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Pathological high-frequency oscillations (HFOs) in intracranial EEG are promising biomarkers of epileptogenic tissue, and their physiological counterparts play a role in sensorimotor and cognitive function. HFOs have also been found in scalp EEG, but an overview of all studies is lacking. In this systematic review, we assessed the methodology to detect scalp HFOs and their clinical potential.We searched PubMed, Embase and the Cochrane Library for studies on HFOs in scalp EEG, and extracted methodological and clinical data.We included 60 studies with data from 1149 unique individuals. Two-thirds of studies analyzed HFOs visually in the time or time-frequency domain, and one-third automatically with visual validation. Most studies evaluated interictal ripples during sleep in children. Pathological HFOs were overall better than spikes in localizing the epileptogenic zone and predicting outcome, correlated negatively with cognition and positively with disease activity and severity, and decreased after medical and surgical treatment.The methodologies of the 60 studies were heterogeneous, but pathological scalp HFOs were clinically valuable as biomarkers in various situations, particularly in children with epilepsy.This systematic review gives an extensive overview of methodological and clinical data on scalp HFOs, establishing their clinical potential and discussing their limitations.

Het elektrocorticogram wordt toegepast als hulpmiddel bij de afgrenzing tussen epileptogeen weefsel en gezondweefsel tijdens epilepsiechirurgie. Mensen met een focale corticale dysplasie die intra-operatieve elektrocorticografieondergaan hebben vaak een kenmerkend elektrocorticogram. In deze studie worden de lokaliserende en tijd-gerela-teerde eigenschappen van met het elektrocorticogram gemeten pieken en hoogfrequente oscillaties geanalyseerd.

Het vinden van de juiste medicatie voor mensen met epilepsie is niet eenvoudig. Variatie tussen patiënten in de mate van remming van hersenactiviteit verklaart mogelijk waarom medicijnen bij de één wel en bij de ander niet werken. Analyse van het EEG tussen aanvallen in biedt inzicht in de mate van verstoorde remming. Hier beschrijven we recent gestart translationeel preklinisch en klinisch onderzoek naar een EEG-biomarker als mogelijke indicator voor effectiviteit van medicatie.

Objective Neonatal seizures are often the first symptom of perinatal brain injury. High-frequency oscillations (HFOs) are promising new biomarkers for epileptogenic tissue and can be found in intracranial and surface EEG. To date, we cannot reliably predict which neonates with seizures will develop childhood epilepsy. We questioned whether epileptic HFOs can be generated by the neonatal brain and potentially predict epilepsy. Methods We selected 24 surface EEGs sampled at 2048 Hz with 175 seizures from 16 neonates and visually reviewed them for HFOs. Interictal epochs were also reviewed. Results We found HFOs in thirteen seizures (7%) from four neonates (25%). 5025 ictal ripples (rate 10 to 1311/min; mean frequency 135 Hz; mean duration 66 ms) and 1427 fast ripples (rate 8 to 356/min; mean frequency 298 Hz; mean duration 25 ms) were marked. Two neonates (13%) showed interictal HFOs (285 ripples and 25 fast ripples). Almost all HFOs co-occurred with sharp transients. We could not find a relationship between neonatal HFOs and outcome yet. Conclusions Neonatal HFOs co-occur with ictal and interictal sharp transients. Significance The neonatal brain can generate epileptic ripples and fast ripples, particularly during seizures, though their occurrence is not common and potential clinical value not evident yet.

The study of high frequency oscillations (HFO) in the electroencephalogram (EEG) as biomarkers of epileptic activity has merely focused on their spatial location and relationship to the epileptogenic zone. It has been suggested in several ways that the amount of HFO at a certain point in time may reflect the disease activity or severity. This could be clinically useful in several ways, especially as noninvasive recording of HFO appears feasible. We grouped the potential hypotheses into 4 categories: (1) HFO as biomarkers to predict the development of epilepsy; (2) HFO as biomarkers to predict the occurrence of seizures; (3) HFO as biomarkers linked to the severity of epilepsy, and (4) HFO as biomarkers to evaluate outcome of treatment. We will review the literature that addresses these 4 hypotheses and see to what extent HFO can be used to measure seizure propensity and help determine prognosis of this unpredictable disease.

Signal analysis biomarkers, in an intra-operative setting, may be complementary tools to guide and tailor the resection in drug-resistant focal epilepsy patients. Effective assessment of biomarker performances are needed to evaluate their clinical usefulness and translation. We defined a realistic ground-truth scenario and compared the effectiveness of different biomarkers alone and combined to localize epileptogenic tissue during surgery. We investigated the performances of univariate, bivariate and multivariate signal biomarkers applied to 1 min inter-ictal intra-operative electrocorticography to discriminate between epileptogenic and non-epileptogenic locations in 47 drug-resistant people with epilepsy (temporal and extra-temporal) who had been seizure-free one year after the operation. The best result using a single biomarker was obtained using the phase-amplitude coupling measure for which the epileptogenic tissue was localized in 17 out of 47 patients. Combining the whole set of biomarkers provided an improvement of the performances: 27 out of 47 patients. Repeating the analysis only on the temporal-lobe resections we detected the epileptogenic tissue in 29 out of 30 combining all the biomarkers. We suggest that the assessment of biomarker performances on a ground-truth scenario is required to have a proper estimate on how biomarkers translate into clinical use. Phase-amplitude coupling seems the best performing single biomarker and combining biomarkers improves localization of epileptogenic tissue. Performance achieved is not adequate as a tool in the operation theater yet, but it can improve the understanding of pathophysiological process.

MRI and intraoperative electrocorticography are often used in tandem to delineate epileptogenic tissue in resective surgery for focal epilepsy. Both the resection of the MRI lesion and tissue with high rates of electrographic discharges on electrocorticography, e.g. spikes and high-frequency oscillations (80–500 Hz), lead to a better surgical outcome. How MRI and electrographic markers are related, however, is currently unknown. The aim of this study was to find the spatial relationship between MRI lesions and spikes/high-frequency oscillations. We retrospectively included 33 paediatric and adult patients with lesional neocortical epilepsy who underwent electrocorticography-tailored surgery (14 females, median age = 13.4 years, range = 0.6–47.0 years). Mesiotemporal lesions were excluded. We used univariable linear regression to find correlations between pre-resection spike/high-frequency oscillation rates on an electrode and its distance to the MRI lesion. We tested straight lines to the centre and the edge of the MRI lesion, and the distance along the cortical surface to determine which of these distances best reflects the occurrence of spikes/high-frequency oscillations. We conducted a moderator analysis to investigate the influence of the underlying pathology type and lesion volume on our results. We found spike and high-frequency oscillation rates to be spatially linked to the edge of the MRI lesion. The underlying pathology type influenced the spatial relationship between spike/high-frequency oscillation rates and the MRI lesion (Pspikes < 0.0001, Pripples < 0.0001), while the lesion volume did not (Pspikes = 0.64, Pripples = 0.89). A higher spike rate was associated with a shorter distance to the edge of the lesion for cavernomas [F(1,64) = −1.37, P < 0.0001, η2 = 0.22], focal cortical dysplasias [F(1,570) = −0.25, P < 0.0001, η2 = 0.05] and pleomorphic xanthoastrocytomas [F(1,66) = −0.18, P = 0.01, η2 = 0.09]. In focal cortical dysplasias, a higher ripple rate was associated with a shorter distance [F(1,570) = −0.35, P < 0.0001, η2 = 0.05]. Conversely, low-grade gliomas showed a positive correlation; the further an electrode was away from the lesion, the higher the rate of spikes [F(1,75) = 0.65, P < 0.0001, η2 = 0.37] and ripples [F(1,75) = 2.67, P < 0.0001, η2 = 0.22]. Pathophysiological processes specific to certain pathology types determine the spatial relationship between the MRI lesion and electrocorticography results. In our analyses, non-tumourous lesions (focal cortical dysplasias and cavernomas) seemed to intrinsically generate spikes and high-frequency oscillations, particularly at the border of the lesion. This advocates for a resection of this tissue. Low-grade gliomas caused epileptogenicity in the peritumoural tissue. Whether a resection of this tissue leads to a better outcome is unclear. Our results suggest that the underlying pathology type should be considered when intraoperative electrocorticography is interpreted.

The neuroscience community increasingly uses the Brain Imaging Data Structure (BIDS) to organize data, extending from MRI to electrophysiology data. While automated tools and workflows are developed that help organize MRI data from the scanner to BIDS, these workflows are lacking for clinical intracranial EEG (iEEG data). We present a practical guideline on how to organize full clinical iEEG epilepsy data into BIDS. We present electrophysiological datasets recorded from twelve subjects who underwent intracranial monitoring followed by resective epilepsy surgery at the University Medical Center Utrecht, the Netherlands, and became seizure-free after surgery. These data include intraoperative electrocorticography recordings from six patients, long-term electrocorticography recordings from three patients and stereo-encephalography recordings from three patients. We describe the 6 steps in the pipeline that are essential to structure the data from these clinical iEEG recordings into BIDS and the challenges during this process. These guidelines enable centers performing clinical iEEG recordings to structure their data to improve accessibility, reusability and interoperability of clinical data.Background & SummaryToday’s era of big data and open science has highlighted the importance of organizing and storing data in keeping with the FAIR Data Principles of Findable, Accessible, Interoperable and Reusable Data to the neuroscientific community1,2. Over the past five years, a community-driven effort to develop a simple standardized method of organizing, annotating and describing neuroimaging data has resulted in the Brain Imaging Data Structure (BIDS). BIDS was originally developed for magnetic resonance imaging data (MRI3), but now also has extensions for magnetoencephalography (MEG4), electroencephalography (EEG5), and intracranial encephalography (iEEG6). BIDS prescribes rules about the organization of the data itself, with a formalized file/folder structure and naming conventions, and provides standardized templates to store associated metadata in human and machine readable, text-based, JSON and TSV file formats. Software packages analyzing neuroimaging data increasingly support data organized using the BIDS format (https://bids-apps.neuroimaging.io/apps/). However, a major challenge in the use of BIDS is to curate the data from their source format into a BIDS validated set. Several tools exist to convert MRI source data into BIDS datasets7–11, but to our knowledge, there is currently no tool or protocol for iEEG.The University Medical Center in Utrecht, the Netherlands, is a tertiary referral center performing around 150 epilepsy surgeries per year. The success of surgery for treating focal epilepsy depends on accurate prediction of brain tissue that needs to be removed or disconnected to yield full seizure control. People referred for epilepsy surgery undergo an extensive presurgical work-up, starting with MRI and video-EEG and, if needed, PET or ictal SPECT. This noninvasive phase is followed directly by a resection, possibly guided by intraoperative ECoG, or by long-term electrocorticography (ECoG) or stereo-encephalography (SEEG) with electrodes placed on or implanted in the brain12. From January 2008 until December 2019, 560 of the epilepsy surgeries in our center were guided by intraoperative ECoG; 163 surgeries followed after long-term ECoG or SEEG investigation. These iEEG data offer a unique combination of high spatial and temporal resolution measurements of the living human brain and it is important to curate these data in a way such that they can be used by many people in the future to study epilepsy and typical brain dynamics.As part of RESPect (Registry for Epilepsy Surgery Patients, ethical committee approval (18-109)), we started to retrospectively convert raw, unprocessed, clinical iEEG data of patients that underwent epilepsy surgery from January 2008 onwards, to the iEEG-BIDS format and identified 6 critical steps in this process. With this paper, we give a practical workflow of how we collected iEEG data in the UMC Utrecht and converted these data to BIDS. We share our entire pipeline and provide practical examples of six patients with intraoperative ECoG, three patients with long-term ECoG and three patients with SEEG data, demonstrating how BIDS can be used for intraoperative as well as long-term recordings.

This scientific commentary refers to ‘Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach’ by Zhang et al. (https://doi.org/10.1093/braincomms/fcab267).

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

New-onset epilepsy is a symptomatic expression of diffuse glioma and lowers the quality of life. Surgical resection could extend beyond the traditional lesionectomy and include the epileptic focus when permitted by neurological function. Intraoperative electrocorticography (ioECoG) is used during epilepsy surgery to estimate the epileptogenic tissue by mapping the irritative zone. We aimed to determine the added value of ioECoG for postoperative seizure control in patients with non-refractory, glioma-related seizures. We retrospectively included patients who underwent ioECoG-assisted (awake) tumor resection. The ioECoG tailoring strategy was based on electrographic seizures and interictal discharges (sporadic spikes, continuous spiking patterns, and sharp waves). The extent of resection (EOR) and the residual tumor volume (cm3) were quantified from pre- and postoperative magnetic resonance imaging. We used the Weibull regression model for multivariate time to event (seizure recurrence) analyses. Fifty patients underwent ioECoG-assisted surgery. IoECoG recorded eight (16%) spontaneous electrographic seizures. Seventeen patients had residual interictal discharges after tumor resection. Residual interictal discharges were significantly associated with shorter seizure-free period after surgery (HR = 3.1 [1.3 – 7.2], p-value = 0,01). This effect was especially prominent in primary high-grade glioma resections. Residual tumor volume and EOR did not associate with seizure outcome. We sampled 14 interictal spike-positive brain tissues (three inside and 11 outside the MRI abnormalities) for additional histopathological analyses. Tumor cells were found in all specimens. The overall sensitivity of interictal spikes for tumor infiltration was 78% (95%CI [56 – 93]) and the specificity was 100%. IoECoG could serve multiple purposes in brain tumor surgery. It can assist in intraoperative seizure detection. Complete resection of epileptic discharges may improve seizure outcome independently of rest tumor volume or EOR. IoECoG could help guide supramaximal resection to improve seizure control and assist patient counseling. Furthermore, interictal spikes might serve as markers for intraoperative tumor identification.

Objective High frequency oscillations (HFOs) in intraoperative electrocorticography (ioECoG) are thought to be generated by hyperexcitable neurons. Inflammation may promote neuronal hyperexcitability. We investigated the relation between HFOs and inflammation in tumor-related epilepsy. Methods We identified HFOs (ripples 80–250 Hz, fast ripples 250–500 Hz) in the preresection ioECoG of 32 patients with low-grade tumors. Localization of recorded HFOs was classified based on magnetic resonance imaging reconstructions: in tumor, in resected non-tumorous area and outside the resected area. We tested if the following inflammatory markers in the tumor or peritumoral tissue were related to HFOs: activated microglia, cluster of differentiation 3 (CD3)-positive T-cells, interleukin 1-beta (IL1β), toll-like receptor 4 (TLR4) and high mobility group box 1 protein (HMGB1). Results Tumors that generated ripples were infiltrated by more CD3-positive cells than tumors without ripples. Ripple rate outside the resected area was positively correlated with IL1β/TLR4/HMGB1 pathway activity in peritumoral area. These two areas did not directly overlap. Conclusions Ripple rates may be associated with inflammatory processes. Significance Our findings support that ripple generation and spread might be associated with synchronized fast firing of hyperexcitable neurons due to certain inflammatory processes. This pilot study provides arguments for further investigations in HFOs and inflammation.

Objective In people with low‐grade intrinsic brain tumors, an epileptic focus is often located close to the lesion. High‐frequency oscillations (HFOs) in electrocorticography (ECoG) might help to delineate this focus. We investigated the relationship between HFOs and low‐grade brain tumors and their potential value for tumor‐related epilepsy surgery. Methods We analyzed pre‐ and postresection intraoperative ECoG in 41 patients with refractory epilepsy and a low‐grade lesion. Electrodes were designated as overlying the tumor, adjacent resected tissue (peritumoral), or outside the resection bed using magnetic resonance imaging (MRI) and intraoperative photographs. We then used a semiautomated approach to detect HFOs as either ripples (80–250 Hz) or fast ripples (250–500 Hz). Results The rate of fast ripples was higher in electrodes covering tumor and peritumoral tissue than outside the resection (p = .04). Mesiotemporal tumors showed more ripples (p = .002), but not more fast ripples (p = .07), than superficial tumors. Rates of fast ripples were higher in glioma and extraventricular neurocytoma than in ganglioglioma or dysembryoplastic neuroepithelial tumor (DNET). The rate of ripples and fast ripples in postresection ECoG was not higher in patients with residual tumor tissue on MRI than those without. The rate of ripples in postresection ECoG was higher in patients with good than bad seizure outcome (p = .03). Fast ripples outside the resection and in post‐ECoG seem related to seizure recurrence. Significance Fast ripples in intraoperative ECoG can be used to help guide resection in tumor‐related epilepsy surgery. Preresection fast ripples occur predominantly in epileptogenic tumor and peritumoral tissue. Fast ripple rates are higher in glioma and extraventricular neurocytoma than in ganglioglioma and DNET.

Objective Intraoperative electrocorticography (ECoG) can be used to delineate the resection area in epilepsy surgery. High-frequency oscillations (HFOs; 80–500 Hz) seem better biomarkers for epileptogenic tissue than spikes. We studied how HFOs and spikes in combined pre- and postresection ECoG predict surgical outcome in different tailoring approaches. Methods We, retrospectively, marked HFOs, divided into fast ripples (FRs; 250–500 Hz) and ripples (80–250 Hz), and spikes in pre- and postresection ECoG sampled at 2,048 Hz in people with refractory focal epilepsy. We defined four groups of electroencephalography (EEG) event occurrence: pre+post− (+/−), pre+post+ (+/+), pre−post+ (−/+) and pre−post− (−/−). We subcategorized three tailoring approaches: hippocampectomy with tailoring for neocortical involvement; lesionectomy of temporal lesions with tailoring for mesiotemporal involvement; and lesionectomy with tailoring for surrounding neocortical involvement. We compared the percentage of resected pre-EEG events, time to recurrence, and the different tailoring approaches to outcome (seizure-free vs recurrence). Results We included 54 patients (median age, 15.5 years; 25 months of follow-up; 30 seizure free). The percentage of resected FRs, ripples, or spikes in pre-ECoG did not predict outcome. The occurrence of FRs in post-ECoG, given FRs in pre-ECoG (+/−, +/+), predicted outcome (hazard ratio, 3.13; confidence interval = 1.22–6.25; p = 0.01). Seven of 8 patients without spikes in pre-ECoG were seizure free. The highest predictive value for seizure recurrence was presence of FRs in post-ECoG for all tailoring approaches. Interpretation FRs that persist before and after resection predict poor postsurgical outcome. These findings hold for different tailoring approaches. FRs can thus be used for tailoring epilepsy surgery with repeated intraoperative ECoG measurements. Ann Neurol 2017;81:664–676

High frequency oscillations (HFOs, 80–500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting., Highlights • Cross-validation signal space separation and beamformer increase the SNR in MEG. • Automatic detection of MEG ripples in the time domain is feasible. • Our method identifies ripples with minimal user effort and is clinically applicable. • Automatically detected ripples are concordant with MEG spikes in 14/16 patients. • Automatically detected ripples are concordant with resection area in 6/8 patients.

ObjectiveSignal analysis biomarkers, in an intra-operative setting, may be complementary tools to guide and tailor the resection in drug-resistant epilepsy patients. Unbiased assessment of biomarker performances are needed to evaluate their clinical usefulness and translation. We defined a realistic ground-truth scenario and compared the effectiveness of different biomarkers alone and combined to localize epileptogenic tissue.MethodsWe investigated the performances of univariate, bivariate and multivariate signal biomarkers applied to 1 minute inter-ictal intra-operative electrocorticography to discriminate between electrodes covering normal or pathologic activity in 47 drug-resistant people with epilepsy (temporal and extra-temporal) who had been seizure-free one year after the operation.ResultsThe best result using a single biomarker was obtained using the phase-amplitude coupling measure for which the epileptogenic tissue was localized in 16 out of 47 patients. Combining the whole set of biomarkers provided an improvement of the performances: 20 out of 47 patients. Repeating the analysis only on the temporal-lobe resections we reached a sensitivity of 93% (28 out of 30) combining all the biomarkers.ConclusionWe suggest that the assessment of biomarker performances on a ground-truth scenario is required to have a proper estimate on how biomarkers translate into clinical use. Phase-amplitude coupling seems the best performing single biomarker and combining biomarkers improves localization of epileptogenic tissue. However, sensitivity achieved is not adequate for the usage as a tool in the operation theater, but it can improve the understanding of pathophysiological process.

This scientific commentary refers to ‘Magnetoencephalography imaging of high frequency oscillations strengthens presurgical localization and outcome prediction’, by Velmurugan et al. (doi:10.1093/brain/awz284).

Objective New insights into high‐frequency electroencephalographic activity and network analysis provide potential tools to improve delineation of epileptic tissue and increase the chance of postoperative seizure freedom. Based on our observation of high‐frequency oscillations “spreading outward” from the epileptic source, we hypothesize that measures of directed connectivity in the high‐frequency range distinguish epileptic from healthy brain tissue. Methods We retrospectively selected refractory epilepsy patients with a malformation of cortical development or tumor World Health Organization grade I/II who underwent epilepsy surgery with intraoperative electrocorticography for tailoring the resection based on spikes. We assessed directed functional connectivity in the theta (4‐8 Hz), gamma (30‐80 Hz), ripple (80‐250 Hz), and fast ripple (FR; 250‐500 Hz) bands using the short‐time direct directed transfer function, and calculated the total, incoming, and outgoing propagation strength for each electrode. We compared network measures of electrodes covering the resected and nonresected areas separately for patients with good and poor outcome, and of electrodes with and without spikes, ripples, and FRs (group level: paired t test; patient level: Mann‐Whitney U test). We selected the measure that could best identify the resected area and channels with epileptic events using the area under the receiver operating characteristic curve, and calculated the positive and negative predictive value, sensitivity, and specificity. Results We found higher total and outstrength in the ripple and gamma bands in resected tissue in patients with good outcome (rippletotal: P = .01; rippleout: P = .04; gammatotal: P = .01; gammaout: P = .01). Channels with events showed lower total and instrength, and higher outstrength in the FR band, and higher total and outstrength in the ripple, gamma, and theta bands (FRtotal: P = .05; FRin: P

Candidates for epilepsy surgery must undergo presurgical evaluation to establish whether and how surgical treatment can stop seizures without causing neurological deficits. Various techniques, including MRI, PET, single-photon emission CT, video-EEG, magnetoencephalography and invasive EEG, aim to identify the diseased brain tissue and the involved network. Recent technical and methodological developments, encompassing both advances in existing techniques and new combinations of technologies, are enhancing the ability to define the optimal resection strategy. Multimodal interpretation and predictive computer models are expected to aid surgical planning and patient counselling, and multimodal intraoperative guidance is likely to increase surgical precision. In this Review, we discuss how the knowledge derived from these new approaches is challenging our way of thinking about surgery to stop focal seizures. In particular, we highlight the importance of looking beyond the EEG seizure onset zone and considering focal epilepsy as a brain network disease in which long-range connections need to be taken into account. We also explore how new diagnostic techniques are revealing essential information in the brain that was previously hidden from view.

Introduction High-frequency oscillations (HFOs, 80–500 Hz) are newly-described EEG markers of epileptogenicity. The proportion of physiological and pathological HFOs is unclear, as frequency analysis is insufficient for separating the two types of events. For instance, ripples (80–250 Hz) also occur physiologically during memory consolidation processes in medial temporal lobe structures. We investigated the correlation between HFO rates and memory performance. Methods Patients investigated with bilateral medial temporal electrodes and an intellectual capacity allowing for memory testing were included. High-frequency oscillations were visually marked, and rates of HFOs were calculated for each channel during slow-wave sleep. Patients underwent three verbal and three nonverbal memory tests. They were grouped into severe impairment, some impairment, mostly intact, or intact for verbal and nonverbal memory. We calculated a Pearson correlation between HFO rates in the hippocampi and the memory category and compared HFO rates in each hippocampus with the corresponding (verbal — left, nonverbal — right) memory result using Wilcoxon rank-sum test. Results Twenty patients were included; ten had bilateral, five had unilateral, and five had no memory impairment. Unilateral memory impairment was verbal in one patient and nonverbal in four. There was no correlation between HFO rates and memory performance in seizure onset areas. There was, however, a significant negative correlation between the overall memory performance and ripple rates (r = − 0.50, p = 0.03) outside the seizure onset zone. Conclusion Our results suggest that the majority of spontaneous hippocampal ripples, as defined in the present study, may reflect pathological activity, taking into account the association with memory impairment. The absence of negative correlation between memory performance and HFO rates in seizure onset areas could be explained by HFO rates in the SOZ being generally so high that differences between areas with remaining and impaired memory function cannot be seen.

People with epilepsy often report seizures precipitated by stress. This is believed to be due to effects of stress hormones, such as cortisol, on neuronal excitability. Cortisol, regardless of stress, is released in hourly pulses, whose effect on epileptic activity is unknown. We tested the relation between cortisol levels and the incidence of epileptiform abnormalities in the electroencephalogram of people with focal epilepsy. Morning cortisol levels were measured in saliva samples obtained every 15 min. Interictal epileptiform discharges were determined in the same time periods. We investigated the relationship between cortisol levels and the epileptiform discharges distinguishing persons with from those without stress-precipitated seizures (linear mixed model), and analysed the contribution of individual, epilepsy and recording characteristics with multivariable analysis. Twenty-nine recordings were performed in 21 individuals. Cortisol was positively related to incidence of epileptiform discharges (β = 0.26, P = 0.002) in people reporting stress-sensitive seizures, but not those who did not report stress sensitivity (β = -0.07, P = 0.64). The relationship between cortisol and epileptiform discharges was positively associated only with stress sensitivity of seizures (β = 0.31, P = 0.005). The relationship between cortisol levels and incidence of interictal epileptiform discharges in people with stress-sensitive seizures suggests that stress hormones influence disease activity in epilepsy, also under basal conditions.

OBJECTIVE: High frequency oscillations (HFOs, >80Hz) are biomarkers for epileptogenic cortex in invasive and non-invasive electroencephalography (EEG). Identification of HFOs in magnetoencephalography (MEG) is hindered by noise. Computing spatial filters using beamforming to reconstruct time series for selected brain regions, so-called virtual sensors (VS), can increase the signal-to-noise ratio. We identified HFOs in MEG in time domain using VS.METHODS: Fifteen minutes of MEG data were selected from 12 patients. VS were placed around the epileptic spikes (affected region) and in the contralateral hemisphere. VS and physical sensors were reviewed for HFOs and spikes. HFO locations were compared to spikes and other clinical parameters.RESULTS: Eight patients showed 78 time points with 575 HFOs in VS, 513 were in the affected region. HFOs could not be identified in physical sensors for 61 of the 78 VS time points. HFOs overlapped with presumed epileptogenic areas and were also visible in unfiltered VS signals.CONCLUSION: Beamformer-based VS analysis can help to identify epileptic HFOs that are not discernable in physical MEG sensors.SIGNIFICANCE: This approach can be extended to enable localization of non-invasively recorded HFOs. This would help surgical planning and reduce the need for invasive diagnostics.

Study Objectives A dialogue between hippocampal ripples (80-250 Hz) and neocortical sleep-specific transients is important for memory consolidation. Physiological neocortical ripples can be recognized in scalp EEGs of children. We investigated how often scalp-EEG recorded ripples co-occur with different types of sleep-specific transients, the distribution and spatial extent of ripples with and without co-occurring sleep-specific transients, and the occurrence of ripples across sleep stages. Methods We marked ripples in daytime sleep-EEGs of 19 children and determined for each ripple if it co-occurred with a sleep-specific transient. We compared the distribution of ripples without co-occurring sleep-specific transients to the distribution of all ripples. We estimated the spatial extent of simultaneously occurring ripples by counting how many EEG regions they comprised. We compared ripple rate per sleep stage using Friedman's analysis of variance and Wilcoxon signed-rank test. Results 74.4 % of ripples co-occurred with sleep-specific transients: 27.8 % with vertex waves, 14.7 % with hypnagogic hypersynchrony, 13.7 % with slow waves, 12.2 % with spindles, and 6.0 % with K-complexes. Ripples without co-occurring sleep-specific transients showed the same central dominance but a significantly less pronounced midline dominance than the overall distribution pattern. Spatial extent was larger when ripples co-occurred with sleep-specific transients. Ripple rates during nonrapid eye movement (N) sleep stages N1 and N2 were higher than during N3 (T = 22.00, p = 0.02 and T = 23.00, p = 0.01). Conclusions Scalp-EEG recorded physiological ripples co-occur with various sleep-specific EEG-transients, especially with vertex waves. These ripples occur most frequently during light sleep.

Objective Surface EEG can show epileptiform ripples in people with focal epilepsy, but identification is impeded by the low signal-to-noise ratio of the electrode recordings. We used beamformer-based virtual electrodes to improve ripple identification. Methods We analyzed ten minutes of interictal EEG of nine patients with refractory focal epilepsy. EEGs with more than 60 channels and 20 spikes were included. We computed ∼79 virtual electrodes using a scalar beamformer and marked ripples (80–250 Hz) co-occurring with spikes in physical and virtual electrodes. Ripple numbers in physical and virtual electrodes were compared, and sensitivity and specificity of ripples for the region of interest (ROI; based on clinical information) were determined. Results Five patients had ripples in the physical electrodes and eight in the virtual electrodes, with more ripples in virtual than in physical electrodes (101 vs. 57, p =.007). Ripples in virtual electrodes predicted the ROI better than physical electrodes (AUC 0.65 vs. 0.56, p =.03). Conclusions Beamforming increased ripple visibility in surface EEG. Virtual ripples predicted the ROI better than physical ripples, although sensitivity was still poor. Significance Beamforming can facilitate ripple identification in EEG. Ripple localization needs to be improved to enable its use for presurgical evaluation in people with epilepsy.

International audience; 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.

Objective Removal of brain tissue showing high frequency oscillations (HFOs; ripples: 80–250 Hz and fast ripples: 250–500 Hz) in preresection electrocorticography (preECoG) in epilepsy patients seems a predictor of good surgical outcome. We analyzed occurrence and localization of HFOs in intra-operative preECoG and postresection electrocorticography (postECoG). Methods HFOs were automatically detected in one-minute epochs of intra-operative ECoG sampled at 2048 Hz of fourteen patients. Ripple, fast ripple, spike, ripples on a spike (RoS) and not on a spike (RnoS) rates were analyzed in pre- and postECoG for resected and nonresected electrodes. Results Ripple, spike and fast ripple rates decreased after resection. RnoS decreased less than RoS (74% vs. 83%; p = 0.01). Most fast ripples in preECoG were located in resected tissue. PostECoG fast ripples occurred in one patient with poor outcome. Patients with good outcome had relatively high postECoG RnoS rates, specifically in the sensorimotor cortex. Conclusions Our observations show that fast ripples in intra-operative ECoG, compared to ripples, may be a better biomarker for epileptogenicity. Further studies have to determine the relation between resection of epileptogenic tissue and physiological ripples generated by the sensorimotor cortex. Significance Fast ripples in intra-operative ECoG can help identify the epileptogenic zone, while ripples might also be physiological.

Op 22 februari 2018 promoveerde Nicole van Klink aan de Universiteit van Utrecht op haar proefschrift High frequency oscillations: towards clinical application. Nicole onderzocht het nut van hoog-frequente oscillaties in zowel oppervlakte-EEG, magneto-encephalogram en intra-operatief elektrocorticogram voor diagnostiek en behandeling van epilepsie en kwam hierbij tot een aantal relevante conclusies.

Neurophysiological studies have reported functional network alterations in epilepsy, most consistently in the theta frequency band. Highly interconnected brain regions (so-called 'hubs') seem to be important in these epileptic networks. High frequency oscillations (HFOs) in intracranial EEG recordings are recently discovered biomarkers that can identify the epileptogenic area and are thought to result from altered neuronal interactions. We studied whether the epileptogenic zone (identified by HFOs and seizure onset zone) is associated with pathological hubs. Bilateral depth electrode recordings from the hippocampus and amygdala were available from twelve patients suspected of temporal lobe epilepsy. HFOs, classified as ripples (80-250 Hz) and fast ripples (250-500 Hz), and epileptiform spikes were marked for all patients in a five-minute epoch of slow-wave sleep. For each channel, we computed hub-measures from a period without epileptiform spikes and found that the epileptogenic zone was associated with a decreased hub-value in the theta frequency band. The amount of HFOs, especially fast ripples, was negatively correlated with the hub-value per channel. Results from post-hoc analyses of other frequency bands, particularly the broad- and gamma frequency band, pointed in the same direction as the results for the theta frequency band. These findings suggest a pathological functional 'isolation' of the epileptogenic zone in the interictal state.

Excerpted proceedings of The Eighth International Workshop on Advances in Electrocorticography (ECoG), which convened October 15–16, 2015 in Chicago, IL, are presented. The workshop series has become the foremost gathering to present current basic and clinical research in subdural brain signal recording and analysis.

Objective Up to 30% of people with juvenile myoclonic epilepsy (JME) have photoparoxysmal responses (PPR). Recent studies report on structural and pathophysiological differences between people with JME with (JME+PPR) and without PPR (JME−PPR). We investigated whether electrophysiological features outside photic stimulation differ between these subtypes. Methods We analysed EEG recordings of people with JME at a tertiary epilepsy centre and an academic hospital. Photosensitivity was assessed in a drug-naïve condition. We compared the occurrence and involvement of posterior electrodes for focal abnormalities and generalised spike-wave activity in the EEG outside photic stimulation between JME+PPR and JME−PPR. Results We included EEG recordings of 18 people with JME+PPR and 21 with JME−PPR. People with JME−PPR had less focal abnormalities in the posterior brain regions than people with JME+PPR (19% vs 55%, p

OBJECTIVE: High frequency oscillations (HFOs) and in particular fast ripples (FRs) in the post-resection electrocorticogram (ECoG) have recently been shown to be highly specific predictors of outcome of epilepsy surgery. FR visual marking is time consuming and prone to observer bias. We validate here a fully automatic HFO detector against seizure outcome. METHODS: Pre-resection ECoG dataset (N=14 patients) with visually marked HFOs were used to optimize the detector's parameters in the time-frequency domain. The optimized detector was then applied on a larger post-resection ECoG dataset (N=54) and the output was compared with visual markings and seizure outcome. The analysis was conducted separately for ripples (80-250Hz) and FRs (250-500Hz). RESULTS: Channel-wise comparison showed a high association between automatic detection and visual marking (p