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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.

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.

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.

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.

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.

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.

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.

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.

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

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.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).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.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.

Objective High frequency oscillations (HFOs; > 80 Hz), especially fast ripples (FRs, 250–500 Hz), are novel biomarkers for epileptogenic tissue. The pathophysiology suggests enhanced functional connectivity within FR generating tissue. Our aim was to determine the relation between brain areas showing FRs and ‘baseline’ functional connectivity within EEG networks, especially in the high frequency bands. Methods We marked FRs, ripples (80–250 Hz) and spikes in the electrocorticogram of 14 patients with refractory temporal lobe epilepsy. We assessed ‘baseline’ functional connectivity in epochs free of epileptiform events within these recordings, using the phase lag index. We computed the Eigenvector Centrality (EC) per channel in the FR and gamma band network. We compared EC between channels that did or did not show events at other moments in time. Results FR-band EC was higher in channels with than without spikes. Gamma-band EC was lower in channels with ripples and FRs. Conclusions We confirmed previous findings of functional isolation in the gamma-band and found a first proof of functional integration in the FR-band network of channels covering presumed epileptogenic tissue. Significance ‘Baseline’ high-frequency network parameters might help intra-operative recognition of epileptogenic tissue without the need for waiting for events. These findings can increase our understanding of the ‘architecture’ of epileptogenic networks and help unravel the pathophysiology of HFOs., Highlights • The epileptogenic area is functionally isolated in the gamma-band network. • The epileptogenic area seems functionally integrated in the high frequency network. • The ‘baseline’ signal contains clinically useful high frequency surrogate biomarker information.

The discovery that electroencephalography (EEG) contains useful information at frequencies above the traditional 80Hz limit has had a profound impact on our understanding of brain function. In epilepsy, high-frequency oscillations (HFOs, >80Hz) have proven particularly important and useful. This literature review describes the morphology, clinical meaning, and pathophysiology of epileptic HFOs. To record HFOs, the intracranial EEG needs to be sampled at least at 2,000Hz. The oscillatory events can be visualized by applying a high-pass filter and increasing the time and amplitude scales, or EEG time-frequency maps can show the amount of high-frequency activity. HFOs appear excellent markers for the epileptogenic zone. In patients with focal epilepsy who can benefit from surgery, invasive EEG is often required to identify the epileptic cortex, but current information is sometimes inadequate. Removal of brain tissue generating HFOs has been related to better postsurgical outcome than removing the seizure onset zone, indicating that HFOs may mark cortex that needs to be removed to achieve seizure control. The pathophysiology of epileptic HFOs is challenging, probably involving populations of neurons firing asynchronously. They differ from physiological HFOs in not being paced by rhythmic inhibitory activity and in their possible origin from population spikes. Their link to the epileptogenic zone argues that their study will teach us much about the pathophysiology of epileptogenesis and ictogenesis. HFOs show promise for improving surgical outcome and accelerating intracranial EEG investigations. Their potential needs to be assessed by future research.

We aim to analysis the relationship between HFOs-generating regions and the seizure onset zone (SOZ) in epileptic patients without a visible lesion on MRI.Intracerebral EEGs were recorded in 17 patients with intractable focal seizures and normal MRIs. The rates of interictal HFOs and spikes inside and outside the SOZ were analyzed as well as the specificity, sensitivity and accuracy of HFOs and spikes to determine the SOZ.The mean rate of spikes, ripples and fast ripples (FR) was higher in the SOZ than in the non-SOZ channels. In regard to the identification of the SOZ the sensitivity was 91% for spikes, 91% for ripples and 66% for FR, the specificity was 30% for spikes, 42% for ripples and 80% for FR, and the accuracy was 44% for spikes, 54% for ripples and 76% for FR.The rates of spikes and HFOs were higher inside than outside the SOZ. However, HFOs are also more specific and accurate than spikes to delineate the SOZ.Analysis of interictal HFOs during 5-10 min of sleep recording is a good tool to localize the SOZ in patients with epilepsy and normal MRI, and could potentially reduce the duration of chronic intracerebral EEG recordings.

The purpose of this study was to evaluate the contribution of posterior circulation to memory function by comparing memory scores between patients with and without a foetal-type posterior cerebral artery (FTP) during the intracarotid amobarbital procedure (IAP) in epilepsy patients. Patients undergoing bilateral IAP between January 2004 and January 2010 were retrospectively included. Pre-test angiograms were assessed for the presence of a FTP. Memory function scores (% correct) after right and left injections were obtained. Functional significance of FTP was affirmed by relative occipital versus parietal EEG slow-wave increase during IAP. Memory and EEG scores were compared between patients with and without FTP (Mann–Whitney U test). A total of 106 patients were included, 73 with posterior cerebral arteries (PCA) without FTP (‘non-FTP’), 28 patients with unilateral FTP and 5 with a bilateral FTP. Memory scores were lower when amytal was injected to the hemisphere contralateral to the presumed seizure focus (on the right decreasing from 98.3 to 59.1, and on the left decreasing from 89.1 to 72.4; p

Epilepsy surgery depends on reliable pre-surgical markers of epileptogenic tissue. The current gold standard is the seizure onset zone in ictal, i.e. chronic, electrocorticography recordings. Single pulse electrical stimulation can evoke epileptic, spike-like responses in areas of seizure onset also recorded by electrocorticography. Recently, spontaneous pathological high-frequency oscillations (80–520 Hz) have been observed in the electrocorticogram that are related to epileptic spikes, but seem more specific for epileptogenic cortex. We wanted to see whether a quantitative electroencephalography analysis using time–frequency information including the higher frequency range could be applied to evoked responses by single pulse electrical stimulation, to enhance its specificity and clinical use. Electrocorticography data were recorded at a 2048-Hz sampling rate from 13 patients. Single pulse electrical stimulation (10 stimuli, 1 ms, 8 mA, 0.2 Hz) was performed stimulating pairs of adjacent electrodes. A time–frequency analysis based on Morlet wavelet transformation was performed in a [−1 s : 1 s] time interval around the stimulus and a frequency range of 10–520 Hz. Significant ( P = 0.05) changes in power spectra averaged for 10 epochs were computed, resulting in event-related spectral perturbation images. In these images, time–frequency analysis of single pulse-evoked responses, in the range of 10–80 Hz for spikes, 80–250 Hz for ripples and 250–520 Hz for fast ripples, were scored by two observers independently. Sensitivity, specificity and predictive value of time–frequency single pulse-evoked responses in the three frequency ranges were compared with seizure onset zone and post-surgical outcome. In all patients, evoked responses included spikes, ripples and fast ripples. For the seizure onset zone, the median sensitivity of time–frequency single pulse-evoked responses decreased from 100% for spikes to 67% for fast ripples and the median specificity increased from 17% for spikes to 79% for fast ripples. A median positive predictive value for the evoked responses in the seizure onset zone of 17% was found for spikes, 26% for ripples and 37% for fast ripples. Five out of seven patients with

Epilepsy has a significant impact on health-related quality of life (HRQOL) of patients and personal coping style is an important determinant. Less is known about home caregivers. This study investigates HRQOL and coping style of both patients and caregivers and their interaction. Epilepsy patients attending the outpatient clinic of the University Medical Centre in Utrecht and their caregivers were sent EQ5D and RAND-36 questionnaires. The Utrecht Coping List was used to chart personal coping styles. HRQOL scores of patients and caregivers were compared to the general Dutch population. The association between patient and caregiver HRQOL scores was calculated. A stepwise backward multivariate linear regression analysis was used to explain variances in caregiver HRQOL. Eighty-six couples (49%) returned all questionnaires. Caregiver HRQOL scores were comparable to the general Dutch population (EQ5D: 0.88–0.88; p = 0.90, RAND-36 MCS: −2 points; p = 0.16), while patients HRQOL scores were lower (EQ5D: 0.79; p

The simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be used to localize interictal epileptiform discharges (IEDs). Previous studies have reported varying degrees of concordance of EEG-fMRI with electroclinical findings. The aim of the present study is to evaluate to what extent this variability is determined by the analytical strategy or by the properties of the EEG data. For that purpose, 42 IED sets obtained in 29 patients with epilepsy were reanalyzed using a finite impulse response approach, which estimates the hemodynamic response function (HRF) from the data and allows non-causal effects. Cardiac effects were treated as additional confounders in the model. This approach was compared to the classical approach assuming a fixed HRF for each voxel in the brain. The performance of each method was assessed by comparing the fMRI results to the EEG focus. The flexible model revealed more significantly activated voxels, which resulted in more activated brain regions concordant with the EEG focus (26 vs. 16). Correction for cardiac effects improved the results in 7 out of the 42 data sets. Furthermore, design theory for event-related experiments was applied in order to determine the influence of the number of IEDs and their temporal distribution on the success of an experiment. It appeared that this success is highly dependent upon the number of IEDs present during the recording and less on their temporal spacing. We conclude that the outcome of EEG-fMRI can be improved by using an optimized analytical strategy, but also depends on the number of IEDs occurring during the recording.

Thirty percent to 40% of patients with focal epilepsy are medically intractable,1 and for some, surgical removal of epileptogenic areas is the best option to gain seizure freedom. Intracranial electroencephalographic (iEEG) investigations are indicated for patients in whom noninvasive methods fail to identify a single focal seizure generator.2 iEEG is used to define the seizure onset zone (SOZ).3 Removal of the SOZ alone, however, does not always predict the surgical benefit.4,5 It is uncertain whether the outcome can be improved by removing areas of interictal spiking, often more widespread than the SOZ.6,7 Intracranial studies also have limitations, as their results depend on electrode location and type of implantation (intracortical vs subdural). For instance, iEEG electrodes only record neuronal activity in their direct vicinity and are blind for other areas,8 making it hard to judge whether the activity at seizure onset really represents the seizure generator or is the result of propagation from else-where. Thus the actual focus and its extent may be missed, leading to unsuccessful surgery. Microelectrode-recorded high-frequency oscillations (HFOs), ripples (80 –250Hz), and fast ripples (FRs, 250 –500Hz), were found predominantly in epileptogenic tissue.9 –11 They can also be recorded with macroelectrodes during clinical iEEG investigation.12,13 HFOs were more specific in indicating the SOZ than spikes.14 Additionally, they were linked to the SOZ independently of the underlying lesion and were infrequent in lesional areas outside the SOZ.15 Evidence therefore suggests that HFOs are good markers of epileptic tissue and may help to identify epileptogenic areas. We hypothesize that removing areas generating HFOs results in good surgical outcome. The correlation between removal of HFO-generating areas and seizure outcome was compared to that coming from spikes and to the current gold standard, removing the SOZ.

Purpose To study 3T compared to 1.5T phased array magnetic resonance imaging (MRI) in the presurgical work-up of patients with epilepsy with complex focus localization. Materials and Methods In all, 37 patients (>10 years) in preoperative work-up for epilepsy surgery were offered 3T in addition to 1.5T MRI if ambiguity existed about the epileptic focus. Scans were randomly reviewed by two observers, blinded for prior imaging, patient-identifying information, and each other's assessments, followed by a consensus meeting. The number of abnormal scans, detected lesions, and interobserver agreement were calculated and compared. The final consensus was compared to original scan reports. Results One observer identified 22 lesions in both 3 and 1.5T scans, while the second identified more lesions in 1.5T scans (28 vs. 20). 3T MRI had better interobserver agreement. 3T revealed more dysplasias, while 1.5T revealed more tissue loss and mesial temporal sclerosis (MTS). The final consensus yielded 29 lesions, whereas original reports identified only 17 lesions. Conclusion The 3T scans revealed different lesions compared to 1.5T. Patients can benefit most from 3T scans when a dysplasia is suspected. Reevaluation by another experienced neuroradiologist is advised in case of negative or equivocal MRIs. J. Magn. Reson. Imaging 2009;30:256–262. © 2009 Wiley-Liss, Inc.

High frequency oscillations (HFOs) have been associated with epileptogenicity. In rats, the extent of HFOs (>200 Hz) is correlated with seizure frequency. We studied whether the same applies to patients with focal epilepsy. Thirty-nine patients with intracerebral EEG sampled at 2000 Hz were studied for interictal ripples (80–250 Hz), fast ripples (FR, 250–500 Hz) and spikes. Seizure frequency before implantation was compared to numbers of channels with HFOs (>1/min). Analyses were repeated for HFO rates of >5, >10 and >20. Separate analyses were done for 25 patients with temporal lobe epilepsy only and for a selection of similar unilateral temporal channels in 12 patients. No linear correlation or trend was found relating the number of channels with HFOs and seizure frequency. There was a linear positive correlation between the number of channels with more than 20 FRs/min and seizure frequency. The hypothesis that the more tissue generating HFOs, the higher the seizure frequency, was not confirmed, though there might be a correlation for high FR rates.

Objective: High-frequency oscillations (HFOs) can be recorded in epileptic patients with clinical intracranial EEG. HFOs have been associated with seizure genesis because they occur in the seizure focus and during seizure onset. HFOs are also found interictally, partly co-occurring with epileptic spikes. We studied how HFOs are influenced by antiepileptic medication and seizure occurrence, to improve understanding of the pathophysiology and clinical meaning of HFOs. Methods: Intracerebral depth EEG was partly sampled at 2,000 Hz in 42 patients with intractable focal epilepsy. Patients with five or more usable nights of recording were selected. A sample of slow-wave sleep from each night was analyzed, and HFOs (ripples: 80–250 Hz, fast ripples: 250–500 Hz) and spikes were identified on all artifact-free channels. The HFOs and spikes were compared before and after seizures with stable medication dose and during medication reduction with no intervening seizures. Results: Twelve patients with five to eight nights were included. After seizures, there was an increase in spikes, whereas HFO rates remained the same. Medication reduction was followed by an increase in HFO rates and mean duration. Conclusions: Contrary to spikes, high-frequency oscillations (HFOs) do not increase after seizures, but do so after medication reduction, similarly to seizures. This implies that spikes and HFOs have different pathophysiologic mechanisms and that HFOs are more tightly linked to seizures than spikes. HFOs seem to play an important role in seizure genesis and can be a useful clinical marker for disease activity. AED = antiepileptic drug; CBZ = carbamazepine; CLOB = clobazam; FR = fast ripple; FR_isol = fast ripples without co-occurring spikes; FR_Sp = fast ripples with co-occurring spikes; GBP = gabapentin; HFO = high-frequency oscillation; Lai/s = left anterior inferior/superior electrode (porencephalic cyst); LEV = levetiracetam; LF/p/a = left frontal/posterior/anterior electrode; LOP = left frontal operculum electrode; Lpi/s = left posterior inferior/superior electrode; L/RA = left/right amygdale electrode; L/RC/a/s = left/right cingulate/anterior/superior electrode; L/RE = left/right epidural electrode; L/RH = left/right hippocampus electrode; L/ROF = left/right orbitofrontal electrode; L/RO/i/s = left/right occipital/infracalcine/supracalcine electrode; L/RP = left/right parahippocampus electrode; L/RS = left/right supramarginal gyrus electrode; LSMAa/p = left supplementary motor area anterior/posterior electrode; LT = left anteriotemporal electrode; LTG = lamotrigine; OXC = oxcarbamazepine; PRI = primidone; PTH = phenytoin; R = ripple; R_isol = ripples without co-occurring spikes; R_Sp = ripples with co-occurring spikes; SEEG = stereo-EEG; SEZ = one or more seizures; SOZ = seizure onset zone; Sp = spike; TPM = topiramate.

Objective To compare nasopharyngeal (NP), cheek and anterior temporal (AT) electrodes for the detection yield and localization of interictal spikes in temporal lobe epilepsy. Methods In patients evaluated for epilepsy surgery with subdural electrocorticography electrodes, we simultaneously recorded NP, cheek and AT electrodes. Two observers identified spikes in EEG traces and marked in which channels they occurred. Interobserver agreement was calculated using Cohen’s kappa. For localization, data-sets with high interobserver agreement (κ-value ⩾0.4) were evaluated. The subdural distribution of NP and AT spikes was mapped. Results Seven patients were included, six were analyzed for localization. Only 1.5% of spikes recorded by cheek electrodes were not seen on temporal leads, while 25% of NP spikes were not seen on either. Spikes only recorded by NP electrodes had mesiobasal, while AT spikes had lateral temporal distribution. Conclusions NP electrodes can increase EEG spike detection rate in temporal lobe epilepsy and are more useful than cheek electrodes. Spikes that are seen only on NP electrodes tend to be mesiobasal temporal lobe spikes. Significance Adding NP electrodes to scalp EEG can aid interictal spike detection and source localization, especially in short recordings like MEG–EEG.

Epilepsy surgery requires precise localization of the epileptic source. EEG-correlated functional MRI (EEG-fMRI) is a new technique showing the haemodynamic effects of interictal epileptiform activity. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult surgical candidates considered ineligible because of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Interictal epileptic discharges (IEDs) in the EEG during fMRI were identified by consensus between two observers. Topographically distinct IED sets were analysed separately. Only patients with significant, positive blood oxygen level-dependent (BOLD) responses that were topographically related to the EEG were re-evaluated for surgery. Forty-six IED sets from 29 patients were analysed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus (n = 3), presumed multifocality (n = 2) or a combination of both (n = 3). EEG-fMRI improved localization in four out of six unclear foci. In patients with presumed multifocality, EEG-fMRI advocated one of the foci in one patient and confirmed multifocality in four out of five patients. In four patients EEG-fMRI opened new prospects for surgery and in two of these patients intracranial EEG supported the EEG-fMRI results. In these complex cases, EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. It is thus a valuable tool in the presurgical evaluation of patients. Guidelines for the use of EEG-fMRI in clinical practice are proposed.

A novel automated algorithm is proposed to approximate the seizure onset zone (SOZ), while providing reproducible output. The SOZ, a surrogate marker for the epileptogenic zone (EZ), was approximated from intracranial electroencephalograms (iEEG) of nine people with temporal lobe epilepsy (TLE), using three methods: (1) Total ripple length (TRL): Manually segmented high-frequency oscillations, (2) Rippleness (R): Area under the curve (AUC) of the autocorrelation functions envelope, and (3) Autoregressive model residual variation (ARR, novel algorithm): Time-variation of residuals from autoregressive models of iEEG windows. TRL, R, and ARR results were compared in terms of separability, using Kolmogorov–Smirnov tests, and performance, using receiver operating characteristic (ROC) curves, to the gold standard for SOZ delineation: visual observation of ictal video-iEEGs. TRL, R, and ARR can distinguish signals from iEEG channels located within the SOZ from those outside it (p < 0.01). The ROC AUC was 0.82 for ARR, while it was 0.79 for TRL, and 0.64 for R. ARR outperforms TRL and R, and may be applied to identify channels in the SOZ automatically in interictal iEEGs of people with TLE. ARR, interpreted as evidence for nonharmonicity of high-frequency EEG components, could provide a new way to delineate the EZ, thus contributing to presurgical workup.

Objective: Single Pulse Electrical Stimulation (SPES) probes epileptogenic cortex during electrocorticography. Two SPES responses are described: pathological delayed responses (DR, >100 ms) associated with the seizure onset zone (SOZ) and physiological early responses (ER, 80 Hz, in the SOZ and seizure propagation areas. Methods: We used data from 12 refractory epilepsy patients. SPES consisted of 10 pulses of 1 ms, 4–8 mA and 5 s interval on adjacent electrodes pairs. Data were available at 2048 samples/s for six and 512 samples/s (22 bits) for eight patients and analyzed in the time–frequency (TF) and time-domain (TD). Results: Electrodes with ERs were stronger associated with SOZ than non-SOZ electrodes. ERs with frequency content >80 Hz exist and are specific for SOZ channels. ERs evoked by stimulation of seizure onset electrodes were associated with electrodes involved in seizure propagation. Conclusion: Analysis of ERs can reveal aspects of pathology, manifested by association with seizure propagation and areas with high ER numbers that coincide with the SOZ. Significance: Not only DRs, but also ERs could have clinical value for mapping epileptogenic cortex and help to unravel aspects of the epileptic network.

Objective Ripples (80–250 Hz) have been shown to be a more specific biomarker for the epileptogenic zone than epileptic spikes in intracranial EEG and even surface EEG. Ripples often co-occur with spikes. We investigated the spatiotemporal relation between spikes and ripples, and differences between spikes that do and do not co-occur with ripples. Methods We marked 50 time points with spikes in bipolar surface EEG during NREM sleep in patients with focal or multifocal epilepsy. We marked ripples that occurred with spikes and calculated parameters relating spikes and ripples: the duration, amplitude and slope of spikes, the timing of the start of ripples and spikes and the proportion of overlap. Results In total 219 ripples and 5995 individual spikes were marked in 31 patients. Spikes with ripples were on average shorter, had higher amplitude and higher slope than spikes without ripples. 64% of ripples started before spikes started. Spikes occurred on 13 (5–26) channels per patient, and ripples on 3 (0–14) channels, which were also spike channels. Conclusion Ripples precede rather than follow spikes, so ripples are unlikely to result from spikes. Significance Ripples and spikes seem not one-on-one coupled, but certain states of the brain can accommodate both.

Epileptiform spikes may have a different morphology and signal-to-noise ratio in simultaneously recorded EEGs and magnetoencephalograms (MEGs) that may lead to differences in the identification of spikes if both the modalities are presented separately. Moreover, there are no criteria for MEG spikes. It is unknown to which extent the visual assessment of MEG data yields consistent and meaningful results. Nineteen patients were selected with mesial temporal lobe epilepsy who underwent whole-head simultaneous MEG/EEG. These data were split into MEG and EEG files and were assessed independently by three observers for the occurrence of spikes. Interobserver kappa values were calculated. A mean kappa value greater than 0.5 was taken as a criterion for the presence of unequivocal spikes. Index cases from the resulting four subgroups were studied further. One patient had unequivocal spikes in both modalities, one in EEG only, one in MEG only, and one did not show any unequivocal spike. Spikes on which at least two observers agreed were then subjected to a template match algorithm to test for equal morphology and distribution. Equal spikes were averaged and electrical and magnetic field maps were plotted. Unequivocal spikes were found in both MEG and EEG in one patient, in MEG only in two patients, in EEG only in two patients, and no spikes in either modality were seen in 14 patients. In the four index patients, MEG showed 50 to 80% more spikes than EEG. After averaging identical consensus spikes, MEG spikes revealed a concomitant spike in the EEG, but the reverse was not always true. Even in the patient with MEG and EEG spikes that met all selection criteria, simultaneous field maps showed unexpected inconsistencies. In most patients with mesial temporal lobe epilepsy, there are no unequivocal spikes during MEG/EEG. In some cases, however, experienced electroencephalographers can identify MEG spikes reliably. Because of a better signal-to-noise ratio, more spikes could be identified in MEG than in EEG. Simultaneous MEG/EEG recordings do not simply ensure the best of both, but one modality may improve the identification of spikes in the other. In addition, different aspects of a complex source can be revealed. Our three-step approach to combined data ensures a reproducible selection of spikes for source modeling.

Pathological high frequency oscillations (HFOs, >80 Hz) are considered new biomarkers for epilepsy. They have mostly been recorded invasively, but pathological ripples (80–250 Hz) can also be found in scalp EEGs with frequent epileptiform spikes. Physiological HFOs also exist. They have been recorded invasively in hippocampus and neocortex. There are no reports of spontaneously occurring physiological HFOs recorded with scalp EEG. We aimed to study ripples in spike-free scalp EEGs. We included 23 children (6 with, 17 without epilepsy) who had an EEG without interictal epileptiform spikes recorded during sleep. We differentiated true ripples from spurious ripples such as filtering effects of sharp artifacts and high frequency components of muscle artifacts by viewing ripples simultaneously in bipolar and average montage and double-checking the unfiltered signal. We calculated mean frequency, duration and root mean square amplitude of the ripples, and studied their shape and distribution. We found ripples in EEGs of 20 out of 23 children (4 with, 16 without epilepsy). Ripples had a regular shape and occurred mostly on central and midline channels. Mean frequency was 102 Hz, mean duration 70 ms, mean root mean square amplitude 0.95 µV. Ripples occurring in normal EEGs of children without epilepsy were considered physiological; the similarity in appearance suggested that the ripples occurring in normal EEGs of children with epilepsy were also physiological. The finding that it is possible to study physiological neocortical ripples in scalp EEG paves the way for investigating their occurrence during brain development and their relation with cognitive functioning. Electronic supplementary material The online version of this article (doi:10.1007/s10548-017-0590-y) contains supplementary material, which is available to authorized users.

Objective Spontaneous high frequency oscillations (HFOs; ripples 80–250 Hz, fast ripples (FRs) 250–500 Hz) are biomarkers for epileptogenic tissue in focal epilepsy. Single pulse electrical stimulation (SPES) can evoke HFOs. We hypothesized that stimulation distinguishes pathological from physiological ripples and compared the occurrence of evoked and spontaneous HFOs within the seizure onset zone (SOZ) and eloquent functional areas. Methods Ten patients underwent SPES during 2048 Hz electrocorticography (ECoG). Evoked HFOs in time–frequency plots and spontaneous HFOs were visually analyzed. We compared electrodes with evoked and spontaneous HFOs for: percentages in the SOZ, sensitivity and specificity for the SOZ, percentages in functional areas outside the SOZ. Results Two patients without spontaneous FRs showed evoked FRs in the SOZ. Percentages of evoked and spontaneous HFOs in the SOZ were similar (ripples 32:33%, p = 0.77; FRs 43:48%, p = 0.63), but evoked HFOs had generally a lower specificity (ripples 45:69%, p = 0.02; FRs 83:92%, p = 0.04) and higher sensitivity (ripples 85:70%, p = 0.27; FRs 52:37%, p = 0.05). More electrodes with evoked than spontaneous ripples were found in functional (54:30%, p = 0.03) and ‘silent’ areas (57:27%, p = 0.01) outside the SOZ. Conclusions SPES can elicit SOZ-specific FRs in patients without spontaneous FRs, but activates ripples in all areas. Significance SPES is an alternative for waiting for spontaneous HFOs, but does not warrant exclusively pathological ripples.

Objective: We studied whether residual high-frequency oscillations (80–500 Hz; ripples, 80–250 Hz), especially fast ripples (FRs, 250–500 Hz), in post-resection intraoperative electrocorticography (ECoG) predicted seizure recurrence in comparison to residual interictal spikes and ictiform spike patterns. Methods: We studied, retrospectively, ECoG recorded at 2,048 Hz after resection in a cohort of patients with refractory focal epilepsy. We analyzed occurrence and number of residual FRs, ripples, interictal spikes, and ictiform spike patterns within the last minute of each recording and compared these to seizure recurrence. Results: We included 54 patients (median age 15.5 years) with 25 months median follow-up. Twenty-four patients had recurrent seizures. We found residual FRs, ripples, spikes, and ictiform spike patterns in 12, 51, 38, and 9 patients. Nine out of 12 patients with residual FRs had recurrent seizures ( p = 0.016, positive predictive value 75%). Other ECoG events did not predict seizure recurrence. Patients with seizures had higher FR rates than seizure-free patients ( p = 0.022). FRs near the resection and in distant pathologic areas could have changed the resection in 8 patients without harming functionally eloquent areas. One seizure-free patient had FRs in distant functionally eloquent areas. Conclusions: Residual FRs in post-resection ECoG are prognostic markers for seizure recurrence, especially if their number is high. Tailoring could rely on FRs, but requires careful assessment of the ECoG, as FRs in functionally eloquent areas might not be pathologic.

Generally, activation of the frontal eye field during seizures can cause versive (forced) gaze deviation, while non-versive head deviation is hypothesised to result from ictal neglect after inactivation of the ipsilateral temporoparietal area. Almost all non-versive head deviations occurring during temporal lobe seizures are directed to the side of seizure onset, so in derogatory cases it is worth while explaining the paradoxical event. We present a patient with a paradoxical direction of gaze deviation during temporal lobe seizures with an unexpected explanation. Electrocortical stimulation of the temporo-parieto-occipital junction elicited an irrepressible urge to look towards an illusory shadow person besides the patient. Paradoxical non-versive gaze deviations in temporal lobe seizures may be due to illusory experiences masked by postictal amnesia.

Summary Purpose [ 18 F] Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a semi-invasive, interictal method of localization of hypometabolic epileptic foci. FDG-PET can be useful in the clinical work-up prior to epilepsy surgery, especially in equivocal cases. We investigated whether we could increase the yield of presurgical FDG-PET in patients with difficult epilepsy requiring chronic subdural electrocorticography (ECoG). Methods We retrospectively studied patients with refractory focal epilepsy in whom there was uncertainty about the focus localization and who underwent FDG-PET and ECoG. Two experts (epileptologist and nuclear medicine radiologist) together systematically re-assessed the scans visually (PET RE ), blinded to their initial reports. Scans were also re-analyzed by comparing them to a normal control dataset with Statistical Parametric Mapping (SPM), using a liberal (PET SPM1 ), and strict (PET SPM2 ) statistical threshold. Regions with hypometabolism and regions containing the seizure onset zone (SOZ) in ECoG were marked as positive anatomical regions (PARs). We compared the concordance of these PARs for the different PET re-assessments. We calculated the sensitivity, specificity and accuracy of the PET results for the SOZ. The added value of the re-assessments was evaluated with emphasis on scans initially reported as negative. Results 41 Patients (63% extra-temporal) were included. PET RE identified the SOZ best, with a sensitivity of 62% and specificity of 93%. PET SPM1 had a sensitivity of 62% and specificity 69%, for PET SPM2 this was 35% and 85% respectively. The overlap between PET RE vs. PET SPM1 and vs. PET SPM2 was 71% and 37%. Visual re-assessment and PET SPM1 identified the SOZ in four out of five scans that were initially reported as negative. Conclusions Pre-surgical re-assessment of PET scans is worthwhile in epilepsy patients who undergo ECoG, especially when results were reported as negative before. Visual re-assessment itself has a higher combined specificity, sensitivity and accuracy than SPM analysis alone. SPM analysis could be used as a guide for visual (re-)assessment, because of its high sensitivity.