High frequency oscillations as markers of epileptogenic tissue - End of the party?

Passive and active markers of cortical excitability in epilepsy

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

Epileptic seizure focus detection from interictal electroencephalogram: a survey

Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.

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

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

Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom

In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80-250 Hz) and in the fast ripple (250-500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient's HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.

Graph theoretical measures of fast ripples support the epileptic network hypothesis

The epileptic network hypothesis and epileptogenic zone hypothesis are two theories of ictogenesis. The network hypothesis posits that coordinated activity among interconnected nodes produces seizures. The epileptogenic zone hypothesis posits that distinct regions are necessary and sufficient for seizure generation. High-frequency oscillations, and particularly fast ripples, are thought to be biomarkers of the epileptogenic zone. We sought to test these theories by comparing high-frequency oscillation rates and networks in surgical responders and non-responders, with no appreciable change in seizure frequency or severity, within a retrospective cohort of 48 patients implanted with stereo-EEG electrodes. We recorded inter-ictal activity during non-rapid eye movement sleep and semi-automatically detected and quantified high-frequency oscillations. Each electrode contact was localized in normalized coordinates. We found that the accuracy of seizure onset zone electrode contact classification using high-frequency oscillation rates was not significantly different in surgical responders and non-responders, suggesting that in non-responders the epileptogenic zone partially encompassed the seizure onset zone(s) (P > 0.05). We also found that in the responders, fast ripple on oscillations exhibited a higher spectral content in the seizure onset zone compared with the non-seizure onset zone (P < 1 × 10⁻⁵). By contrast, in the non-responders, fast ripple had a lower spectral content in the seizure onset zone (P < 1 × 10⁻⁵). We constructed two different networks of fast ripple with a spectral content >350 Hz. The first was a rate–distance network that multiplied the Euclidian distance between fast ripple-generating contacts by the average rate of fast ripple in the two contacts. The radius of the rate–distance network, which excluded seizure onset zone nodes, discriminated non-responders, including patients not offered resection or responsive neurostimulation due to diffuse multifocal onsets, with an accuracy of 0.77 [95% confidence interval (CI) 0.56–0.98]. The second fast ripple network was constructed using the mutual information between the timing of the events to measure functional connectivity. For most non-responders, this network had a longer characteristic path length, lower mean local efficiency in the non-seizure onset zone, and a higher nodal strength among non-seizure onset zone nodes relative to seizure onset zone nodes. The graphical theoretical measures from the rate–distance and mutual information networks of 22 non- responsive neurostimulation treated patients was used to train a support vector machine, which when tested on 13 distinct patients classified non-responders with an accuracy of 0.92 (95% CI 0.75–1). These results indicate patients who do not respond to surgery or those not selected for resection or responsive neurostimulation can be explained by the epileptic network hypothesis that is a decentralized network consisting of widely distributed, hyperexcitable fast ripple-generating nodes.

Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone

Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics.

Neurophysiology during epilepsy surgery

Intraoperative neuromonitoring (IONM) complements modern presurgical investigations by providing information about the epileptic focus as well as real-time identification of critical functional tissue and assessment of ongoing neural integrity during resective epilepsy surgery. This chapter summarizes current IONM methods for mapping the epileptic focus and for mapping and monitoring functionally important structures with direct brain stimulation and evoked potentials. These techniques include electrocorticography, computerized high-frequency oscillation mapping, single-pulse electric stimulation, cortical and subcortical motor evoked potentials, somatosensory evoked potentials, visual evoked potentials, and cortico-cortical evoked potentials. They may help to maximize epileptic tissue resection while avoiding permanent postoperative neurologic deficits.

Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions

To see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1-2 days later during non-rapid eye movement (REM) sleep. Ripples on oscillations (RonO, 80-250 Hz), ripples on spikes (RonS), sharp-spikes, fast RonO (fRonO, 250-600 Hz), and fast RonS (fRonS) were semi-automatically detected. HFO power and frequency were compared between the conditions using a generalized linear mixed-effects model. HFO rates were compared using a two-way repeated measures ANOVA with anesthesia type and SOZ as factors. A receiver-operating characteristic (ROC) curve analysis quantified seizure onset zone (SOZ) classification accuracy, and the scalar product was used to assess spatial reliability. Resection of contacts with the highest rate of events was compared with outcome. During sleep, all HFOs, except fRonO, were larger in amplitude compared to intraoperatively (p < 0.01). HFO frequency was also affected (p < 0.01). Anesthesia selection affected HFO and sharp-spike rates. In both conditions combined, sharp-spikes and all HFO subtypes were increased in the SOZ (p < 0.01). However, the increases were larger during the sleep recordings (p < 0.05). The area under the ROC curves for SOZ classification were significantly smaller for intraoperative sharp-spikes, fRonO, and fRonS rates (p < 0.05). HFOs and spikes were only significantly spatially reliable for a subset of the patients (p < 0.05). A failure to resect fRonO areas in the sleep recordings trended the most sensitive and accurate for predicting failure. In summary, HFO morphology is altered by anesthesia. Intraoperative SEEG recordings exhibit increased rates of HFOs in the SOZ, but their spatial distribution can differ from sleep recordings. Recording these biomarkers during non-REM sleep offers a more accurate delineation of the SOZ and possibly the epileptogenic zone.

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

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

Fast Ripples as a Biomarker of Epileptogenic Tuber in Tuberous Sclerosis Complex Patients Using Stereo-Electroencephalograph

Objectives: To evaluate the value of fast ripples (FRs) (200–500 Hz) recorded with stereo-electroencephalograph (SEEG) in the localization of epileptogenic tubers in patients with tuberous sclerosis complex (TSC).

Methods: Seventeen TSC patients who underwent preoperative SEEG examination and resective epilepsy surgery were retrospectively enrolled. They were divided into two groups according to the seizure control at 1-year postoperative follow-up. The occurrence frequencies of FRs were automatically counted, and the FR rate was calculated. The high FR rate was defined as FR rate ≧0.5. According to different positions, the contacts’ locations were divided into three groups: inner of the tubers, the junction region of the tubers, and out of the tubers. The influence factors of postoperative seizure freedom were also analyzed.

Results: Twelve patients reached postoperative seizure freedom at 1-year follow-up. In total, FRs were found in 24.2% of the contacts and 67.1% of the tubers in all assessed patients. There were 47 high FR rate contacts localized in the junction region of the tubers, which was 62.7% of the 75 high FR rate contacts in total and was 8.4% of the total 561 contacts localized in the junction region of the tubers. Total removal of epileptogenic tubers and total resection of the high FR rate tubers/contacts were associated with postoperative seizure freedom (P < 0.05).

Conclusion: FRs could be extensively detected in TSC patients using SEEG, and high FR rate contacts were mostly localized in the junction region of the epileptogenic tuber, which could aid in the localization of epileptogenic tubers.

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

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

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

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

Scalp high-frequency oscillation rates are higher in younger children

High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the paediatric population. We prospectively recorded whole-night scalp EEG in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80-250 Hz) in bipolar channels. Children < 7 years had higher high-frequency oscillation rates (P = 0.021) when compared with older children. The median test-retest reliability of high-frequency oscillation rates reached 100% (iqr 50) for a data interval duration of 10 min. Scalp high-frequency oscillation frequency decreased with age (r = -0.558, P = 0.002), whereas scalp high-frequency oscillation duration and amplitude were unaffected. The signal-to-noise ratio improved with age (r = 0.37, P = 0.048), and the background ripple band activity decreased with age (r = -0.463, P = 0.011). We characterize the relationship of scalp high-frequency oscillation features and age in paediatric patients. EEG intervals of ≥ 10 min duration are required for reliable measurements of high-frequency oscillation rates. This study is a further step towards establishing scalp high-frequency oscillations as a valid epileptogenicity biomarker in this vulnerable age group.

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

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

Seizures in the neonate: A review of etiologies and outcomes

Neonatal seizures occur in their majority in close temporal relation to an acute brain injury or systemic insult, and are accordingly defined as acute symptomatic or provoked seizures. However less frequently, unprovoked seizures may also present in the neonatal period as secondary to structural brain abnormalities, thus corresponding to structural epilepsies, or to genetic conditions, thus corresponding to genetic epilepsies. Unprovoked neonatal seizures should be thus considered as the clinical manifestation of early onset structural or genetic epilepsies that often have the characteristics of early onset epileptic encephalopathies. In this review, we address the conundrum of neonatal seizures including acute symptomatic, remote symptomatic, provoked, and unprovoked seizures, evolving to post-neonatal epilepsies, and neonatal onset epilepsies. The different clinical scenarios involving neonatal seizures, each with their distinct post-neonatal evolution are presented. The structural and functional impact of neonatal seizures on brain development and the concept of secondary epileptogenesis, with or without a following latent period after the acute seizures, are addressed. Finally, we underline the need for an early differential diagnosis between an acute symptomatic seizure and an unprovoked seizure, since it is associated with fundamental differences in clinical evolution. These are crucial aspects for neonatal management, counselling and prognostication. In view of the above aspects, we provide an outlook on future strategies and potential lines of research in this field.

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.