz), and spikes were automatically detected. Rates of these markers and several combinations-spikes co-occurring with HFOs or FRs and cross-rate (Spk⊗HFO)-were compared to a quantified measure of the seizure onset zone (SOZ) by performing a receiver operating characteristic analysis for each patient individually. We used a Wilcoxon signed-rank test corrected for false-discovery rate to assess whether a marker was better than the others for predicting the SOZ.

RESULTS

A total of 2,930 channels was analyzed (median of 100 channels per patient). The HFOs or any of its variants were not statistically better than spikes. Only one feature, the cross-rate, was better than all the other markers. Moreover, fast ripples, even though very specific, were not delineating all epileptogenic tissues.

INTERPRETATION

At the patient level, the performance of HFOs is weakened by the presence of strong physiological HFO generators. Fast ripples are not sensitive enough to be the unique biomarker of epileptogenicity. Nevertheless, combining HFOs and spikes using our proposed measure-the cross-rate-is a better strategy than using only one marker. Ann Neurol 2018;83:84-97.

Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction

The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%.

Electrophysiological Evidence for the Development of a Self-Sustained Large-Scale Epileptic Network in the Kainate Mouse Model of Temporal Lobe Epilepsy

Most research on focal epilepsy focuses on mechanisms of seizure generation in the primary epileptic focus (EF). However, neurological deficits that are not directly linked to seizure activity and that may persist after focus removal are frequent. The recruitment of remote brain regions of an epileptic network (EN) is recognized as a possible cause, but a profound lack of experimental evidence exists concerning their recruitment and the type of pathological activities they exhibit. We studied the development of epileptic activities at the large-scale in male mice of the kainate model of unilateral temporal lobe epilepsy using high-density surface EEG and multiple-site intracortical recordings. We show that, along with focal spikes and fast ripples that remain localized to the injected hippocampus (i.e., the EF), a subpopulation of spikes that propagate across the brain progressively emerges even before the expression of seizures. The spatiotemporal propagation of these generalized spikes (GSs) is highly stable within and across animals, defining a large-scale EN comprising both hippocampal regions and frontal cortices. Interestingly, GSs are often concomitant with muscular twitches. In addition, while fast ripples are, as expected, highly frequent in the EF, they also emerge in remote cortical regions and in particular in frontal regions where GSs propagate. Finally, we demonstrate that these remote interictal activities are dependent on the focus in the early phase of the disease but continue to be expressed after focus silencing at later stages. Our results provide evidence that neuronal networks outside the initial focus are progressively altered during epileptogenesis.SIGNIFICANCE STATEMENT It has long been held that the epileptic focus is responsible for triggering seizures and driving interictal activities. However, focal epilepsies are associated with heterogeneous symptoms, calling into question the concept of a strictly focal disease. Using the mouse model of hippocampal sclerosis, this work demonstrates that focal epilepsy leads to the development of pathological activities specific to the epileptic condition, notably fast ripples, that appear outside of the primary epileptic focus. Whereas these activities are dependent on the focus early in the disease, focus silencing fails to control them in the chronic stage. Thus, dynamical changes specific to the epileptic condition are built up outside of the epileptic focus along with disease progression, which provides supporting evidence for network alterations in focal epilepsy.

Characterization of Hand Clenching in Human Sensorimotor Cortex Using High-, and Ultra-High Frequency Band Modulations of Electrocorticogram

Functional mapping of eloquent cortex before the resection of a tumor is a critical procedure for optimizing survival and quality of life. In order to locate the hand area of the motor cortex in two patients with low-grade gliomas (LGG), we recorded electrocorticogram (ECoG) from a 113 channel hybrid high-density grid (64 large contacts with diameter of 2.7 mm and 49 small contacts with diameter of 1 mm) while they executed hand clenching movements. We investigated the spatio-spectral characteristics of the neural oscillatory activity and observed that, in both patients, the hand movements were consistently associated with a wide spread power decrease in the low frequency band (LFB: 8–32 Hz) and a more localized power increase in the high frequency band (HFB: 60–280 Hz) within the sensorimotor region. Importantly, we observed significant power increase in the ultra-high frequency band (UFB: 300–800 Hz) during hand movements of both patients within a restricted cortical region close to the central sulcus, and the motor cortical “hand knob.” Among all frequency bands we studied, the UFB modulations were closest to the central sulcus and direct cortical stimulation (DCS) positive site. Both HFB and UFB modulations exhibited different timing characteristics at different locations. Power increase in HFB and UFB starting before movement onset was observed mostly at the anterior part of the activated cortical region. In addition, the spatial patterns in HFB and UFB indicated a probable postcentral shift of the hand motor function in one of the patients. We also compared the task related subband modulations captured by the small and large contacts in our hybrid grid. We did not find any significant difference in terms of band power changes. This study shows initial evidence that event-driven neural oscillatory activity recorded from ECoG can reach up to 800 Hz. The spatial distribution of UFB oscillations was found to be more focalized and closer to the central sulcus compared to LFB and HFB. More studies are needed to characterize further the functional significance of UFB relative to LFB and HFB.

Learning-enhanced coupling between ripple oscillations in association cortices and hippocampus

Consolidation of declarative memories requires hippocampal-neocortical communication. Although experimental evidence supports the role of sharp-wave ripples in transferring hippocampal information to the neocortex, the exact cortical destinations and the physiological mechanisms of such transfer are not known. We used a conducting polymer-based conformable microelectrode array (NeuroGrid) to record local field potentials and neural spiking across the dorsal cortical surface of the rat brain, combined with silicon probe recordings in the hippocampus, to identify candidate physiological patterns. Parietal, midline, and prefrontal, but not primary cortical areas, displayed localized ripple (100 to 150 hertz) oscillations during sleep, concurrent with hippocampal ripples. Coupling between hippocampal and neocortical ripples was strengthened during sleep following learning. These findings suggest that ripple-ripple coupling supports hippocampal-association cortical transfer of memory traces.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

OBJECTIVE

To develop and validate a detector that identifies ripple (80-200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ).

METHODS

iEEG recordings from 16 patients were first band-pass filtered (80-600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection.

RESULTS

The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p < .001).

CONCLUSIONS

Utilizing ICA to analyze iEEG recordings in referential montage provides many benefits to the study of high-frequency oscillations. The ripple rates and properties defined using this approach may accurately delineate the seizure onset zone.

SIGNIFICANCE

Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers.

Very high-frequency oscillations: Novel biomarkers of the epileptogenic zone

OBJECTIVE

In the present study, we aimed to investigate depth electroencephalographic (EEG) recordings in a large cohort of patients with drug-resistant epilepsy and to focus on interictal very high-frequency oscillations (VHFOs) between 500Hz and 2kHz. We hypothesized that interictal VHFOs are more specific biomarkers for epileptogenic zone compared to traditional HFOs.

METHODS

Forty patients with focal epilepsy who underwent presurgical stereo-EEG (SEEG) were included in the study. SEEG data were recorded with a sampling rate of 25kHz, and a 30-minute resting period was analyzed for each patient. Ten patients met selected criteria for analyses of correlations with surgical outcome: detection of interictal ripples (Rs), fast ripples (FRs), and VHFOs; resective surgery; and at least 1 year of postoperative follow-up. Using power envelope computation and visual inspection of power distribution matrixes, electrode contacts with HFOs and VHFOs were detected and analyzed.

RESULTS

Interictal very fast ripples (VFRs; 500-1,000Hz) were detected in 23 of 40 patients and ultrafast ripples (UFRs; 1,000-2,000Hz) in almost half of investigated subjects (n = 19). VFRs and UFRs were observed only in patients with temporal lobe epilepsy and were recorded exclusively from mesiotemporal structures. The UFRs were more spatially restricted in the brain than lower-frequency HFOs. When compared to R oscillations, significantly better outcomes were observed in patients with a higher percentage of removed contacts containing FRs, VFRs, and UFRs.

INTERPRETATION

Interictal VHFOs are relatively frequent abnormal phenomena in patients with epilepsy, and appear to be more specific biomarkers for epileptogenic zone when compared to traditional HFOs. Ann Neurol 2017;82:299-310.

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.

Computational analysis of network activity and spatial reach of sharp wave-ripples

Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex. To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses from neuronal populations monitored by conventional microelectrodes. In this work, we investigate spatiotemporal characteristics of SPW-Rs and how microelectrode size and distance influence SPW-R recordings using a biophysical model of hippocampus. We also explore contributions from neuronal spikes and synaptic potentials to SPW-Rs based on two different types of network activity. Our study suggests that neuronal spikes from pyramidal cells contribute significantly to ripples while high amplitude sharp waves mainly arise from synaptic activity. Our simulations on spatial reach of SPW-Rs show that the amplitudes of sharp waves and ripples exhibit a steep decrease with distance from the network and this effect is more prominent for smaller area electrodes. Furthermore, the amplitude of the signal decreases strongly with increasing electrode surface area as a result of averaging. The relative decrease is more pronounced when the recording electrode is closer to the source of the activity. Through simulations of field potentials across a high-density microelectrode array, we demonstrate the importance of finding the ideal spatial resolution for capturing SPW-Rs with great sensitivity. Our work provides insights on contributions from spikes and synaptic potentials to SPW-Rs and describes the effect of measurement configuration on LFPs to guide experimental studies towards improved SPW-R recordings.

Mechanisms for Selective Single-Cell Reactivation during Offline Sharp-Wave Ripples and Their Distortion by Fast Ripples

Memory traces are reactivated selectively during sharp-wave ripples. The mechanisms of selective reactivation, and how degraded reactivation affects memory, are poorly understood. We evaluated hippocampal single-cell activity during physiological and pathological sharp-wave ripples using juxtacellular and intracellular recordings in normal and epileptic rats with different memory abilities. CA1 pyramidal cells participate selectively during physiological events but fired together during epileptic fast ripples. We found that firing selectivity was dominated by an event- and cell-specific synaptic drive, modulated in single cells by changes in the excitatory/inhibitory ratio measured intracellularly. This mechanism collapses during pathological fast ripples to exacerbate and randomize neuronal firing. Acute administration of a use- and cell-type-dependent sodium channel blocker reduced neuronal collapse and randomness and improved recall in epileptic rats. We propose that cell-specific synaptic inputs govern firing selectivity of CA1 pyramidal cells during sharp-wave ripples.

How to record high-frequency oscillations in epilepsy: A practical guideline

OBJECTIVE

Technology for localizing epileptogenic brain regions plays a central role in surgical planning. Recent improvements in acquisition and electrode technology have revealed that high-frequency oscillations (HFOs) within the 80-500 Hz frequency range provide the neurophysiologist with new information about the extent of the epileptogenic tissue in addition to ictal and interictal lower frequency events. Nevertheless, two decades after their discovery there remain questions about HFOs as biomarkers of epileptogenic brain and there use in clinical practice.

METHODS

In this review, we provide practical, technical guidance for epileptologists and clinical researchers on recording, evaluation, and interpretation of ripples, fast ripples, and very high-frequency oscillations.

RESULTS

We emphasize the importance of low noise recording to minimize artifacts. HFO analysis, either visual or with automatic detection methods, of high fidelity recordings can still be challenging because of various artifacts including muscle, movement, and filtering. Magnetoencephalography and intracranial electroencephalography (iEEG) recordings are subject to the same artifacts.

SIGNIFICANCE

High-frequency oscillations are promising new biomarkers in epilepsy. This review provides interested researchers and clinicians with a review of current state of the art of recording and identification and potential challenges to clinical translation.

What are the assets and weaknesses of HFO detectors? A benchmark framework based on realistic simulations

High-frequency oscillations (HFO) have been suggested as biomarkers of epileptic tissues. While visual marking of these short and small oscillations is tedious and time-consuming, automatic HFO detectors have not yet met a large consensus. Even though detectors have been shown to perform well when validated against visual marking, the large number of false detections due to their lack of robustness hinder their clinical application. In this study, we developed a validation framework based on realistic and controlled simulations to quantify precisely the assets and weaknesses of current detectors. We constructed a dictionary of synthesized elements—HFOs and epileptic spikes—from different patients and brain areas by extracting these elements from the original data using discrete wavelet transform coefficients. These elements were then added to their corresponding simulated background activity (preserving patient- and region- specific spectra). We tested five existing detectors against this benchmark. Compared to other studies confronting detectors, we did not only ranked them according their performance but we investigated the reasons leading to these results. Our simulations, thanks to their realism and their variability, enabled us to highlight unreported issues of current detectors: (1) the lack of robust estimation of the background activity, (2) the underestimated impact of the 1/f spectrum, and (3) the inadequate criteria defining an HFO. We believe that our benchmark framework could be a valuable tool to translate HFOs into a clinical environment.

Automated detection of high-frequency oscillations in electrophysiological signals: Methodological advances

In recent years, new recording technologies have advanced such that oscillations of neuronal networks can be identified from simultaneous, multisite recordings at high temporal and spatial resolutions. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings also depends on the development of new mathematical methods capable of extracting meaningful information related to time, frequency and space. In this review, we aim to bridge this gap by focusing on the new analysis tools developed for the automated detection of high-frequency oscillations (HFOs, >40Hz) in local field potentials. For this, we provide a revision of different aspects associated with physiological and pathological HFOs as well as the several stages involved in their automatic detection including preprocessing, selection, rejection and analysis through time-frequency processes. Beyond basic research, the automatic detection of HFOs would greatly assist diagnosis of epilepsy disorders based on the recognition of these typical pathological patterns in the electroencephalogram (EEG). Also, we emphasize how these HFO detection methods can be applied and the properties that might be inferred from neuronal signals, indicating potential future directions.

Interictal high-frequency oscillations in focal human epilepsy

PURPOSE OF REVIEW

Localization of focal epileptic brain is critical for successful epilepsy surgery and focal brain stimulation. Despite significant progress, roughly half of all patients undergoing focal surgical resection, and most patients receiving focal electrical stimulation, are not seizure free. There is intense interest in high-frequency oscillations (HFOs) recorded with intracranial electroencephalography as potential biomarkers to improve epileptogenic brain localization, resective surgery, and focal electrical stimulation. The present review examines the evidence that HFOs are clinically useful biomarkers.

RECENT FINDINGS

Performing the PubMed search 'High-Frequency Oscillations and Epilepsy' for 2013-2015 identifies 308 articles exploring HFO characteristics, physiological significance, and potential clinical applications.

SUMMARY

There is strong evidence that HFOs are spatially associated with epileptic brain. There remain, however, significant challenges for clinical translation of HFOs as epileptogenic brain biomarkers: Differentiating true HFO from the high-frequency power changes associated with increased neuronal firing and bandpass filtering sharp transients. Distinguishing pathological HFO from normal physiological HFO. Classifying tissue under individual electrodes as normal or pathological. Sharing data and algorithms so research results can be reproduced across laboratories. Multicenter prospective trials to provide definitive evidence of clinical utility.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram

BACKGROUND

High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges.

NEW METHOD

The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis.

RESULTS

We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability.

COMPARISON WITH EXISTING METHOD

The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable.

CONCLUSIONS

Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable.

High frequency oscillations and high frequency functional network characteristics in the intraoperative electrocorticogram in epilepsy

OBJECTIVE

High frequency oscillations (HFOs; > 80 Hz), especially fast ripples (FRs, 250-500 Hz), are novel biomarkers for epileptogenic tissue. The pathophysiology suggests enhanced functional connectivity within FR generating tissue. Our aim was to determine the relation between brain areas showing FRs and 'baseline' functional connectivity within EEG networks, especially in the high frequency bands.

METHODS

We marked FRs, ripples (80-250 Hz) and spikes in the electrocorticogram of 14 patients with refractory temporal lobe epilepsy. We assessed 'baseline' functional connectivity in epochs free of epileptiform events within these recordings, using the phase lag index. We computed the Eigenvector Centrality (EC) per channel in the FR and gamma band network. We compared EC between channels that did or did not show events at other moments in time.

RESULTS

FR-band EC was higher in channels with than without spikes. Gamma-band EC was lower in channels with ripples and FRs.

CONCLUSIONS

We confirmed previous findings of functional isolation in the gamma-band and found a first proof of functional integration in the FR-band network of channels covering presumed epileptogenic tissue.

SIGNIFICANCE

'Baseline' high-frequency network parameters might help intra-operative recognition of epileptogenic tissue without the need for waiting for events. These findings can increase our understanding of the 'architecture' of epileptogenic networks and help unravel the pathophysiology of HFOs.

RIPPLELAB: A Comprehensive Application for the Detection, Analysis and Classification of High Frequency Oscillations in Electroencephalographic Signals

High Frequency Oscillations (HFOs) in the brain have been associated with different physiological and pathological processes. In epilepsy, HFOs might reflect a mechanism of epileptic phenomena, serving as a biomarker of epileptogenesis and epileptogenicity. Despite the valuable information provided by HFOs, their correct identification is a challenging task. A comprehensive application, RIPPLELAB, was developed to facilitate the analysis of HFOs. RIPPLELAB provides a wide range of tools for HFOs manual and automatic detection and visual validation; all of them are accessible from an intuitive graphical user interface. Four methods for automated detection-as well as several options for visualization and validation of detected events-were implemented and integrated in the application. Analysis of multiple files and channels is possible, and new options can be added by users. All features and capabilities implemented in RIPPLELAB for automatic detection were tested through the analysis of simulated signals and intracranial EEG recordings from epileptic patients (n = 16; 3,471 analyzed hours). Visual validation was also tested, and detected events were classified into different categories. Unlike other available software packages for EEG analysis, RIPPLELAB uniquely provides the appropriate graphical and algorithmic environment for HFOs detection (visual and automatic) and validation, in such a way that the power of elaborated detection methods are available to a wide range of users (experts and non-experts) through the use of this application. We believe that this open-source tool will facilitate and promote the collaboration between clinical and research centers working on the HFOs field. The tool is available under public license and is accessible through a dedicated web site.