Pathological responses to single-pulse electrical stimuli in epilepsy: The role of feedforward inhibition.

Delineation of epileptogenic cortex in focal epilepsy patients may profit from single-pulse electrical stimulation during intracranial EEG recordings. Single-pulse electrical stimulation evokes early and delayed responses. Early responses represent connectivity. Delayed responses are a biomarker for epileptogenic cortex, but up till now, the precise mechanism generating delayed responses remains elusive. We used a data-driven modelling approach to study early and delayed responses. We hypothesized that delayed responses represent indirect responses triggered by early response activity and investigated this for 11 patients. Using two coupled neural masses, we modelled early and delayed responses by combining simulations and bifurcation analysis. An important feature of the model is the inclusion of feedforward inhibitory connections. The waveform of early responses can be explained by feedforward inhibition. Delayed responses can be viewed as second-order responses in the early response network which appear when input to a neural mass falls below a threshold forcing it temporarily to a spiking state. The combination of the threshold with noisy background input explains the typical stochastic appearance of delayed responses. The intrinsic excitability of a neural mass and the strength of its input influence the probability at which delayed responses to occur. Our work gives a theoretical basis for the use of delayed responses as a biomarker for the epileptogenic zone, confirming earlier clinical observations. The combination of early responses revealing effective connectivity, and delayed responses showing intrinsic excitability, makes single-pulse electrical stimulation an interesting tool to obtain data for computational models of epilepsy surgery.

A Comparison of Evoked and Non-evoked Functional Networks.

The growing interest in brain networks to study the brain's function in cognition and diseases has produced an increase in methods to extract these networks. Typically, each method yields a different network. Therefore, one may ask what the resulting networks represent. To address this issue we consider electrocorticography (ECoG) data where we compare three methods. We derive networks from on-going ECoG data using two traditional methods: cross-correlation (CC) and Granger causality (GC). Next, connectivity is probed actively using single pulse electrical stimulation (SPES). We compare the overlap in connectivity between these three methods as well as their ability to reveal well-known anatomical connections in the language circuit. We find that strong connections in the CC network form more or less a subset of the SPES network. GC and SPES are related more weakly, although GC connections coincide more frequently with SPES connections compared to non-existing SPES connections. Connectivity between the two major hubs in the language circuit, Broca's and Wernicke's area, is only found in SPES networks. Our results are of interest for the use of patient-specific networks obtained from ECoG. In epilepsy research, such networks form the basis for methods that predict the effect of epilepsy surgery. For this application SPES networks are interesting as they disclose more physiological connections compared to CC and GC networks.

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation.

We investigated effective networks constructed from single pulse electrical stimulation (SPES) in epilepsy patients who underwent intracranial electrocorticography. Using graph analysis, we compared network characteristics of tissue within and outside the epileptogenic area. In 21 patients with subdural electrode grids (1 cm interelectrode distance), we constructed a binary, directional network derived from SPES early responses (<100 ms). We calculated in-degree, out-degree, betweenness centrality, the percentage of bidirectional, receiving and activating connections, and the percentage of connections toward the (non-)epileptogenic tissue for each node in the network. We analyzed whether these network measures were significantly different in seizure onset zone (SOZ)-electrodes compared to non-SOZ electrodes, in resected area (RA)-electrodes compared to non-RA electrodes, and in seizure free compared to not seizure-free patients. Electrodes in the SOZ/RA showed significantly higher values for in-degree and out-degree, both at group level, and at patient level, and more so in seizure-free patients. These differences were not observed for betweenness centrality. There were also more bidirectional and fewer receiving connections in the SOZ/RA in seizure-free patients. It appears that the SOZ/RA is densely connected with itself, with only little input arriving from non-SOZ/non-RA electrodes. These results suggest that meso-scale effective network measures are different in epileptogenic compared to normal brain tissue. Local connections within the SOZ/RA are increased and the SOZ/RA is relatively isolated from the surrounding cortex. This offers the prospect of enhanced prediction of epilepsy-prone brain areas using SPES.

Tailoring epilepsy surgery with fast ripples in the intraoperative electrocorticogram.

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.

Phenomenological network models: Lessons for epilepsy surgery.

The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies. Here we study, using such a model, the influence of connections on seizures and how this might change our traditional views of epilepsy surgery. We use a simple network model consisting of four interconnected neuronal populations. One of these populations can be made hyperexcitable, modeling a pathological region of cortex. Using model simulations, the effect of surgery on the seizure rate is studied. We find that removal of the hyperexcitable population is, in most cases, not the best approach to reduce the seizure rate. Removal of normal populations located at a crucial spot in the network, the "driver," is typically more effective in reducing seizure rate. This work strengthens the idea that network structure and connections may be more important than localizing the pathological node. This can explain why lesionectomy may not always be sufficient.

Delirium detection based on monitoring of blinks and eye movements.

OBJECTIVE: To investigate whether delirious patients differ from nondelirious patients with regard to blinks and eye movements to explore opportunities for delirium detection. METHODS: Using a single-center, observational study in a tertiary hospital in the Netherlands, we studied 28 delirious elderly and 28 age- and gender-matched (group level) nondelirious elderly, postoperative cardiac surgery patients. Patients were evaluated for delirium by a geriatrician, psychiatrist, or neurologist using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria. Blinks were automatically extracted from electro-oculograms and eye movements from electroencephalography recordings using independent component analysis. The number and duration of eye movements and blinks were compared between patients with and without delirium, based on the classification of the delirium experts described above. RESULTS: During eyes-open registrations, delirious patients showed, compared with nondelirious patients, a significant decrease in the number of blinks per minute (median: 12 [interquartile range {IQR}: 5-18] versus 18 [IQR: 8-25], respectively; p = 0.02) and number of vertical eye movements per minute (median: 1 [IQR: 0-13] versus 15 [IQR: 2-54], respectively; p = 0.01) as well as an increase in the average duration of blinks (median: 0.5 [IQR: 0.36-0.95] seconds versus 0.34 [IQR: 0.23-0.53] seconds, respectively; p <0.01). During eyes-closed registrations, the average duration of horizontal eye movements was significantly increased in delirious patients compared with patients without delirium (median: 0.41 [IQR: 0.15-0.75] seconds versus 0.08 [IQR: 0.06-0.22] seconds, respectively; p <0.01). CONCLUSION: Spontaneous eye movements and particularly blinks appear to be affected in delirious patients, which holds promise for delirium detection.

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman's ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.

ER-detect: a pipeline for robust detection of early evoked responses in BIDS-iEEG electrical stimulation data.

Human brain connectivity can be measured in different ways. Intracranial EEG (iEEG) measurements during single pulse electrical stimulation provide a unique way to assess the spread of electrical information with millisecond precision. To provide a robust workflow to process these cortico-cortical evoked potential (CCEP) data and detect early evoked responses in a fully automated and reproducible fashion, we developed Early Response (ER)-detect. ER-detect is an open-source Python package and Docker application to preprocess BIDS structured iEEG data and detect early evoked CCEP responses. ER-detect can use three response detection methods, which were validated against 14-manually annotated CCEP datasets from two different sites by four independent raters. Results showed that ER-detect's automated detection performed on par with the inter-rater reliability (Cohen's Kappa of ~0.6). Moreover, ER-detect was optimized for processing large CCEP datasets, to be used in conjunction with other connectomic investigations. ER-detect provides a highly efficient standardized workflow such that iEEG-BIDS data can be processed in a consistent manner and enhance the reproducibility of CCEP based connectivity results.

EEG-fMRI correlation patterns in the presurgical evaluation of focal epilepsy: a comparison with electrocorticographic data and surgical outcome measures.

EEG-correlated functional MRI (EEG-fMRI) visualizes brain regions associated with interictal epileptiform discharges (IEDs). This technique images the epileptiform network, including multifocal, superficial and deeply situated cortical areas. To understand the role of EEG-fMRI in presurgical evaluation, its results should be validated relative to a gold standard. For that purpose, EEG-fMRI data were acquired for a heterogeneous group of surgical candidates (n=16) who were later implanted with subdural grids and strips (ECoG). The EEG-fMRI correlation patterns were systematically compared with brain areas involved in IEDs ECoG, using a semi-automatic analysis method, as well as to the seizure onset zone, resected area, and degree of seizure freedom. In each patient at least one of the EEG-fMRI areas was concordant with an interictally active ECoG area, always including the early onset area of IEDs in the ECoG data. This confirms that EEG-fMRI reflects a pattern of onset and propagation of epileptic activity. At group level, 76% of the BOLD regions that were covered with subdural grids, were concordant with interictally active ECoG electrodes. Due to limited spatial sampling, 51% of the BOLD regions were not covered with electrodes and could, therefore, not be validated. From an ECoG perspective it appeared that 29% of the interictally active ECoG regions were missed by EEG-fMRI and that 68% of the brain regions were correctly identified as inactive with EEG-fMRI. Furthermore, EEG-fMRI areas included the complete seizure onset zone in 83% and resected area in 93% of the data sets. No clear distinction was found between patients with a good or poor surgical outcome: in both patient groups, EEG-fMRI correlation patterns were found that were either focal or widespread. In conclusion, by comparison of EEG-fMRI with interictal invasive EEG over a relatively large patient population we were able to show that the EEG-fMRI correlation patterns are spatially accurate at the level of neurosurgical units (i.e. anatomical brain regions) and reflect the underlying network of IEDs. Therefore, we expect that EEG-fMRI can play an important role for the determination of the implantation strategy.

Inverse modeling in magnetic source imaging: Comparison of MUSIC, SAM(g2), and sLORETA to interictal intracranial EEG.

Magnetoencephalography (MEG) is used in the presurgical work-up of patients with focal epilepsy. In particular, localization of MEG interictal spikes may guide or replace invasive electroencephalography monitoring that is required in difficult cases. From literature, it is not clear which MEG source localization method performs best in this clinical setting. Therefore, we applied three source localization methods to the same data from a large patient group for which a gold standard, interictal spikes as identified in electrocorticography (ECoG), was available. The methods used were multiple signal classification (MUSIC), Synthetic Aperture Magnetometry kurtosis [SAM(g2)], and standardized low-resolution electromagnetic tomography. MEG and ECoG data from 38 patients with refractory focal epilepsy were obtained. Results of the three source localization methods applied to the interictal MEG data were assigned to predefined anatomical regions. Interictal spikes as identified in ECoG were also assigned to these regions. Identified regions by each MEG method were compared to ECoG. Sensitivity and positive predictive value (PPV) of each MEG method were calculated. All three MEG methods showed a similar overall correlate with ECoG spikes, but the methods differ in which regions they detect. The choice of the inverse model thus has an unexpected influence on the results of magnetic source imaging. Combining inverse methods and seeking consensus can be used to improve specificity at the cost of some sensitivity. Combining MUSIC with SAM(g2) gives the best results (sensitivity = 38% and PPV = 82%).

Developmental trajectory of transmission speed in the human brain.

The structure of the human connectome develops from childhood throughout adolescence to middle age, but how these structural changes affect the speed of neuronal signaling is not well described. In 74 subjects, we measured the latency of cortico-cortical evoked responses across association and U-fibers and calculated their corresponding transmission speeds. Decreases in conduction delays until at least 30 years show that the speed of neuronal communication develops well into adulthood.

Epileptic high-frequency oscillations in intraoperative electrocorticography: the effect of propofol.

PURPOSE: Epileptic high-frequency oscillations (HFOs; 80-500 Hz) may be used to guide neurosurgeons during epilepsy surgery to identify epileptogenic tissue. We studied the effect of the anesthetic agent propofol on the occurrence of HFOs in intraoperative electrocorticography (ECoG). METHODS: We selected patients who were undergoing surgery for temporal lobe epilepsy with a standardized electrode grid placement. Intraoperative ECoG was recorded at 2,048 Hz following cessation of propofol. The number and distribution of interictal spikes, ripples (R [80-250 Hz]), and fast ripples (FRs; 250-500 Hz) were analyzed. The amount of events on mesiotemporal channels and lateral neocortical channels were compared between patients with a suspected mesiotemporal and lateral epileptogenic area (Student's t-test), and HFOs were compared with the irritative zone, using correlation between amounts of events per channel, to provide evidence for the epileptic nature of the HFOs. Next, the amount of events within the first minute and the last minute were compared to each other and the change in events over the entire epochs was analyzed using correlation analyses of 10 epochs during the emergence periods (Spearman rank test). We studied whether the duration of HFOs changed over time. The change in events within presumed epileptogenic area was compared to the change outside this area (Student's t-test). Periods of burst suppression and continuous background activity were compared between and within patients (t-test). KEY FINDINGS: Twelve patients were included: five with suspected mesiotemporal epileptogenic area and three with suspected lateral epileptogenic area (and four were "other"). Spikes, ripples, and FRs were related to the suspected epileptogenic areas, and HFO zones were related to the irritative zones. Ripples and FRs increased during emergence from propofol anesthesia (mean number of ripples from first minute-last minute: 61.5-73.0, R = 0.46, p < 0.01; FRs: 3.1-5.7, R = 0.30, p < 0.01) and spikes remained unchanged (80.1-79.9, R = -0.05, p = 0.59). There was a decrease in number of channels with spikes (R = -0.18, p = 0.05), but no change in ripples (R = -0.13, p = 0.16) or FRs (R = 0.11, p = 0.45). There was no change in the durations of HFOs. The amount of HFOs in the presumed epileptogenic areas did not change more than the amount outside the presumed epileptogenic area, whereas spikes paradoxically decreased more within the suspected epileptogenic area. Six patients showing burst-suppression had lower rates of ripples than six other patients with continuous background activity (p = 0.02). No significant difference was found between burst suppression and continuous background activity in four patients, but there was a trend toward showing more ripples during continuous background activity (p = 0.16). SIGNIFICANCE: Propofol, known for its antiepileptic effects, reduces the number of epileptic HFOs, but has no effect on spikes. This enforces the hypothesis that, in epilepsy, HFOs mirror the disease activity and HFOs might be useful for monitoring antiepileptic drug treatment. It is feasible to record HFOs during surgery, but propofol infusion should be interrupted for some minutes to improve detection.

Time-frequency analysis of single pulse electrical stimulation to assist delineation of epileptogenic cortex.

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 <50% of fast ripples removed by resection had a poor outcome. A wavelet transform-based time-frequency analysis of single pulse electrical stimulation reveals evoked responses in the frequency range of spikes, ripples and fast ripples. We demonstrate that time-frequency analysis of single pulse electrical stimulation can assist in delineation of the epileptogenic cortex using time-frequency single pulse-evoked fast ripples as a potential new marker.

High-resolution electric source imaging for presurgical evaluation of tuberous sclerosis complex patients.

OBJECTIVE: We retrospectively assessed the localizing value of patient-history-based semiology (PHS), video-based semiology (VS), long-term monitoring video electroencephalography (LTM-VEEG) and interictal high resolution electric source imaging (HR-ESI) in the presurgical workup of patients with tuberous sclerosis complex (TSC). METHODS: Data from 24 consecutive TSC surgical candidates who underwent both HR-ESI and LTM-VEEG was retrospectively collected. PHS and VS were analyzed to hypothesize the symptomatogenic zone localization. LTM-VEEG and HR-ESI localization results were extracted from the diagnostic reports. Localizing value was compared between modalities, taken the resected/disconnected area of surgical patients in consideration. HR-ESI's impact on the epileptogenic zone hypothesis and surgical workup was evaluated. RESULTS: Semiology, interictal EEG, ictal EEG and HR-ESI were localizing in 25%, 54%, 63% and 79% of patients. Inter-modality concordance ranged between 33-89%. In good surgical outcome patients, PHS, VS, interictal EEG, ictal EEG and HR-ESI showed concordance with resected area in 1/9 (11%), 0/9 (0%), 4/9 (44%), 3/9 (33%) and 6/9 patients (67%). HR-ESI positively impacts clinical management in 50% of patients. CONCLUSIONS: In presurgical evaluation of TSC patients, semiology often has limited localizing value. Presurgical work-up benefits from HR-ESI. SIGNIFICANCE: Our findings may advice future presurgical epilepsy workup of TSC patients with the ultimate aim to improve outcome.

Accurate differentiation between physiological and pathological ripples recorded with scalp-EEG.

OBJECTIVE: To compare scalp-EEG recorded physiological ripples co-occurring with vertex waves to pathological ripples co-occurring with interictal epileptiform discharges (IEDs). METHODS: We marked ripples in sleep EEGs of children. We compared the start of ripples to vertex wave- or IED-start, and duration, frequency, and root mean square (RMS) amplitude of physiological and pathological ripples using multilevel modeling. Ripples were classified as physiological or pathological using linear discriminant analysis. RESULTS: We included 40 children with and without epilepsy. Ripples started (χ2(1) = 38.59, p < 0.001) later if they co-occurred with vertex waves (108.2 ms after vertex wave-start) than if they co-occurred with IEDs (4.3 ms after IED-start). Physiological ripples had longer durations (75.7 ms vs 53.0 ms), lower frequencies (98.3 Hz vs 130.6 Hz), and lower RMS amplitudes (0.9 µV vs 1.8 µV, all p < 0.001) than pathological ripples. Ripples could be classified as physiological or pathological with 98 % accuracy. Ripples recorded in children with idiopathic or symptomatic epilepsy seemed to form two subgroups of pathological ripples. CONCLUSIONS: Ripples co-occurring with vertex waves or IEDs have different characteristics and can be differentiated as physiological or pathological with high accuracy. SIGNIFICANCE: This is the first study that compares physiological and pathological ripples recorded with scalp EEG.

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

BACKGROUND: Intraoperative electrocorticography is used to tailor epilepsy surgery by analysing interictal spikes or spike patterns that can delineate epileptogenic tissue. High-frequency oscillations (HFOs) on intraoperative electrocorticography have been proposed as a new biomarker of epileptogenic tissue, with higher specificity than spikes. We prospectively tested the non-inferiority of HFO-guided tailoring of epilepsy surgery to spike-guided tailoring on seizure freedom at 1 year. METHODS: The HFO trial was a randomised, single-blind, adaptive non-inferiority trial at an epilepsy surgery centre (UMC Utrecht) in the Netherlands. We recruited children and adults (no age limits) who had been referred for intraoperative electrocorticography-tailored epilepsy surgery. Participants were randomly allocated (1:1) to either HFO-guided or spike-guided tailoring, using an online randomisation scheme with permuted blocks generated by an independent data manager, stratified by epilepsy type. Treatment allocation was masked to participants and clinicians who documented seizure outcome, but not to the study team or neurosurgeon. Ictiform spike patterns were always considered in surgical decision making. The primary endpoint was seizure outcome after 1 year (dichotomised as seizure freedom [defined as Engel 1A-B] vs seizure recurrence [Engel 1C-4]). We predefined a non-inferiority margin of 10% risk difference. Analysis was by intention to treat, with prespecified subgroup analyses by epilepsy type and for confounders. This completed trial is registered with the Dutch Trial Register, Toetsingonline ABR.NL44527.041.13, and ClinicalTrials.gov, NCT02207673. FINDINGS: Between Oct 10, 2014, and Jan 31, 2020, 78 individuals were enrolled to the study and randomly assigned (39 to HFO-guided tailoring and 39 to spike-guided tailoring). There was no loss to follow-up. Seizure freedom at 1 year occurred in 26 (67%) of 39 participants in the HFO-guided group and 35 (90%) of 39 in the spike-guided group (risk difference -23·5%, 90% CI -39·1 to -7·9; for the 48 patients with temporal lobe epilepsy, the risk difference was -25·5%, -45·1 to -6·0, and for the 30 patients with extratemporal lobe epilepsy it was -20·3%, -46·0 to 5·4). Pathology associated with poor prognosis was identified as a confounding factor, with an adjusted risk difference of -7·9% (90% CI -20·7 to 4·9; adjusted risk difference -12·5%, -31·0 to 5·9, for temporal lobe epilepsy and 5·8%, -7·7 to 19·5, for extratemporal lobe epilepsy). We recorded eight serious adverse events (five in the HFO-guided group and three in the spike-guided group) requiring hospitalisation. No patients died. INTERPRETATION: HFO-guided tailoring of epilepsy surgery was not non-inferior to spike-guided tailoring on intraoperative electrocorticography. After adjustment for confounders, HFOs show non-inferiority in extratemporal lobe epilepsy. This trial challenges the clinical value of HFOs as an epilepsy biomarker, especially in temporal lobe epilepsy. Further research is needed to establish whether HFO-guided intraoperative electrocorticography holds promise in extratemporal lobe epilepsy. FUNDING: UMCU Alexandre Suerman, EpilepsieNL, RMI Talent Fellowship, European Research Council, and MING Fund.

Regional differences in the sensitivity of MEG for interictal spikes in epilepsy.

MEG interictal spikes as recorded in epilepsy patients are a reflection of intracranial interictal activity. This study investigates the relationship between the estimated sources of MEG spikes and the location, distribution and size of interictal spikes in the invasive ECoG of a group of 38 epilepsy patients that are monitored for pre-surgical evaluation. An amplitude/surface area measure is defined to quantify and rank ECoG spikes. It is found that all MEG spikes are associated with an ECoG spike that is among the three highest ranked in a patient. Among the different brain regions considered, the fronto-orbital, inter-hemispheric, tempero-lateral and central regions stand out. In an accompanying simulation study it is shown that for hypothesized extended sources of larger sizes, as suggested by the data, source location, orientation and curvature can partly explain the observed sensitivity of MEG for interictal spikes.

A skew-based method for identifying intracranial EEG channels with epileptic activity without detecting spikes, ripples, or fast ripples.

OBJECTIVE: To develop a method for identifying intracranial EEG (iEEG) channels with epileptic activity without the need to detect spikes, ripples, or fast ripples. METHODS: We compared the skew of the distribution of power values from five minutes non-rapid eye movement stage N3 sleep for the 5-80 Hz, 80-250 Hz (ripple), and 250-500 Hz (fast ripple) bands of epileptic (located in seizure-onset or irritative zone) and non-epileptic iEEG channels recorded in patients with drug-resistant focal epilepsy. We optimized settings in 120 bipolar channels from 10 patients, compared the results to 120 channels from another 10 patients, and applied the method to channels of 12 individual patients. RESULTS: The distribution of power values was more skewed in epileptic than in non-epileptic channels in all three frequency bands. The differences in skew were correlated with the presence of spikes, ripples, and fast ripples. When classifying epileptic and non-epileptic channels, the mean accuracy over 12 patients was 0.82 (sensitivity: 0.76, specificity: 0.91). CONCLUSIONS: The 'skew method' can distinguish epileptic from non-epileptic channels with good accuracy and, in particular, high specificity. SIGNIFICANCE: This is an easy-to-apply method that circumvents the need to visually mark or automatically detect interictal epileptic events.