Removing high-frequency oscillations: A prospective multicenter study on seizure outcome

The value of intra-operative electrographic biomarkers for tailoring during epilepsy surgery: from group-level to patient-level analysis

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.

Cognitive refractory state caused by spontaneous epileptic high-frequency oscillations in the human brain

Epileptic brain tissue is often considered physiologically dysfunctional, and the optimal treatment of many patients with uncontrollable seizures involves surgical removal of the epileptic tissue. However, it is unclear to what extent the epileptic tissue is capable of generating physiological responses to cognitive stimuli and how cognitive deficits ensuing surgical resections can be determined using state-of-the-art computational methods. To address these unknowns, we recruited six patients with nonlesional epilepsies and identified the epileptic focus in each patient with intracranial electrophysiological monitoring. We measured spontaneous epileptic activity in the form of high-frequency oscillations (HFOs), recorded stimulus-locked physiological responses in the form of physiological high-frequency broadband activity, and explored the interaction of the two as well as their behavioral correlates. Across all patients, we found abundant normal physiological responses to relevant cognitive stimuli in the epileptic sites. However, these physiological responses were more likely to be "seized" (delayed or missed) when spontaneous HFOs occurred about 850 to 1050 ms before, until about 150 to 250 ms after, the onset of relevant cognitive stimuli. Furthermore, spontaneous HFOs in medial temporal lobe affected the subjects' memory performance. Our findings suggest that nonlesional epileptic sites are capable of generating normal physiological responses and highlight a compelling mechanism for cognitive deficits in these patients. The results also offer clinicians a quantitative tool to differentiate pathological and physiological high-frequency activities in epileptic sites and to indirectly assess their possible cognitive reserve function and approximate the risk of resective surgery.

Shift in electrocorticography electrode locations after surgical implantation in children

Interpreting electrocorticography (ECoG) in the context of neuroimaging requires that multimodal information be integrated accurately. However, the implantation of ECoG electrodes can shift the brain impacting the spatial interpretation of electrode locations in the context of pre-implant imaging. We characterized the amount of shift in ECoG electrode locations immediately after implant in a pediatric population. Electrode-shift was quantified as the difference in the electrode locations immediately after surgery (via post-operation CT) compared to the brain surface before the operation (pre-implant T1 MRI). A total of 1140 ECoG contracts were assessed across 18 patients ranging from 3 to 19 (12.1 ± 4.8) years of age who underwent intracranial monitoring in preparation for epilepsy resection surgery. Patients had an average of 63 channels assessed with an average of 5.64 ± 3.27 mm shift from the pre-implant brain surface within 24 h of implant. This shift significantly increased with estimated intracranial volume, but not age. Shift also varied significantly depending of the lobe the contact was over; where contacts on the temporal and frontal lobe had less shift than the parietal. Furthermore, contacts on strips had significantly less shift than those on grids. The shift in the brain surface due to ECoG implantation could lead to a misinterpretation of contact location particularly in patients with larger intracranial volume and for grid contacts over the parietal lobes.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CLASSIFICATION OF EVIDENCE

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

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

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

Intracranial EEG in the 21st Century

Intracranial electroencephalography (iEEG) has been the mainstay of identifying the seizure onset zone (SOZ), a key diagnostic procedure in addition to neuroimaging when considering epilepsy surgery. In many patients, iEEG has been the basis for resective epilepsy surgery, to date still the most successful treatment for drug-resistant epilepsy. Intracranial EEG determines the location and resectability of the SOZ. Advances in recording and implantation of iEEG provide multiple options in the 21st century. This not only includes the choice between subdural electrodes (SDE) and stereoelectroencephalography (SEEG) but also includes the implantation and recordings from microelectrodes. Before iEEG implantation, especially in magnetic resonance imaging -negative epilepsy, a clear hypothesis for seizure generation and propagation should be based on noninvasive methods. Intracranial EEG implantation should be planned by a multidisciplinary team considering epileptic networks. Recordings from SDE and SEEG have both their advantages and disadvantages. Stereo-EEG seems to have a lower rate of complications that are clinically significant, but has limitations in spatial sampling of the cortical surface. Stereo-EEG can sample deeper areas of the brain including deep sulci and hard to reach areas such as the insula. To determine the epileptogenic zone, interictal and ictal information should be taken into consideration. Interictal spiking, low frequency slowing, as well as high frequency oscillations may inform about the epileptogenic zone. Ictally, high frequency onsets in the beta/gamma range are usually associated with the SOZ, but specialized recordings with combined macro and microelectrodes may in the future educate us about onset in higher frequency bands. Stimulation of intracranial electrodes triggering habitual seizures can assist in identifying the SOZ. Advanced computational methods such as determining the epileptogenicity index and similar measures may enhance standard clinical interpretation. Improved techniques to record and interpret iEEG may in the future lead to a greater proportion of patients being seizure free after epilepsy surgery.

Expert consensus on clinical applications of high-frequency oscillations in epilepsy

Studies in animal models of epilepsy and pre-surgical patients have unanimously found a strong correlation between high-frequency oscillations (HFOs, > 80 Hz) and the epileptogenic zone, suggesting that HFOs can be a potential biomarker of epileptogenicity and epileptogenesis. This consensus includes the definition and standard detection techniques of HFOs, the localizing value of pathological HFOs for epileptic foci, and different ways to distinguish physiological from epileptic HFOs. The latest clinical applications of HFOs in epilepsy and the related findings are also discussed. HFOs will advance our understanding of the pathophysiology of epilepsy.

Seizure onset location shapes dynamics of initiation

OBJECTIVE

Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-frequency interactions, can discriminate between seizure types.

METHODS

We analyzed temporal changes in low and high frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and ripple/fast ripple frequency bands at seizure onset.

RESULTS

Seizures of multiple electrographic patterns were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset.

CONCLUSIONS

Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism.

SIGNIFICANCE

To learn how seizures are initiated, researchers would do well to consider other aspects of their manifestation, in addition to their electrographic patterns. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.

Estimation of the epileptogenic-zone with HFO sub-groups exhibiting various levels of epileptogenicity

High-Frequency-Oscillations (HFO) are biomarkers of the epileptogenic-zone (EZ) and thus a potential aid in guiding epilepsy-surgery. HFO are normally sub-divided according to their oscillating-frequency into Ripples (80-250 Hz) and Fast-Ripples (FR) (250-500 Hz) and are known to also occur in the non-epileptic brain. We address two challenges faced by HFO: firstly, estimating the margins of the EZ using the HFO occurrence-rate from each intracranial EEG channel; secondly, selecting HFO sub-groups with a higher probability of being purely epileptic. We propose the clustering of channels with high HFO occurrence-rates as a deterministic method to delimit the EZ. Additionally, we perform the EZ estimation using 9 sub-groups of HFO; these sub-groups are determined by their temporal and spatial coincidence with intracranial interictal-epileptic-spikes (IES) and are assumed to have varying levels of epileptogenicity. The EZ estimated with the different HFO-sub-groups are compared between themselves and with a proxy of the factually undefinable EZ, namely the resected-volume (RV). The proposed clustering method proved to be deterministic and allowed estimating the EZ for each patient and each HFO-sub-group. Those Ripples assumed to be more epileptogenic occurred in lower numbers than all Ripples but showed the highest correspondence with the RV. All FR sub-groups showed a high specificity to the RV. The proposed clustering method successfully extracted the information from the HFO occurrence-rate to estimate the EZ. The selection of more epileptogenic HFO based on their coincidence with IES proved to be of value for both Ripples and FR.

SGM: a novel time-frequency algorithm based on unsupervised learning improves high-frequency oscillation detection in epilepsy

OBJECTIVE

We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors.

APPROACH

This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments.

MAIN RESULTS

We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM.

SIGNIFICANCE

The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.

Kurtosis and skewness of high-frequency brain signals are altered in paediatric epilepsy

Intracranial studies provide solid evidence that high-frequency brain signals are a new biomarker for epilepsy. Unfortunately, epileptic (pathological) high-frequency signals can be intermingled with physiological high-frequency signals making these signals difficult to differentiate. Recent success in non-invasive detection of high-frequency brain signals opens a new avenue for distinguishing pathological from physiological high-frequency signals. The objective of the present study is to characterize pathological and physiological high-frequency signals at source levels by using kurtosis and skewness analyses. Twenty-three children with medically intractable epilepsy and age-/gender-matched healthy controls were studied using magnetoencephalography. Magnetoencephalographic data in three frequency bands, which included 2–80 Hz (the conventional low-frequency signals), 80–250 Hz (ripples) and 250–600 Hz (fast ripples), were analysed. The kurtosis and skewness of virtual electrode signals in eight brain regions, which included left/right frontal, temporal, parietal and occipital cortices, were calculated and analysed. Differences between epilepsy and controls were quantitatively compared for each cerebral lobe in each frequency band in terms of kurtosis and skewness measurements. Virtual electrode signals from clinical epileptogenic zones and brain areas outside of the epileptogenic zones were also compared with kurtosis and skewness analyses. Compared to controls, patients with epilepsy showed significant elevation in kurtosis and skewness of virtual electrode signals. The spatial and frequency patterns of the kurtosis and skewness of virtual electrode signals among the eight cerebral lobes in three frequency bands were also significantly different from that of the controls (2–80 Hz, P < 0.001; 80–250 Hz, P < 0.00001; 250–600 Hz, P < 0.0001). Compared to signals from non-epileptogenic zones, virtual electrode signals from epileptogenic zones showed significantly altered kurtosis and skewness (P < 0.001). Compared to normative data from the control group, aberrant virtual electrode signals were, for each patient, more pronounced in the epileptogenic lobes than in other lobes(kurtosis analysis of virtual electrode signals in 250–600 Hz; odds ratio = 27.9; P < 0.0001). The kurtosis values of virtual electrode signals in 80–250 and 250–600 Hz showed the highest sensitivity (88.23%) and specificity (89.09%) for revealing epileptogenic lobe, respectively. The combination of virtual electrode and kurtosis/skewness measurements provides a new quantitative approach to distinguishing pathological from physiological high-frequency signals for paediatric epilepsy. Non-invasive identification of pathological high-frequency signals may provide novel important information to guide clinical invasive recordings and direct surgical treatment of epilepsy.

Active direct current (DC) shifts and "Red slow": two new concepts for seizure mechanisms and identification of the epileptogenic zone

An accurate identification of the epileptogenic zone is essential for patients with intractable epilepsy who are candidates to neurosurgery. EEG recordings can provide predictive biomarkers of the epileptogenic zone. Wide-band EEG makes it possible to record from infraslow (including DC shifts) to high frequency (HFO, over 300 Hz) oscillations for diagnostic purposes in patients with epilepsy. Although the presence of HFOs have been proposed to sign the epileptogenic zone, DC-like recordings demonstrate that DC shifts precede HFOs at seizure onset. This led to the proposal that "ictal active DC shifts" are causally related to seizure onset as opposed to "ictal passive DC shifts". Thus, active DC shifts may constitute predictive biomarkers of the epileptogenic zone in epilepsy. Since DC shift is commonly associated to a rise in extracellular potassium, potassium homeostasis regulated by Kir4.1 channels in astrocytes may play an key role at seizure onset. In addition, we hypothesize that, during the interictal period, the co-occurrence of slow events and interictal HFOs, so-called "Red slow", may also delineate an epileptogenic zone, even if a seizure would not be actually recorded.

Changing concepts in presurgical assessment for epilepsy surgery

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.

Automatic detection of high-frequency-oscillations and their sub-groups co-occurring with interictal-epileptic-spikes

OBJECTIVE

High-frequency-oscillations (HFO) and interictal-epileptic-spikes (IES) are spatial biomarkers of the epileptogenic-zone. Those HFO spatially and temporally co-occurring with IES (IES-HFO) are potentially superior biomarkers, their use is however challenged by the difficulty in detecting the low amplitude HFO riding the high-amplitude and steep-waveform of IES. We aim to develop an automatic HFO detector with an improved performance with respect to current methods and that also correctly distinguishes IES-HFO from IES occurring in isolation (isol-IES). We also aim to validate the biomarker-value of the automatic detections by utilizing them to localize a surrogate of the epileptogenic-zone.

APPROACH

We developed automatic-detectors of HFO-Ripples (80-250 Hz), HFO-FastRipples (250-500 Hz) and IES based on kernelized support-vector-machines (SVM). The training of the HFO-detectors was based on visually marked HFO and the parameter optimization during this training-process promoted the correct discernment between IES-HFO and isol-IES. Both HFO-detectors were benchmarked against other published detectors using databases with both visually marked and simulated gold-standards. The IES-detector was trained with a publicly available simulated database and tested against both simulated and visually marked gold-standards. To validate the detections' biomarker-value, the detectors were run on intracranial-EEGs from 8 patients and each with durations of 2-3 days, the detections' cumulated per-channel occurrence-rate was then used to predict the seizure-onset-zone as a surrogate of the epileptogenic-zone.

MAIN RESULTS

The HFO-detectors obtained at least 21 more F1-score points than previously published algorithms at the lowest signal-to-noise-ratio. The success achieved when discerning between IES-HFO and isol-IES was comparable to that of other published algorithms. The automatically detected IES-HFO and IES-Ripples showed the best biomarker-value to localize the epileptogenic-zone.

SIGNIFICANCE

The developed detectors are publicly available and provide a reliable tool to further study HFO, IES-HFO and their value as biomarkers. The putative higher biomarker value from IES-HFO was validated on automatically-detected, long-term data.

Are high-frequency oscillations better biomarkers of the epileptogenic zone than spikes?

PURPOSE OF REVIEW

Precise localization of the epileptogenic zone is imperative for the success of resective surgery of drug-resistant epileptic patients. To decrease the number of surgical failures, clinical research has been focusing on finding new biomarkers. For the past decades, high-frequency oscillations (HFOs, 80-500 Hz) have ousted interictal spikes - the classical interictal marker - from the research spotlight. Many studies have claimed that HFOs were more linked to epileptogenicity than spikes. This present review aims at refining this statement in light of recent studies.

RECENT FINDINGS

Analysis based on single-patient characteristics has not been able to determine which of HFOs or spikes were better marker of epileptogenic tissues. Physiological HFOs are one of the main obstacles to translate HFOs to clinical practice as separating them from pathological HFOs remains a challenge. Fast ripples (a subgroup of HFOs, 250-500 Hz) which are mostly pathological are not found in all epileptogenic tissues.

SUMMARY

Quantified measures of HFOs and spikes give complementary results, but many barriers still persist in applying them in clinical routine. The current way of testing HFO and spike detectors and their performance in delineating the epileptogenic zone is debatable and still lacks practicality. Solutions to handle physiological HFOs have been proposed but are still at a preliminary stage.

Graph index complexity as a novel surrogate marker of high frequency oscillations in delineating the seizure onset zone

OBJECTIVE

To investigate the Graph Index Complexity (uGIC) as a marker of high frequency oscillatory (HFO) activity, the seizure onset zone (SOZ), and surgical outcome.

METHODS

The SOZ, rates of HFOs at two thresholds (broad, strict), and uGIC were determined using EEG data from 41 patients. The correlation between HFOs and uGIC were calculated. HFOs and uGIC were compared within and outside the SOZ. Postsurgical outcome was compared to the colocalization of HFOs and resected SOZ.

RESULTS

There was significant correlation between uGIC and both broad (r = 0.69, p < 0.0005) and strict HFOs (r = 0.48, p < 0.0005). All were significantly greater within the SOZ overall, but only in 17/41 (strict, uGIC) or 18/41 (broad) patients. HFO markers were significantly greater within the SOZ for 8/15 patients with positive postsurgical outcomes, but not for any patients with negative outcomes (0/5).

CONCLUSION

The uGIC is a marker of HFO activity, while HFOs and uGIC are markers of the SOZ overall. Colocalization of HFOs and the SOZ has strong positive predictive value for postsurgical outcome, but poor negative predictive value.

SIGNIFICANCE

The uGIC is an objective surrogate marker of HFO activity independent of identifying discrete HFO events.

Quantitative analysis of intracranial electrocorticography signals using the concept of statistical parametric mapping

Statistical parametric mapping (SPM) is a technique with which one can delineate brain activity statistically deviated from the normative mean, and has been commonly employed in noninvasive neuroimaging and EEG studies. Using the concept of SPM, we developed a novel technique for quantification of the statistical deviation of an intracranial electrocorticography (ECoG) measure from the nonepileptic mean. We validated this technique using data previously collected from 123 patients with drug-resistant epilepsy who underwent resective epilepsy surgery. We determined how the measurement of statistical deviation of modulation index (MI) from the non-epileptic mean (rated by z-score) improved the performance of seizure outcome classification model solely based on conventional clinical, seizure onset zone (SOZ), and neuroimaging variables. Here, MI is a summary measure quantifying the strength of in-situ coupling between high-frequency activity at >150 Hz and slow wave at 3-4 Hz. We initially generated a normative MI atlas showing the mean and standard deviation of slow-wave sleep MI of neighboring non-epileptic channels of 47 patients, whose ECoG sampling involved all four lobes. We then calculated 'MI z-score' at each electrode site. SOZ had a greater 'MI z-score' compared to non-SOZ in the remaining 76 patients. Subsequent multivariate logistic regression analysis and receiver operating characteristic analysis to the combined data of all patients revealed that the full regression model incorporating all predictor variables, including SOZ and 'MI z-score', best classified the seizure outcome with sensitivity/specificity of 0.86/0.76. The model excluding 'MI z-score' worsened its sensitivity/specificity to 0.86/0.48. Furthermore, the leave-one-out analysis successfully cross-validated the full regression model. Measurement of statistical deviation of MI from the non-epileptic mean on invasive recording is technically feasible. Our analytical technique can be used to evaluate the utility of ECoG biomarkers in epilepsy presurgical evaluation.

High-frequency oscillations mirror severity of human temporal lobe seizures

Objective

Many patients with epilepsy have both focal and bilateral tonic‐clonic seizures (BTCSs), but it is largely unclear why ictal activity spreads only sometimes. Previous work indicates that interictal high‐frequency oscillations (HFOs), traditionally subdivided into ripples (80–250 Hz) and fast ripples (250–500 Hz), are a promising biomarker of epileptogenicity. We aimed to investigate whether HFOs correlate with the emergence of seizure activity and whether they differ between focal seizures (FSs) with impaired awareness and BTCSs.

Methods

We retrospectively analyzed 15 FSs and 13 BTCSs from seven patients with mesial temporal lobe epilepsy, each of them with at least one BTCS and at least one FS. Representative intervals of intracranial electroencephalography from the seizure onset zone (SOZ) and remote non‐SOZ areas were selected to compare pre‐ictal, complex focal, tonic‐clonic, and postictal periods. Ripples and fast ripples were visually identified and their density, that is, percentage of time occupied by the respective events, computed.

Results

Ripple and fast ripple densities increased inside the SOZ after seizure onset (P < 0.01) and in remote areas after progression to BTCSs (P < 0.01). Postictal SOZ ripple density dropped below pre‐ictal levels (P < 0.001). Prior to onset of bilateral tonic‐clonic movements, ripple density inside the SOZ is higher in BTCSs than in FSs (P < 0.05).

Interpretation

Ripples and fast ripples correlate with onset and spread of ictal activity. Abundant ripples inside the SOZ may reflect the activation of specific neuronal networks related to imminent spread of seizure activity.

NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram

OBJECTIVE

Interictal epileptiform anomalies such as epileptiform discharges or high-frequency oscillations show marked variations across the sleep-wake cycle. This study investigates which state of vigilance is the best to localize the epileptogenic zone (EZ) in interictal intracranial electroencephalography (EEG).

METHODS

Thirty patients with drug-resistant epilepsy undergoing stereo-EEG (SEEG)/sleep recording and subsequent open surgery were included; 13 patients (43.3%) had good surgical outcome (Engel class I). Sleep was scored following standard criteria. Multiple features based on the interictal EEG (interictal epileptiform discharges, high-frequency oscillations, univariate and bivariate features) were used to train a support vector machine (SVM) model to classify SEEG contacts placed in the EZ. The performance of the algorithm was evaluated by the mean area under the receiver-operating characteristic (ROC) curves (AUCs) and positive predictive values (PPVs) across 10-minute sections of wake, non-rapid eye movement sleep (NREM) stages N2 and N3, REM sleep, and their combination.

RESULTS

Highest AUCs were achieved in NREM sleep stages N2 and N3 compared to wakefulness and REM (P < .01). There was no improvement when using a combination of all four states (P > .05); the best performing features in the combined state were selected from NREM sleep. There were differences between good (Engel I) and poor (Engel II-IV) outcomes in PPV (P < .05) and AUC (P < .01) across all states. The SVM multifeature approach outperformed spikes and high-frequency oscillations (P < .01) and resulted in results similar to those of the seizure-onset zone (SOZ; P > .05).

SIGNIFICANCE

Sleep improves the localization of the EZ with best identification obtained in NREM sleep stages N2 and N3. Results based on the multifeature classification in 10 minutes of NREM sleep were not different from the results achieved by the SOZ based on 12.7 days of seizure monitoring. This finding might ultimately result in a more time-efficient intracranial presurgical investigation of focal epilepsy.

Magnetoencephalography imaging of high frequency oscillations strengthens presurgical localization and outcome prediction

In patients with medically refractory epilepsy, resective surgery is the mainstay of therapy to achieve seizure freedom. However, ∼20-50% of cases have intractable seizures post-surgery due to the imprecise determination of epileptogenic zone. Recent intracranial studies suggest that high frequency oscillations between 80 and 200 Hz could serve as one of the consistent epileptogenicity biomarkers for localization of the epileptogenic zone. However, these high frequency oscillations are not adopted in the clinical setting because of difficult non-invasive detection. Here, we investigated non-invasive detection and localization of high frequency oscillations and its clinical utility in accurate pre-surgical assessment and post-surgical outcome prediction. We prospectively recruited 52 patients with medically refractory epilepsy who underwent standard pre-surgical workup including magnetoencephalography (MEG) followed by resective surgery after determination of the epileptogenic zone. The post-surgical outcome was assessed after 22.14 ± 10.05 months. Interictal epileptic spikes were expertly identified, and interictal epileptic oscillations across the neural activity frequency spectrum from 8 to 200 Hz were localized using adaptive spatial filtering methods. Localization results were compared with epileptogenic zone and resected cortex for congruence assessment and validated against the clinical outcome. The concordance rate of high frequency oscillations sources (80-200 Hz) with the presumed epileptogenic zone and the resected cortex were 75.0% and 78.8%, respectively, which is superior to that of other frequency bands and standard dipole fitting methods. High frequency oscillation sources corresponding with the resected cortex, had the best sensitivity of 78.0%, positive predictive value of 100% and an accuracy of 78.84% to predict the patient's surgical outcome, among all other frequency bands. If high frequency oscillation sources were spatially congruent with resected cortex, patients had an odds ratio of 5.67 and 82.4% probability of achieving a favourable surgical outcome. If high frequency oscillations sources were discordant with the epileptogenic zone or resection area, patient has an odds ratio of 0.18 and only 14.3% probability of achieving good outcome, and mostly tended to have an unfavourable outcome (χ2 = 5.22; P = 0.02; φ = -0.317). In receiver operating characteristic curve analyses, only sources of high-frequency oscillations demonstrated the best sensitivity and specificity profile in determining the patient's surgical outcome with area under the curve of 0.76, whereas other frequency bands indicate a poor predictive performance. Our study is the first non-invasive study to detect high frequency oscillations, address the efficacy of high frequency oscillations over the different neural oscillatory frequencies, localize them and clinically validate them with the post-surgical outcome in patients with medically refractory epilepsy. The evidence presented in the current study supports the fact that HFOs might significantly improve the presurgical assessment, and post-surgical outcome prediction, where it could widely be used in a clinical setting as a non-invasive biomarker.

Assessing Epileptogenicity Using Phase-Locked High Frequency Oscillations: A Systematic Comparison of Methods

High frequency oscillations (HFOs) are traditional biomarkers to identify the epileptogenic tissue during presurgical evaluation in pharmacoresistant epileptic patients. Recently, the resection of brain tissue exhibiting coupling between the amplitude of HFOs and the phase of low frequencies demonstrated a more favorable surgical outcome. Here we compare the predictive value of ictal HFOs and four methods for quantifying the ictal phase-amplitude coupling, namely mean vector length, phase-locked high gamma, phase locking value, and modulation index (MI). We analyzed 32 seizures from 16 patients to identify the channels that exhibit HFOs and phase-locked HFOs during seizures. We compared the resection ratio, defined as the percentage of channels exhibiting coupling located in the resected tissue, with the postsurgical outcome. We found that the MI is the only method to show a significant difference between the resection ratios of patients with good and poor outcomes. We further show that the whole seizure, not only the onset, is critical to assess epileptogenicity using the phase-locked HFOs. We postulate that the superiority of MI stems from its capacity to assess coupling of discrete HFO events and its independence from the HFO power. These results confirm that quantitative analysis of HFOs can boost presurgical evaluation and indicate the paramount importance of algorithm selection for clinical applications.

Linear and nonlinear interrelations show fundamentally distinct network structure in preictal intracranial EEG of epilepsy patients

Resection of the seizure generating tissue can be highly beneficial in patients with drug-resistant epilepsy. However, only about half of all patients undergoing surgery get permanently and completely seizure free. Investigating the dependences between intracranial EEG signals adds a multivariate perspective largely unavailable to visual EEG analysis, which is the current clinical practice. We examined linear and nonlinear interrelations between intracranial EEG signals regarding their spatial distribution and network characteristics. The analyzed signals were recorded immediately before clinical seizure onset in epilepsy patients who received a standardized electrode implantation targeting the mesiotemporal structures. The linear interrelation networks were predominantly locally connected and highly reproducible between patients. In contrast, the nonlinear networks had a clearly centralized structure, which was specific for the individual pathology. The nonlinear interrelations were overrepresented in the focal hemisphere and in patients with no or only rare seizures after surgery specifically in the resected tissue. Connections to the outside were predominantly nonlinear. In all patients without worthwhile improvement after resective treatment, tissue producing strong nonlinear interrelations was left untouched by surgery. Our findings indicate that linear and nonlinear interrelations play fundamentally different roles in preictal intracranial EEG. Moreover, they suggest nonlinear signal interrelations to be a marker of epileptogenic tissue and not a characteristic of the mesiotemporal structures. Our results corroborate the network-based nature of epilepsy and suggest the application of network analysis to support the planning of resective epilepsy surgery.

Pathological high frequency oscillations associate with increased GABA synaptic activity in pediatric epilepsy surgery patients

Pathological high-frequency oscillations (HFOs), specifically fast ripples (FRs, >250 Hz), are pathognomonic of an active epileptogenic zone. However, the origin of FRs remains unknown. Here we explored the correlation between FRs recorded with intraoperative pre-resection electrocorticography (ECoG) and spontaneous synaptic activity recorded ex vivo from cortical tissue samples resected for the treatment of pharmacoresistant epilepsy. The cohort included 47 children (ages 0.22-9.99 yr) with focal cortical dysplasias (CD types I and II), tuberous sclerosis complex (TSC) and non-CD pathologies. Whole-cell patch clamp recordings were obtained from pyramidal neurons and interneurons in cortical regions that were positive or negative for pathological HFOs, defined as FR band oscillations (250-500 Hz) at ECoG. The frequency of spontaneous excitatory and inhibitory postsynaptic currents (sEPSCs and IPSCs, respectively) was compared between HFO+ and HFO- regions. Regardless of pathological substrate, regions positive for FRs displayed significantly increased frequencies of sIPSCs compared with regions negative for FRs. In contrast, the frequency of sEPSCs was similar in both regions. In about one third of cases (n = 17), pacemaker GABA synaptic activity (PGA) was observed. In the vast majority (n = 15), PGA occurred in HFO+ areas. Further, fast-spiking interneurons displayed signs of hyperexcitability exclusively in HFO+ areas. These results indicate that, in pediatric epilepsy patients, increased GABA synaptic activity is associated with interictal FRs in the epileptogenic zone and suggest an active role of GABAergic interneurons in the generation of pathological HFOs. Increased GABA synaptic activity could serve to dampen excessive excitability of cortical pyramidal neurons in the epileptogenic zone, but it could also promote neuronal network synchrony.

Prospective observational study: Fast ripple localization delineates the epileptogenic zone

OBJECTIVE

To investigate spatial correlation between interictal HFOs and neuroimaging abnormalities, and to determine if complete removal of prospectively identified interictal HFOs correlates with post-surgical seizure-freedom.

METHODS

Interictal fast ripples (FRs: 250-500 Hz) in 19 consecutive children with pharmacoresistant focal epilepsy who underwent extra-operative electrocorticography (ECoG) recording were prospectively analyzed. The interictal FRs were sampled at 2000 Hz and were visually identified during 10 min of slow wave sleep. Interictal FRs, MRI and FDG-PET were delineated on patient-specific reconstructed three-dimensional brain MRI.

RESULTS

Interictal FRs were observed in all patients except one. Thirteen out of 18 patients (72%) exhibited FRs beyond the extent of neuroimaging abnormalities. Fifteen of 19 children underwent resective surgery, and survival analysis with log-rank test demonstrated that complete resection of cortical sites showing interictal FRs correlated with longer post-operative seizure-freedom (p < 0.01). Complete resection of seizure onset zones (SOZ) also correlated with longer post-operative seizure-freedom (p = 0.01), yet complete resection of neuroimaging abnormalities did not (p = 0.43).

CONCLUSIONS

Prospective visual analysis of interictal FRs was feasible, and it seemed to accurately localize epileptogenic zones.

SIGNIFICANCE

Topological extent of epileptogenic region may exceed what is discernible by multimodal neuroimaging.

Multi-feature localization of epileptic foci from interictal, intracranial EEG

OBJECTIVE

When considering all patients with focal drug-resistant epilepsy, as high as 40-50% of patients suffer seizure recurrence after surgery. To achieve seizure freedom without side effects, accurate localization of the epileptogenic tissue is crucial before its resection. We investigate an automated, fast, objective mapping process that uses only interictal data.

METHODS

We propose a novel approach based on multiple iEEG features, which are used to train a support vector machine (SVM) model for classification of iEEG electrodes as normal or pathologic using 30 min of inter-ictal recording.

RESULTS

The tissue under the iEEG electrodes, classified as epileptogenic, was removed in 17/18 excellent outcome patients and was not entirely resected in 8/10 poor outcome patients. The overall best result was achieved in a subset of 9 excellent outcome patients with the area under the receiver operating curve = 0.95.

CONCLUSION

SVM models combining multiple iEEG features show better performance than algorithms using a single iEEG marker. Multiple iEEG and connectivity features in presurgical evaluation could improve epileptogenic tissue localization, which may improve surgical outcome and minimize risk of side effects.

SIGNIFICANCE

In this study, promising results were achieved in localization of epileptogenic regions by SVM models that combine multiple features from 30 min of inter-ictal iEEG recordings.

DC shifts, high frequency oscillations, ripples and fast ripples in relation to the seizure onset zone

Efforts to improve epilepsy surgery outcomes have led to increased interest in the study of electroencephalographic oscillations outside the conventional EEG bands. These include fast activity above the gamma band, known as high frequency oscillations (HFOs), and infraslow activity (ISA) below the delta band, sometimes referred to as direct current (DC) or ictal baseline shifts (IBS). HFOs in particular have been extensively studied as potential biomarkers for epileptogenic tissue in light of evidence showing that resection of brain tissue containing HFOs is associated with good surgical outcomes. Not all HFOs are conclusively pathological, however, as they can be recorded in nonepileptic tissue and induced by cognitive, visual, or motor tasks. Consequently, efforts to distinguish between pathological and physiological HFOs have identified several traits specific to pathological HFOs, such as coupling with interictal spikes, association with delta waves, and stereotypical morphologies. On the opposite end of the EEG spectrum, sub-delta oscillations have been shown to co-localize with the seizure onset zones (SOZ) and appear in a narrower spatial distribution than activity in the conventional EEG frequency bands. In this report, we review studies that implicate HFOs and ISA in ictogenesis and discuss current limitations such as inter-observer variability and poor standardization of recording techniques. Furthermore, we propose that HFOs and ISA should be analyzed in addition to activity in the conventional EEG band during intracranial presurgical EEG monitoring to identify the best possible surgical margin.

Localizing epileptogenic regions using high-frequency oscillations and machine learning

Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.

High-Frequency Activity During Stereotyped Low-Frequency Events Might Help to Identify the Seizure Onset Zone

Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy. Liu S, Gurses C, Sha Z, Quach MM, Sencer A, Bebek N, et al. Brain. 2018;141(3):713-730. doi:10.1093/brain/awx374. PMID: 29394328 . High-frequency oscillations in local field potentials recorded with intracranial electroencephalogram are putative biomarkers of seizure-onset zones in epileptic brain. However, localized 80- to 500-Hz oscillations can also be recorded from normal and nonepileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones that limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 patients with epilepsy and 5 control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent of the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step toward the translation of these signature events into valid clinical biomarkers.

Spatiotemporal dynamics of auditory and picture naming-related high-gamma modulations: A study of Japanese-speaking patients

OBJECTIVE

To characterize the spatiotemporal dynamics of auditory and picture naming-related cortical activation in Japanese-speaking patients.

METHODS

Ten patients were assigned auditory naming and picture naming tasks during extraoperative intracranial EEG recording in a tertiary epilepsy center. Time-frequency analysis determined at what electrode sites and at what time windows during each task the amplitude of high-gamma activity (65-95 Hz) was modulated.

RESULTS

The superior-temporal gyrus on each hemisphere showed high-gamma augmentation during sentence listening, whereas the left middle-temporal and inferior-frontal gyri showed high-gamma augmentation peaking around stimulus offset. Auditory naming-specific high-gamma augmentation was noted in the bilateral superior-temporal gyri as well as left frontal-parietal-temporal perisylvian network regions, whereas picture naming-specific augmentation was noted in the occipital-fusiform regions, bilaterally. The inferior pre- and postcentral gyri on each hemisphere showed modality-common high-gamma augmentation time-locked to overt responses.

CONCLUSIONS

The spatiotemporal dynamics of auditory and picture naming-related high-gamma augmentation in Japanese-speaking patients were qualitatively similar to those previously reported in studies of English-speaking patients.

SIGNIFICANCE

The cortical dynamics for auditory sentence recognition are at least partly shared by cohorts speaking two distinct languages. Multicenter studies regarding the clinical utility of high-gamma language mapping across Eastern and Western hemispheres may be feasible.

High-Frequency Oscillation Networks and Surgical Outcome in Adult Focal Epilepsy

OBJECTIVE

To investigate whether high-frequency oscillations (HFOs) show spatiotemporal propagation and assess the relevance of the earliest oscillations in relation to the seizure onset zone (SOZ) and postsurgical outcome.

METHODS

We retrospectively investigated the intracerebral electroencephalography (EEG) of patients who became seizure free after subsequent surgery. We marked HFOs during 1 hour of recordings. We calculated the time delay between pairs of channels as the median delay between their HFOs and constructed a time line of the delay of each channel with respect to the earliest channel (first source channel). A network was defined when a temporal order could be established among the channels based on the existence of statistically significant delays.

RESULTS

Fifteen patients with good surgical outcome were included. We found ripple networks in all patients, and fast ripple networks in 9. For ripples, first source channels were found in a higher proportion in the SOZ than the rest of the network channels (15 of 27 [56%] versus 93 of 262 [35%]; p = 0.04). For both ripples and fast ripples, first source channels were resected more often that the rest of the network channels (ripples: 13 of 27 [48%] versus 65 of 262 [25%]; p = 0.01; fast ripples: 8 of 9 [89%] versus 17 of 40 [43%]; p = 0.002); channels with the highest rates of ripples and fast ripples were resected in a similar proportion.

INTERPRETATION

These results demonstrate that interictal HFOs are organized in networks and indicate a possible need for the resection of first source channels. However, resecting them is not superior to resecting channels with highest rates of HFOs. Ann Neurol 2019;85:485-494.

Localization of the Epileptogenic Zone Using High Frequency Oscillations

For patients with drug-resistant focal epilepsy, surgery is the therapy of choice in order to achieve seizure freedom. Epilepsy surgery foremost requires the identification of the epileptogenic zone (EZ), defined as the brain area indispensable for seizure generation. The current gold standard for identification of the EZ is the seizure-onset zone (SOZ). The fact, however that surgical outcomes are unfavorable in 40-50% of well-selected patients, suggests that the SOZ is a suboptimal biomarker of the EZ, and that new biomarkers resulting in better postsurgical outcomes are needed. Research of recent years suggested that high-frequency oscillations (HFOs) are a promising biomarker of the EZ, with a potential to improve surgical success in patients with drug-resistant epilepsy without the need to record seizures. Nonetheless, in order to establish HFOs as a clinical biomarker, the following issues need to be addressed. First, evidence on HFOs as a clinically relevant biomarker stems predominantly from retrospective assessments with visual marking, leading to problems of reproducibility and reliability. Prospective assessments of the use of HFOs for surgery planning using automatic detection of HFOs are needed in order to determine their clinical value. Second, disentangling physiologic from pathologic HFOs is still an unsolved issue. Considering the appearance and the topographic location of presumed physiologic HFOs could be immanent for the interpretation of HFO findings in a clinical context. Third, recording HFOs non-invasively via scalp electroencephalography (EEG) and magnetoencephalography (MEG) is highly desirable, as it would provide us with the possibility to translate the use of HFOs to the scalp in a large number of patients. This article reviews the literature regarding these three issues. The first part of the article focuses on the clinical value of invasively recorded HFOs in localizing the EZ, the detection of HFOs, as well as their separation from physiologic HFOs. The second part of the article focuses on the current state of the literature regarding non-invasively recorded HFOs with emphasis on findings and technical considerations regarding their localization.