High-Frequency Oscillations in Epilepsy: What Have We Learned and What Needs to be Addressed

Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra

BACKGROUND

Accurate identification of abnormal electroencephalographic (EEG) activity is pivotal for diagnosing and treating epilepsy. Recent studies indicate that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components can illuminate the drivers of spectral activity changes.

NEW METHODS

We analysed intracranial EEG (iEEG) data from 234 subjects, creating a normative map. This map was compared to a cohort of 63 patients with refractory focal epilepsy under consideration for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed, and (iii) the aperiodic exponent. Abnormalities were calculated for each approach in the patient cohort. We evaluated the spatial profiles, assessed their ability to localize abnormalities, and replicated the findings using magnetoencephalography (MEG).

RESULTS

Normative maps of relative complete band power and relative periodic band power exhibited similar spatial profiles, while the aperiodic normative map revealed higher exponent values in the temporal lobe. Abnormalities estimated through complete band power effectively distinguished between good and bad outcome patients. Combining periodic and aperiodic abnormalities enhanced performance, like the complete band power approach.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS

Sparing cerebral tissue with abnormalities in both periodic and aperiodic activity may result in poor surgical outcomes. Both periodic and aperiodic components do not carry sufficient information in isolation. The relative complete band power solution proved to be the most reliable method for this purpose. Future studies could investigate how cerebral location or pathology influences periodic or aperiodic abnormalities.

Application of HFO and scaling analysis of neuronal oscillations in the presurgical evaluation of focal epilepsy

PURPOSE

To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy.

METHODS

MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results.

RESULTS

Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ.

CONCLUSION

HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.

Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain

OBJECTIVE

The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zone (EZ).

METHODS

HFOs were detected in patients with focal epilepsy who underwent SEEG. Subsequently, HFOs within the seizure-onset and early spread zones were defined as pathological HFOs, whereas others were defined as physiological. Three features of HFOs were identified at the channel level, namely, morphological repetition, rhythmicity, and phase-amplitude coupling (PAC). A machine-learning (ML) classifier was then built to distinguish two HFO types at the channel level by application of the above-mentioned features, and the contributions were quantified. Further verification of the characteristics and classifier performance was performed in relation to various conscious states, imaging results, EZ location, and surgical outcomes.

RESULTS

Thirty-five patients were included in this study, from whom 166  104 pathological HFOs in 255 channels and 53 374 physiological HFOs in 282 channels were entered into the analysis pipeline. The results revealed that the morphological repetitions of pathological HFOs were markedly higher than those of the physiological HFOs; this was also observed for rhythmicity and PAC. The classifier exhibited high accuracy in differentiating between the two forms of HFOs, as indicated by an area under the curve (AUC) of 0.89. Both PAC and rhythmicity contributed significantly to this distinction. The subgroup analyses supported these findings.

SIGNIFICANCE

The suggested HFO features can accurately distinguish between pathological and physiological channels substantially improving its usefulness in clinical localization.

PLAIN LANGUAGE SUMMARY

In this study, we computed three quantitative features associated with HFOs in each SEEG channel and then constructed a machine learning-based classifier for the classification of pathological and physiological channels. The classifier performed well in distinguishing the two channel types under different levels of consciousness as well as in terms of imaging results, EZ location, and patient surgical outcomes.

Prognostic Value of Complete Resection of the High-Frequency Oscillation Area in Intracranial EEG: A Systematic Review and Meta-Analysis

BACKGROUND AND OBJECTIVES

High-frequency oscillations (HFOs; ripples 80-250 Hz; fast ripples [FRs] 250-500 Hz) recorded with intracranial electrodes generated excitement and debate about their potential to localize epileptogenic foci. We performed a systematic review and meta-analysis on the prognostic value of complete resection of the HFOs-area (crHFOs-area) for epilepsy surgical outcome in intracranial EEG (iEEG) accessing multiple subgroups.

METHODS

We searched PubMed, Embase, and Web of Science for original research from inception to October 27, 2022. We defined favorable surgical outcome (FSO) as Engel class I, International League Against Epilepsy class 1, or seizure-free status. The prognostic value of crHFOs-area for FSO was assessed by (1) the pooled FSO proportion after crHFOs-area; (2) FSO for crHFOs-area vs without crHFOs-area; and (3) the predictive performance. We defined high combined prognostic value as FSO proportion >80% + FSO crHFOs-area >without crHFOs-area + area under the curve (AUC) >0.75 and examined this for the clinical subgroups (study design, age, diagnostic type, HFOs-identification method, HFOs-rate thresholding, and iEEG state). Temporal lobe epilepsy (TLE) was compared with extra-TLE through dichotomous variable analysis. Individual patient analysis was performed for sex, affected hemisphere, MRI findings, surgery location, and pathology.

RESULTS

Of 1,387 studies screened, 31 studies (703 patients) met our eligibility criteria. Twenty-seven studies (602 patients) analyzed FRs and 20 studies (424 patients) ripples. Pooled FSO proportion after crHFOs-area was 81% (95% CI 76%-86%) for FRs and 82% (73%-89%) for ripples. Patients with crHFOs-area achieved more often FSO than those without crHFOs-area (FRs odds ratio [OR] 6.38, 4.03-10.09, p < 0.001; ripples 4.04, 2.32-7.04, p < 0.001). The pooled AUCs were 0.81 (0.77-0.84) for FRs and 0.76 (0.72-0.79) for ripples. Combined prognostic value was high in 10 subgroups: retrospective, children, long-term iEEG, threshold (FRs and ripples) and automated detection and interictal (FRs). FSO after complete resection of FRs-area (crFRs-area) was achieved less often in people with TLE than extra-TLE (OR 0.37, 0.15-0.89, p = 0.006). Individual patient analyses showed that crFRs-area was seen more in patients with FSO with than without MRI lesions (p = 0.02 after multiple correction).

DISCUSSION

Complete resection of the brain area with HFOs is associated with good postsurgical outcome. Its prognostic value holds, especially for FRs, for various subgroups. The use of HFOs for extra-TLE patients requires further evidence.

A profile on the WISE cortical strip for intraoperative neurophysiological monitoring

INTRODUCTION

During intraoperative neurophysiological monitoring in neurosurgery, brain electrodes are placed to record electrocorticography or to inject current for direct cortical stimulation. A low impedance electrode may improve signal quality.

AREAS COVERED

We review here a brain electrode (WISE Cortical Strip, WCS®), where a thin polymer strip embeds platinum nanoparticles to create conductive electrode contacts. The low impedance contacts enable a high signal-to-noise ratio, allowing for better detection of small signals such as high-frequency oscillations (HFO). The softness of the WCS may hinder sliding the electrode under the dura or advancing it to deeper structures as the hippocampus but assures conformability with the cortex even in the resection cavity. We provide an extensive review on WCS including a market overview, an introduction to the device (mechanistics, cost aspects, performance standards, safety and contraindications) and an overview of the available pre- and post-approval data.

EXPERT OPINION

The WCS improves signal detection by lower impedance and better conformability to the cortex. The higher signal-to-noise ratio improves the detection of challenging signals. The softness of the electrode may be a disadvantage in some applications and an advantage in others.

Pathological and Physiological High-frequency Oscillations on Electroencephalography in Patients with Epilepsy

High-frequency oscillations (HFOs) encompass ripples (80 Hz-200 Hz) and fast ripples (200 Hz-600 Hz), serving as a promising biomarker for localizing the epileptogenic zone in epilepsy. Spontaneous fast ripples are always pathological, while ripples may be physiological or pathological. Distinguishing physiological from pathological ripples is important not only for designating epileptogenic brain regions, but also for investigations that study ripples in the context of memory encoding, consolidation, and recall in patients with epilepsy. Many studies have sought to identify distinguishing features between pathological and physiological ripples over the past two decades. Physiological and pathological ripples differ with respect to their spatial location, cellular mechanisms, morphology, and coupling with background electroencephalographic activity. Retrospective studies have demonstrated that differentiating between pathological and physiological ripples can improve surgical outcome prediction. In this review, we summarize the characteristics, differences, and applications of pathological and physiological HFOs and discuss strategies for their clinical translation.

Pilot trial of perampanel on peritumoral hyperexcitability and clinical outcomes in newly diagnosed high-grade glioma

Background Glutamatergic neuron-glioma synaptogenesis and peritumoral hyperexcitability promote glioma growth in a positive feedback loop. The objective of this study was to evaluate the feasibility and estimated effect sizes of the AMPA-R antagonist, perampanel, on intraoperative electrophysiologic hyperexcitability and clinical outcomes. Methods An open-label trial was performed comparing perampanel to standard of care (SOC) in patients undergoing resection of newly-diagnosed radiologic high-grade glioma. Perampanel was administered as a pre-operative loading dose followed by maintenance therapy until progressive disease or up to 12-months. SOC treatment involved levetiracetam for 7-days or as clinically indicated. The primary outcome of hyperexcitability was defined by intra-operative electrocorticography high frequency oscillation (HFO) rates. Seizure-freedom and overall survival (OS) were estimated by the Kaplan-Meier method. Tissue concentrations of perampanel, levetiracetam, and metabolites were measured by mass spectrometry. Results HFO rates were similar between perampanel-treated and SOC cohorts. The trial was terminated early after interim analysis for futility, and outcomes assessed in 11 patients (7 perampanel-treated, 4 SOC). Over a median 281 days of post-enrollment follow-up, 27% of patients had seizures, including 14% treated with perampanel and 50% treated with SOC. OS in perampanel-treated patients was similar to a glioblastoma reference cohort (p=0.81). Glutamate concentrations in surface biopsies were positively correlated with HFO rates in adjacent electrode contacts and were not significantly associated with treatment assignment or drug concentrations. Conclusions A peri-operative loading regimen of perampanel was safe and well-tolerated, with similar peritumoral hyperexcitability as in levetiracetam-treated patients. Maintenance anti-glutamatergic therapy was not observed to impact survival outcomes.

High frequency oscillations in relation to interictal spikes in predicting postsurgical seizure freedom

We evaluate whether interictal spikes, epileptiform HFOs and their co-occurrence (Spike + HFO) were included in the resection area with respect to seizure outcome. We also characterise the relationship between high frequency oscillations (HFOs) and propagating spikes. We analysed intracranial EEG of 20 patients that underwent resective epilepsy surgery. The co-occurrence of ripples and fast ripples was considered an HFO event; the co-occurrence of an interictal spike and HFO was considered a Spike + HFO event. HFO distribution and spike onset were compared in cases of spike propagation. Accuracy in predicting seizure outcome was 85% for HFO, 60% for Spikes, and 79% for Spike + HFO. Sensitivity was 57% for HFO, 71% for Spikes and 67% for Spikes + HFO. Specificity was 100% for HFO, 54% for Spikes and 85% for Spikes + HFO. In 2/2 patients with spike propagation, the spike onset included the HFO area. Combining interictal spikes with HFO had comparable accuracy to HFO. In patients with propagating spikes, HFO rate was maximal at the onset of spike propagation.

Passive and active markers of cortical excitability in epilepsy

Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.

Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography

OBJECTIVE

High-frequency oscillations (HFOs) are an efficient indicator to locate the epileptogenic zone (EZ). However, physiological HFOs produced in the normal brain region may interfere with EZ localization. The present study aimed to build a machine learning-based classifier to distinguish the properties of each HFO event based on features in different domains.

METHODS

HFOs were detected in focal epilepsy patients from two different hospitals who underwent stereoelectroencephalography and subsequent resection surgery. Subsequently, 37 features in four different domains (time, frequency and time-frequency, entropy-based and nonlinear) were extracted for each HFO. After extraction, a fast correlation-based filter (FCBF) algorithm was applied for feature selection. The machine learning classifier was trained on the feature matrix with and without FCBF and then tested on the data set from patients in another hospital.

RESULTS

A dataset was compiled, consisting of 89,844 pathological HFOs and 23,613 physiological HFOs from 17 patients assigned to the training dataset. Additionally, 12,695 pathological HFOs and 5,599 physiological HFOs from 9 patients were assigned to the testing dataset. Four features (ripple band power, arithmetic mean, Petrosian fractal dimension and zero crossings) were obtained for classifier training after FCBF. The classifier showed an area under the curve (AUC) of 0.95/0.98 for FCBF/no FCBF features in the training dataset and AUC of 0.82/0.90 for FCBF/no FCBF features in the testing dataset. Our findings indicated that the classifier utilizing all features demonstrated superior performance compared to the one relying on FCBF-processed features.

CONCLUSION

Our classifier could reliably differentiate pathological HFOs from physiological ones, which could promote the development of HFOs in EZ localization.

Developmental atlas of phase-amplitude coupling between physiologic high-frequency oscillations and slow waves

We investigated the developmental changes in high-frequency oscillation (HFO) and Modulation Index (MI) - the coupling measure between HFO and slow-wave phase. We generated normative brain atlases, using subdural EEG signals from 8251 nonepileptic electrode sites in 114 patients (ages 1.0-41.5 years) who achieved seizure control following resective epilepsy surgery. We observed a higher MI in the occipital lobe across all ages, and occipital MI increased notably during early childhood. The cortical areas exhibiting MI co-growth were connected via the vertical occipital fasciculi and posterior callosal fibers. While occipital HFO rate showed no significant age-association, the temporal, frontal, and parietal lobes exhibited an age-inversed HFO rate. Assessment of 1006 seizure onset sites revealed that z-score normalized MI and HFO rate were higher at seizure onset versus nonepileptic electrode sites. We have publicly shared our intracranial EEG data to enable investigators to validate MI and HFO-centric presurgical evaluations to identify the epileptogenic zone.

Assessing epilepsy-related autonomic manifestations: Beyond cardiac and respiratory investigations

The Autonomic Nervous System (ANS) regulates many critical physiological functions. Its control relies on cortical input, especially limbic areas, which are often involved in epilepsy. Peri-ictal autonomic dysfunction is now well documented, but inter-ictal dysregulation is less studied. In this review, we discuss the available data on epilepsy-related autonomic dysfunction and the objective tests available. Epilepsy is associated with sympathetic-parasympathetic imbalance and a shift towards sympathetic dominance. Objective tests report alterations in heart rate, baroreflex function, cerebral autoregulation, sweat glands activity, thermoregulation, gastrointestinal and urinary function. However, some tests have found contradictory results and many tests suffer from a lack of sensitivity and reproducibility. Further study on interictal ANS function is required to further understand autonomic dysregulation and the potential association with clinically-relevant complications, including risk of Sudden Unexpected Death In Epilepsy (SUDEP).

Hippocampal area CA2 controls seizure dynamics, interictal EEG abnormalities and social comorbidity in mouse models of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is characterized by spontaneous recurrent seizures, abnormal activity between seizures, and impaired behavior. CA2 pyramidal neurons (PNs) are potentially important because inhibiting them with a chemogenetic approach reduces seizure frequency in a mouse model of TLE. However, whether seizures could be stopped by timing inhibition just as a seizure begins is unclear. Furthermore, whether inhibition would reduce the cortical and motor manifestations of seizures are not clear. Finally, whether interictal EEG abnormalities and TLE comorbidities would be improved are unknown. Therefore, real-time optogenetic silencing of CA2 PNs during seizures, interictal activity and behavior were studied in 2 mouse models of TLE. CA2 silencing significantly reduced seizure duration and time spent in convulsive behavior. Interictal spikes and high frequency oscillations were significantly reduced, and social behavior was improved. Therefore, brief focal silencing of CA2 PNs reduces seizures, their propagation, and convulsive manifestations, improves interictal EEG, and ameliorates social comorbidities.

HIGHLIGHTS

Real-time CA2 silencing at the onset of seizures reduces seizure durationWhen CA2 silencing reduces seizure activity in hippocampus it also reduces cortical seizure activity and convulsive manifestations of seizuresInterictal spikes and high frequency oscillations are reduced by real-time CA2 silencingReal-time CA2 silencing of high frequency oscillations (>250Hz) rescues social memory deficits of chronic epileptic mice.

The anterior nucleus of the thalamus plays a role in the epileptic network

OBJECTIVES

We investigated both the metabolic differences and interictal/ictal discharges of the anterior nucleus of the thalamus (ANT) in patients with epilepsy to clarify the relationship between the ANT and the epileptic network.

METHODS

Nineteen patients with drug-resistant epilepsy who underwent stereoelectroencephalography were studied. Metabolic differences in ANT were analyzed using [18F] fluorodeoxyglucose-positron emission tomography with three-dimensional (3D) visual and quantitative analyses. Interictal and ictal discharges in the ANT were analyzed using visual and time-frequency analyses. The relationship between interictal discharge and metabolic differences was analyzed.

RESULTS

We found that patients with temporal lobe epilepsy (TLE) showed significant metabolic differences in bilateral ANT compared with extratemporal lobe epilepsy in 3D visual and quantitative analyses. Four types of interictal activities were recorded from the ANT: spike, high-frequency oscillation (HFO), slow-wave, and α-rhythmic activity. Spike and HFO waveforms were recorded mainly in patients with TLE. Two spike patterns were recorded: synchronous and independent. In 83.3% of patients, ANT was involved during seizures. Three seizure onset types of ANT were recorded: low-voltage fast activity, rhythmic spikes, and theta band discharge. The time interval of seizure onset between the seizure onset zone and ANT showed two patterns: immediate and delayed.

INTERPRETATION

ANT can receive either interictal discharges or ictal discharges which propagate from the epileptogenic zones. Independent epileptic discharges can also be recorded from the ANT in some patients. Metabolic anomalies and epileptic discharges in the ANT indicate that the ANT plays a role in the epileptic network in most patients with epilepsy, especially TLE.

Intraoperative electrocorticography using high-frequency oscillations or spikes to tailor epilepsy surgery in the Netherlands (the HFO trial): a randomised, single-blind, adaptive non-inferiority trial

Background

Intraoperative electrocorticography is used to tailor epilepsy surgery by analysing interictal spikes or spike patterns that can delineate epileptogenic tissue. High-frequency oscillations (HFOs) on intraoperative electrocorticography have been proposed as a new biomarker of epileptogenic tissue, with higher specificity than spikes. We prospectively tested the non-inferiority of HFO-guided tailoring of epilepsy surgery to spike-guided tailoring on seizure freedom at 1 year.

Methods

The HFO trial was a randomised, single-blind, adaptive non-inferiority trial at an epilepsy surgery centre (UMC Utrecht) in the Netherlands. We recruited children and adults (no age limits) who had been referred for intraoperative electrocorticography-tailored epilepsy surgery. Participants were randomly allocated (1:1) to either HFO-guided or spike-guided tailoring, using an online randomisation scheme with permuted blocks generated by an independent data manager, stratified by epilepsy type. Treatment allocation was masked to participants and clinicians who documented seizure outcome, but not to the study team or neurosurgeon. Ictiform spike patterns were always considered in surgical decision making. The primary endpoint was seizure outcome after 1 year (dichotomised as seizure freedom [defined as Engel 1A–B] vs seizure recurrence [Engel 1C–4]). We predefined a non-inferiority margin of 10% risk difference. Analysis was by intention to treat, with prespecified subgroup analyses by epilepsy type and for confounders. This completed trial is registered with the Dutch Trial Register, Toetsingonline ABR.NL44527.041.13, and ClinicalTrials.gov, NCT02207673.

Findings

Between Oct 10, 2014, and Jan 31, 2020, 78 individuals were enrolled to the study and randomly assigned (39 to HFO-guided tailoring and 39 to spike-guided tailoring). There was no loss to follow-up. Seizure freedom at 1 year occurred in 26 (67%) of 39 participants in the HFO-guided group and 35 (90%) of 39 in the spike-guided group (risk difference –23·5%, 90% CI –39·1 to –7·9; for the 48 patients with temporal lobe epilepsy, the risk difference was –25·5%, –45·1 to –6·0, and for the 30 patients with extratemporal lobe epilepsy it was –20·3%, –46·0 to 5·4). Pathology associated with poor prognosis was identified as a confounding factor, with an adjusted risk difference of –7·9% (90% CI –20·7 to 4·9; adjusted risk difference –12·5%, –31·0 to 5·9, for temporal lobe epilepsy and 5·8%, –7·7 to 19·5, for extratemporal lobe epilepsy). We recorded eight serious adverse events (five in the HFO-guided group and three in the spike-guided group) requiring hospitalisation. No patients died.

Interpretation

HFO-guided tailoring of epilepsy surgery was not non-inferior to spike-guided tailoring on intraoperative electrocorticography. After adjustment for confounders, HFOs show non-inferiority in extratemporal lobe epilepsy. This trial challenges the clinical value of HFOs as an epilepsy biomarker, especially in temporal lobe epilepsy. Further research is needed to establish whether HFO-guided intraoperative electrocorticography holds promise in extratemporal lobe epilepsy.

Funding

UMCU Alexandre Suerman, EpilepsieNL, RMI Talent Fellowship, European Research Council, and MING Fund.

Epileptogenic high-frequency oscillations present larger amplitude both in mesial temporal and neocortical regions

High-frequency oscillations (HFO) are a promising biomarker for the identification of epileptogenic tissue. While HFO rates have been shown to predict seizure outcome, it is not yet clear whether their morphological features might improve this prediction. We validated HFO rates against seizure outcome and delineated the distribution of HFO morphological features. We collected stereo-EEG recordings from 20 patients (231 electrodes; 1,943 contacts). We computed HFO rates (the co-occurrence of ripples and fast ripples) through a validated automated detector during non-rapid eye movement sleep. Applying machine learning, we delineated HFO morphological features within and outside epileptogenic tissue across mesial temporal lobe (MTL) and Neocortex. HFO rates predicted seizure outcome with 85% accuracy, 79% specificity, 100% sensitivity, 100% negative predictive value, and 67% positive predictive value. The analysis of HFO features showed larger amplitude in the epileptogenic tissue, similar morphology for epileptogenic HFO in MTL and Neocortex, and larger amplitude for physiological HFO in MTL. We confirmed HFO rates as a reliable biomarker for epilepsy surgery and characterized the potential clinical relevance of HFO morphological features. Our results support the prospective use of HFO in epilepsy surgery and contribute to the anatomical mapping of HFO morphology.

Seizure Forecasting by High-Frequency Activity (80-170 Hz) in Long-term Continuous Intracranial EEG Recordings

BACKGROUND AND OBJECTIVES

Reliable seizure forecasting has important implications in epilepsy treatment and improving the quality of lives for people with epilepsy. High-frequency activity (HFA) is a biomarker that has received significant attention over the past 2 decades, but its predictive value in seizure forecasting remains uncertain. This work aimed to determine the utility of HFA in seizure forecasting.

METHODS

We used seizure data and HFA (80-170 Hz) data obtained from long-term, continuous intracranial EEG recordings of patients with drug-resistant epilepsy. Instantaneous rates and phases of HFA cycles were used as features for seizure forecasting. Seizure forecasts based on each individual HFA feature, and with the use of a combined approach, were generated pseudo-prospectively (causally). To compute the instantaneous phases for pseudo-prospective forecasting, real-time phase estimation based on an autoregressive model was used. Features were combined with a weighted average approach. The performance of seizure forecasting was primarily evaluated by the area under the curve (AUC).

RESULTS

Of 15 studied patients (median recording duration 557 days, median seizures 151), 12 patients with >10 seizures after 100 recording days were included in the pseudo-prospective analysis. The presented real-time phase estimation is feasible and can causally estimate the instantaneous phases of HFA cycles with high accuracy. Pseudo-prospective seizure forecasting based on HFA rates and phases performed significantly better than chance in 11 of 12 patients, although there were patient-specific differences. Combining rate and phase information improved forecasting performance compared to using either feature alone. The combined forecast using the best-performing channel yielded a median AUC of 0.70, a median sensitivity of 0.57, and a median specificity of 0.77.

DISCUSSION

These findings show that HFA could be useful for seizure forecasting and represent proof of concept for using prior information of patient-specific relationships between HFA and seizures in pseudo-prospective forecasting. Future seizure forecasting algorithms might benefit from the inclusion of HFA, and the real-time phase estimation approach can be extended to other biomarkers.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that HFA (80-170 Hz) in long-term continuous intracranial EEG can be useful to forecast seizures in patients with refractory epilepsy.

Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks

Interictal high-frequency oscillations (HFO) detected in electroencephalography recordings have been proposed as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. Automatic HFO detectors typically analyze the data offline using complex time-consuming algorithms, which limits their clinical application. Neuromorphic circuits offer the possibility of building compact and low-power processing systems that can analyze data on-line and in real time. In this review, we describe a fully automated detection pipeline for HFO that uses, for the first time, spiking neural networks and neuromorphic technology. We demonstrated that our HFO detection pipeline can be applied to recordings from different modalities (intracranial electroencephalography, electrocorticography, and scalp electroencephalography) and validated its operation in a custom-designed neuromorphic processor. Our HFO detection approach resulted in high accuracy and specificity in the prediction of seizure outcome in patients implanted with intracranial electroencephalography and electrocorticography, and in the prediction of epilepsy severity in patients recorded with scalp electroencephalography. Our research provides a further step toward the real-time detection of HFO using compact and low-power neuromorphic devices. The real-time detection of HFO in the operation room may improve the seizure outcome of epilepsy surgery, while the use of our neuromorphic processor for non-invasive therapy monitoring might allow for more effective medication strategies to achieve seizure control. Therefore, this work has the potential to improve the quality of life in patients with epilepsy by improving epilepsy diagnostics and treatment.

A pilot randomized controlled clinical trial of Transcranial Alternating Current Stimulation in patients with multifocal pharmaco-resistant epilepsy

Transcranial Alternating Current Stimulation (tACS) is a promising noninvasive electrical stimulation therapy for neuropsychiatric diseases. Invasive neuromodulation using alternating current has been efficacious for drug-resistant epilepsy, but it is associated with surgical and medical complications. We aimed to explore the safeness and effectivity on seizure frequency reduction of two tACS protocols against placebo in patients with multifocal refractory epilepsy. This was a randomized, double-blinded, placebo-controlled clinical trial with 3-arm parallel-group (placebo, 30 min/2 mA daily sessions for 3 days [tACS-30], and 60 min/2 mA weekday sessions [tACS-60]). The main outcome was considered a change in reducing seizure frequency at 2 months after the intervention. Secondary outcomes were the apparition of any adverse effects during follow-up. At the second month, we observed a nonsignificant reduction in the seizure frequency in the placebo (7.3 ± 40.4%, p > 0.05) and the tACS-60 (26 ± 37.7%, p > 0.05). While the tACS-30 group showed a nonsignificant increase in seizure frequency (63.6 ± 155.3%, p > 0.05). No changes were statistically different from the placebo group. Otherwise, participants experienced only minor adverse events - the most common being an initial local transient tingling sensation (21%). This pilot study of tACS raises no severe safety issues, but provides negligible evidence for efficacy using this brief treatment protocol. Therefore, more studies are warranted testing different parameters to further verify the safety and effectivity of tACS in multifocal epilepsy.

Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue

The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localizing epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner. To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 participants (21 598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We proposed that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures postoperatively. We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions that were spared by surgery were more abnormal than those resected only in patients with persistent postoperative seizures (t = -3.6, P = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (area under curve 0.75 P = 0.0003). Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.

A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG

Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long-term EEG recording. Spiking neural networks (SNN) have emerged as optimal architectures for embedding in compact low-power signal processing hardware. We analyzed 20 scalp EEG recordings from 11 pediatric focal lesional epilepsy patients. We designed a custom SNN to detect events of interest (EoI) in the 80–250 Hz ripple band and reject artifacts in the 500–900 Hz band. We identified the optimal SNN parameters to detect EoI and reject artifacts automatically. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.90 CI [0.75 0.96], p < 0.0001, Spearman’s correlation). The fully automated SNN detected clinically relevant HFO in the scalp EEG. This study is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.

Are High Frequency Oscillations in Scalp EEG Related to Age?

BACKGROUND

High-frequency oscillations (HFOs) have received much attention in recent years, particularly in the clinical context. In addition to their application as a marker for pathological changes in patients with epilepsy, HFOs have also been brought into context with several physiological mechanisms. Furthermore, recent studies reported a relation between an increase of HFO rate and age in invasive EEG recordings. The present study aimed to investigate whether this relation can be replicated in scalp-EEG.

METHODS

We recorded high-density EEG from 11 epilepsy patients at rest as well as during motor performance. Manual detection of HFOs was performed by two independent raters following a standardized protocol. Patients were grouped by age into younger (<25 years) and older (>50 years) participants.

RESULTS

No significant difference of HFO-rates was found between groups [U = 10.5, p = 0.429, r = 0.3].

CONCLUSIONS

Lack of replicability of the age effect of HFOs may be due to the local propagation patterns of age-related HFOs occurring in deep structures. However, limitations such as small sample size, decreased signal-to-noise ratio as compared to invasive recordings, as well as HFO-mimicking artifacts must be considered.

Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation

Accurate identification of high-frequency oscillation (HFO) is an important prerequisite for precise localization of epileptic foci and good prognosis of drug-refractory epilepsy. Exploring a high-performance automatic detection method for HFOs can effectively help clinicians reduce the error rate and reduce manpower. Due to the limited analysis perspective and simple model design, it is difficult to meet the requirements of clinical application by the existing methods. Therefore, an end-to-end bi-branch fusion model is proposed to automatically detect HFOs. With the filtered band-pass signal (signal branch) and time-frequency image (TFpic branch) as the input of the model, two backbone networks for deep feature extraction are established, respectively. Specifically, a hybrid model based on ResNet1d and long short-term memory (LSTM) is designed for signal branch, which can focus on both the features in time and space dimension, while a ResNet2d with a Convolutional Block Attention Module (CBAM) is constructed for TFpic branch, by which more attention is paid to useful information of TF images. Then the outputs of two branches are fused to realize end-to-end automatic identification of HFOs. Our method is verified on 5 patients with intractable epilepsy. In intravalidation, the proposed method obtained high sensitivity of 94.62%, specificity of 92.7%, and F1-score of 93.33%, and in cross-validation, our method achieved high sensitivity of 92.00%, specificity of 88.26%, and F1-score of 89.11% on average. The results show that the proposed method outperforms the existing detection paradigms of either single signal or single time-frequency diagram strategy. In addition, the average kappa coefficient of visual analysis and automatic detection results is 0.795. The method shows strong generalization ability and high degree of consistency with the gold standard meanwhile. Therefore, it has great potential to be a clinical assistant tool.

Blinded study: prospectively defined high-frequency oscillations predict seizure outcome in individual patients

Interictal high-frequency oscillations are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant high-frequency oscillations in intracranial EEG from Montreal and tested it in recordings from Zurich. We here validated the algorithm on intracranial EEG that was recorded in an independent epilepsy centre so that the analysis was blinded to seizure outcome. We selected consecutive patients who underwent resective epilepsy surgery in Geneva with post-surgical follow-up > 12 months. We analysed long-term recordings during sleep that we segmented into intervals of 5 min. High-frequency oscillations were defined in the ripple (80–250 Hz) and the fast ripple (250–500 Hz) frequency bands. Contacts with the highest rate of ripples co-occurring with fast ripples designated the relevant area. As a validity criterion, we calculated the test–retest reliability of the high-frequency oscillations area between the 5 min intervals (dwell time ≥50%). If the area was not fully resected and the patient suffered from recurrent seizures, this was classified as a true positive prediction. We included recordings from 16 patients (median age 32 years, range 18–53 years) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 months, range 12–55 months). For each patient, we included several 5 min intervals (median 17 intervals). The relevant area had high test–retest reliability across intervals (median dwell time 95%). In two patients, the test–retest reliability was too low (dwell time < 50%) so that outcome prediction was not possible. The area was fully included in the resected volume in 2/4 patients who achieved post-operative seizure freedom (specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (sensitivity 90%), leading to an accuracy of 79%. An additional exploratory analysis suggested that high-frequency oscillations were associated with interictal epileptic discharges only in channels within the relevant area and not associated in channels outside the area. We thereby validated the automated procedure to delineate the clinically relevant area in each individual patient of an independently recorded dataset and achieved the same good accuracy as in our previous studies. The reproducibility of our results across datasets is promising for a multicentre study to test the clinical application of high-frequency oscillations to guide epilepsy surgery.

Altered functional connectivity in newly diagnosed benign epilepsy with unilateral or bilateral centrotemporal spikes: A multi-frequency MEG study

OBJECTIVE

Rolandic epilepsy (RE) is one of the most common forms of epilepsy syndromes in children. The condition is usually accompanied with either unilateral or bilateral centrotemporal epileptic discharge. Despite the term "benign", many studies have reported that children with benign epilepsy with centrotemporal spikes (BECTS) display a range of pervasive cognitive difficulties. In addition, existing research suggests that unilateral and bilateral centrotemporal spikes may affect cognition through different mechanisms. Consequently, the present study aimed to investigate cognitive impairment and the resting-state network topology of children with benign epilepsy with unilateral centrotemporal spikes (U-BECTS) and with bilateral centrotemporal spikes (B-BECTS).

METHODS

This study recruited 14 children with U-BECTS and 14 with B-BECTS. Thereafter, cognition was assessed in 28 children with BECTS and 14 healthy controls, using the fourth edition of the Wechsler Intelligence Scale (WISC-IV). Additionally, the functional network of the brain was constructed through magnetoencephalography (MEG) to record the resting-state brain magnetic signals of the brain and by computing virtual sensor waveforms at the source level. Moreover, graph theory (GT) analysis was used to assess the properties of the brain network.

RESULTS

Children in the B-BECTS group had an earlier onset of epilepsy compared to those in the U-BECTS category. In addition, both the B-BECTS and U-BECTS groups had lower Full Scale Intelligence Quotient (FSIQ), Verbal Comprehension Index (VCI), and Working Memory Index (WMI) scores, compared to the healthy controls although only children in the B-BECTS category had lower Perceptual Reasoning Index (PRI) scores. The results also showed that both BECTS groups had increased frontal cortex connectivity in specific frequency bands. Notably, children with B-BECTS showed a more disorderly and randomized network in the 1-4-Hz and 80-250-Hz frequency bands. Moreover, GT analysis showed that children with B-BECTS had lower clustering coefficient and characteristic path length in the 80-250-Hz frequency bands and higher connection strength in the 4-8-Hz frequency bands. On the other hand, the U-BECTS group had a higher clustering coefficient in the 8-12-Hz frequency bands, compared to the healthy controls. Correlation analysis revealed that there were negative correlations between network parameters, clinical characteristics, and neuropsychological data in the U-BECTS category.

CONCLUSION

The findings revealed that children with BECTS display a diffuse early cognitive deficit. In addition, resting-state suboptimal network topology may be the mechanism of cognitive impairment in children with BECTS. The study also showed that and children with B-BECTS may be at a higher risk of cognitive impairment.

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.

A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG

To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative electrocorticography (ECoG) recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an open challenge. Here we present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation. We trained the SNN to detect HFO signals measured from intraoperative ECoG on-line, using an independently labeled dataset (58 min, 16 recordings). We targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz) and compared the network results with the labeled HFO data. We endowed the SNN with a novel artifact rejection mechanism to suppress sharp transients and demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6 HFO/min in pre-resection recordings) detected by this SNN are comparable to those published in the dataset (Spearman's [Formula: see text] = 0.81). The postsurgical seizure outcome was "predicted" with 100% (CI [63 100%]) accuracy for all 8 patients. These results provide a further step towards the construction of a real-time portable battery-operated HFO detection system that can be used during epilepsy surgery to guide the resection of the epileptogenic zone.

Epileptic High-Frequency Oscillations in Intracranial EEG Are Not Confounded by Cognitive Tasks

Rationale: High-frequency oscillations (HFOs) in intracranial EEG (iEEG) are used to delineate the epileptogenic zone during presurgical diagnostic assessment in patients with epilepsy. HFOs are historically divided into ripples (80-250 Hz), fast ripples (FR, >250 Hz), and their co-occurrence (FRandR). In a previous study, we had validated the rate of FRandRs during deep sleep to predict seizure outcome. Here, we ask whether epileptic FRandRs might be confounded by physiological FRandRs that are unrelated to epilepsy. Methods: We recorded iEEG in the medial temporal lobe MTL (hippocampus, entorhinal cortex, and amygdala) in 17 patients while they performed cognitive tasks. The three cognitive tasks addressed verbal working memory, visual working memory, and emotional processing. In our previous studies, these tasks activated the MTL. We re-analyzed the data of these studies with the automated detector that focuses on the co-occurrence of ripples and FRs (FRandR). Results: For each task, we identified those channels in which the HFO rate was modulated during the task condition compared to the control condition. However, the number of these channels did not exceed the chance level. Interestingly, even during wakefulness, the HFO rate was higher for channels within the seizure onset zone (SOZ) than for channels outside the SOZ. Conclusion: Our prospective definition of an epileptic HFO, the FRandR, is not confounded by physiological HFOs that might be elicited by our cognitive tasks. This is reassuring for the clinical use of FRandR as a biomarker of the EZ.