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

Mesial-Temporal Epileptic Ripples Correlate With Verbal Memory Impairment

Rationale

High frequency oscillations (HFO; ripples = 80–200, fast ripples 200–500 Hz) are promising epileptic biomarkers in patients with epilepsy. However, especially in temporal epilepsies differentiation of epileptic and physiological HFO activity still remains a challenge. Physiological sleep-spindle-ripple formations are known to play a role in slow-wave-sleep memory consolidation. This study aimed to find out if higher rates of mesial-temporal spindle-ripples correlate with good memory performance in epilepsy patients and if surgical removal of spindle-ripple-generating brain tissue correlates with a decline in memory performance. In contrast, we hypothesized that higher rates of overall ripples or ripples associated with interictal epileptic spikes correlate with poor memory performance.

Methods

Patients with epilepsy implanted with electrodes in mesial-temporal structures, neuropsychological memory testing and subsequent epilepsy surgery were included. Ripples and epileptic spikes were automatically detected in intracranial EEG and sleep-spindles in scalp EEG. The coupling of ripples to spindles was automatically analyzed. Mesial-temporal spindle-ripple rates in the speech-dominant-hemisphere (left in all patients) were correlated with verbal memory test results, whereas ripple rates in the non-speech-dominant hemisphere were correlated with non-verbal memory test performance, using Spearman correlation).

Results

Intracranial EEG and memory test results from 25 patients could be included. All ripple rates were significantly higher in seizure onset zone channels (p < 0.001). Patients with pre-surgical verbal memory impairment had significantly higher overall ripple rates in left mesial-temporal channels than patients with intact verbal memory (Mann–Whitney-U-Test: p = 0.039). Spearman correlations showed highly significant negative correlations of the pre-surgical verbal memory performance with left mesial-temporal spike associated ripples (r_s = −0.458; p = 0.007) and overall ripples (r_s = −0.475; p = 0.006). All three ripple types in right-sided mesial-temporal channels did not correlate with pre-surgical nonverbal memory. No correlation for the difference between post- and pre-surgical memory and pre-surgical spindle-ripple rates was seen in patients with left-sided temporal or mesial-temporal surgery.

Discussion

This study fails to establish a clear link between memory performance and spindle ripples. This highly suggests that spindle-ripples are only a small portion of physiological ripples contributing to memory performance. More importantly, this study indicates that spindle-ripples do not necessarily compromise the predictive value of ripples in patients with temporal epilepsy. The majority of ripples were clearly linked to areas with poor memory function.

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.

Differential Electrographic Signatures Generated by Mechanistically-Diverse Seizurogenic Compounds in the Larval Zebrafish Brain

We assessed similarities and differences in the electrographic signatures of local field potentials (LFPs) evoked by different pharmacological agents in zebrafish larvae. We then compared and contrasted these characteristics with what is known from electrophysiological studies of seizures and epilepsy in mammals, including humans. Ultimately, our aim was to phenotype neurophysiological features of drug-induced seizures in larval zebrafish for expanding knowledge on the translational potential of this valuable alternative to mammalian models. LFPs were recorded from the midbrain of 4-d-old zebrafish larvae exposed to a pharmacologically diverse panel of seizurogenic compounds, and the outputs of these recordings were assessed using frequency domain analysis. This included analysis of changes occurring within various spectral frequency bands of relevance to mammalian CNS circuit pathophysiology. From these analyses, there were clear differences in the frequency spectra of drug-exposed LFPs, relative to controls, many of which shared notable similarities with the signatures exhibited by mammalian CNS circuits. These similarities included the presence of specific frequency components comparable to those observed in mammalian studies of seizures and epilepsy. Collectively, the data presented provide important information to support the value of larval zebrafish as an alternative model for the study of seizures and epilepsy. These data also provide further insight into the electrophysiological characteristics of seizures generated in nonmammalian species by the action of neuroactive drugs.

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.

Characterizing Brain Signals for Epileptic Pre-ictal Signal Classification

Epilepsy is a kind of neurological disorder characterized by recurrent epileptic seizures. While it is crucial to characterize pre-ictal brain electrical activities, the problem to this day still remains computationally challenging. Using brain signal acquisition and advances in deep learning technology, we aim to classify pre-ictal signals and characterize the brain waveforms of patients with epilepsy during the pre-ictal period. We develop a novel machine learning model called Pre-ictal Signal Classification (PiSC) for pre-ictal signal classification and for identifying brain waveform patterns critical for seizure onset early detection. In PiSC, a unique preprocessing procedure is developed to convert the stereo-electroencephalography (sEEG) signals to data blocks ready for pre-ictal signal classification. Also, a novel deep learning framework is developed to integrate deep neural networks and meta-learning to effectively mitigate patient-to-patient variances as well as fine-tuning a trained classification model for new patients. The unique network architecture ensures model stability and generalization in sEEG data modeling. The experimental results on a real-world patient dataset show that PiSC improved the accuracy and F1 score by 10% compared with the existing models. Two types of sEEG patterns were discovered to be associated with seizure development in nocturnal epileptic patients.

More Than Spikes: On the Added Value of Non-linear Intracranial EEG Analysis for Surgery Planning in Temporal Lobe Epilepsy

Epilepsy surgery can be a very effective therapy in medication refractory patients. During patient evaluation intracranial EEG is analyzed by clinical experts to identify the brain tissue generating epileptiform events. Quantitative EEG analysis increasingly complements this approach in research settings, but not yet in clinical routine. We investigate the correspondence between epileptiform events and a specific quantitative EEG marker. We analyzed 99 preictal epochs of multichannel intracranial EEG of 40 patients with mixed etiologies. Time and channel of occurrence of epileptiform events (spikes, slow waves, sharp waves, fast oscillations) were annotated by a human expert and non-linear excess interrelations were calculated as a quantitative EEG marker. We assessed whether the visually identified preictal events predicted channels that belonged to the seizure onset zone, that were later resected or that showed strong non-linear interrelations. We also investigated whether the seizure onset zone or the resection were predicted by channels with strong non-linear interrelations. In patients with temporal lobe epilepsy (32 of 40), epileptic spikes and the seizure onset zone predicted the resected brain tissue much better in patients with favorable seizure control after surgery than in unfavorable outcomes. Beyond that, our analysis did not reveal any significant associations with epileptiform EEG events. Specifically, none of the epileptiform event types did predict non-linear interrelations. In contrast, channels with strong non-linear excess EEG interrelations predicted the resected channels better in patients with temporal lobe epilepsy and favorable outcome. Also in the small number of patients with seizure onset in the frontal and parietal lobes, no association between epileptiform events and channels with strong non-linear excess EEG interrelations was detectable. In contrast to patients with temporal seizure onset, EEG channels with strong non-linear excess interrelations did neither predict the seizure onset zone nor the resection of these patients or allow separation between patients with favorable and unfavorable seizure control. Our study indicates that non-linear excess EEG interrelations are not strictly associated with epileptiform events, which are one key concept of current clinical EEG assessment. Rather, they may provide information relevant for surgery planning in temporal lobe epilepsy. Our study suggests to incorporate quantitative EEG analysis in the workup of clinical cases. We make the EEG epochs and expert annotations publicly available in anonymized form to foster similar analyses for other quantitative EEG methods.

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.

Spatio-temporal spike dynamics predict surgical outcome in adult focal epilepsy

OBJECTIVE

We hypothesized that spatio-temporal dynamics of interictal spikes reflect the extent and stability of epileptic sources and determine surgical outcome.

METHODS

We studied 30 consecutive patients (14 good outcome). Spikes were detected in prolonged stereo-electroencephalography recordings. We quantified the spatio-temporal dynamics of spikes using the variance of the spike rate, line length and skewness of the spike distribution, and related these features to outcome. We built a logistic regression model, and compared its performance to traditional markers.

RESULTS

Good outcome patients had more dominant and stable sources than poor outcome patients as expressed by a higher variance of spike rates, a lower variance of line length, and a lower variance of positive skewness (ps < 0.05). The outcome was correctly predicted in 80% of patients. This was better or non-inferior to predictions based on a focal lesion (p = 0.016), focal seizure-onset zone, or complete resection (ps > 0.05). In the five patients where traditional markers failed, spike distribution predicted the outcome correctly. The best results were achieved by 18-h periods or longer.

CONCLUSIONS

Analysis of spike dynamics shows that surgery outcome depends on strong, single and stable sources.

SIGNIFICANCE

Our quantitative method has the potential to be a reliable predictor of surgical outcome.

Correcting for physiological ripples improves epileptic focus identification and outcome prediction

Objective

The integration of high‐frequency oscillations (HFOs; ripples [80–250 Hz], fast ripples [250–500 Hz]) in epilepsy evaluation is hampered by physiological HFOs, which cannot be reliably differentiated from pathological HFOs. We evaluated whether defining abnormal HFO rates by statistical comparison to region‐specific physiological HFO rates observed in the healthy brain improves identification of the epileptic focus and surgical outcome prediction.

Methods

We detected HFOs in 151 consecutive patients who underwent stereo‐electroencephalography and subsequent resective epilepsy surgery at two tertiary epilepsy centers. We compared how HFOs identified the resection cavity and predicted seizure‐free outcome using two thresholds from the literature (HFO rate > 1/min; 50% of the total number of a patient's HFOs) and three thresholds based on normative rates from the Montreal Neurological Institute Open iEEG Atlas (https://mni‐open‐ieegatlas.research.mcgill.ca/): global Atlas threshold, regional Atlas threshold, and regional + 10% threshold after regional Atlas correction.

Results

Using ripples, the regional + 10% threshold performed best for focus identification (77.3% accuracy, 27% sensitivity, 97.1% specificity, 80.6% positive predictive value [PPV], 78.2% negative predictive value [NPV]) and outcome prediction (69.5% accuracy, 58.6% sensitivity, 76.3% specificity, 60.7% PPV, 74.7% NPV). This was an improvement for focus identification (+1.1% accuracy, +17.0% PPV; p < .001) and outcome prediction (+12.0% sensitivity, +1.0% PPV; p = .05) compared to the 50% threshold. The improvement was particularly marked for foci in cortex, where physiological ripples are frequent (outcome: +35.3% sensitivity, +5.3% PPV; p = .014). In these cases, the regional + 10% threshold outperformed fast ripple rate > 1/min (+3.6% accuracy, +26.5% sensitivity, +21.6% PPV; p < .001) and seizure onset zone (+13.5% accuracy, +29.4% sensitivity, +17.0% PPV; p < .05–.01) for outcome prediction. Normalization did not improve the performance of fast ripples.

Significance

Defining abnormal HFO rates by statistical comparison to rates in healthy tissue overcomes an important weakness in the clinical use of ripples. It improves focus identification and outcome prediction compared to standard HFO measures, increasing their clinical applicability.

Drug-resistant epilepsy and the hypothesis of intrinsic severity: What about the high-frequency oscillations?

Drug‐resistant epilepsy (DRE) affects approximately one‐third of the patients with epilepsy. Based on experimental findings from animal models and brain tissue from patients with DRE, different hypotheses have been proposed to explain the cause(s) of drug resistance. One is the intrinsic severity hypothesis that posits that drug resistance is an inherent property of epilepsy related to disease severity. Seizure frequency is one measure of epilepsy severity, but frequency alone is an incomplete measure of severity and does not fully explain basic research and clinical studies on drug resistance; thus, other measures of epilepsy severity are needed. One such measure could be pathological high‐frequency oscillations (HFOs), which are believed to reflect the neuronal disturbances responsible for the development of epilepsy and the generation of spontaneous seizures. In this manuscript, we will briefly review the intrinsic severity hypothesis, describe basic and clinical research on HFOs in the epileptic brain, and based on this evidence discuss whether HFOs could be a clinical measure of epilepsy severity. Understanding the mechanisms of DRE is critical for producing breakthroughs in the development and testing of novel strategies for treatment.

Interictal spikes with and without high-frequency oscillation have different single-neuron correlates

Interictal epileptiform discharges (IEDs) are a widely used biomarker in patients with epilepsy but lack specificity. It has been proposed that there are truly epileptogenic and less pathological or even protective IEDs. Recent studies suggest that highly pathological IEDs are characterized by high-frequency oscillations (HFOs). Here, we aimed to dissect these 'HFO-IEDs' at the single-neuron level, hypothesizing that the underlying mechanisms are distinct from 'non-HFO-IEDs'. Analysing hybrid depth electrode recordings from patients with temporal lobe epilepsy, we found that single-unit firing rates were higher in HFO- than in non-HFO-IEDs. HFO-IEDs were characterized by a pronounced pre-peak increase in firing, which coincided with the preferential occurrence of HFOs, whereas in non-HFO-IEDs, there was only a mild pre-peak increase followed by a post-peak suppression. Comparing each unit's firing during HFO-IEDs to its baseline activity, we found many neurons with a significant increase during the HFO component or ascending part, but almost none with a decrease. No such imbalance was observed during non-HFO-IEDs. Finally, comparing each unit's firing directly between HFO- and non-HFO-IEDs, we found that most cells had higher rates during HFO-IEDs and, moreover, identified a distinct subset of neurons with a significant preference for this IED subtype. In summary, our study reveals that HFO- and non-HFO-IEDs have different single-unit correlates. In HFO-IEDs, many neurons are moderately activated, and some participate selectively, suggesting that both types of increased firing contribute to highly pathological IEDs.

The Onset of Interictal Spike-Related Ripples Facilitates Detection of the Epileptogenic Zone

Objective: Accurate estimation of the epileptogenic zone (EZ) is essential for favorable outcomes in epilepsy surgery. Conventional ictal electrocorticography (ECoG) onset is generally used to detect the EZ but is insufficient in achieving seizure-free outcomes. By contrast, high-frequency oscillations (HFOs) could be useful markers of the EZ. Hence, we aimed to detect the EZ using interictal spikes and investigated whether the onset area of interictal spike-related HFOs was within the EZ. Methods: The EZ is considered to be included in the resection area among patients with seizure-free outcomes after surgery. Using a complex demodulation technique, we developed a method to determine the onset channels of interictal spike-related ripples (HFOs of 80-200 Hz) and investigated whether they are within the resection area. Results: We retrospectively examined 12 serial patients who achieved seizure-free status after focal resection surgery. Using the method that we developed, we determined the onset channels of interictal spike-related ripples and found that for all 12 patients, they were among the resection channels. The onset frequencies of ripples were in the range of 80-150 Hz. However, the ictal onset channels (evaluated based on ictal ECoG patterns) and ripple onset channels coincided in only 3 of 12 patients. Conclusions: Determining the onset area of interictal spike-related ripples could facilitate EZ estimation. This simple method that utilizes interictal ECoG may aid in preoperative evaluation and improve epilepsy surgery outcomes.

High-frequency oscillations detected by electroencephalography as biomarkers to evaluate treatment outcome, mirror pathological severity and predict susceptibility to epilepsy

High-frequency oscillations (HFOs) in the electroencephalography (EEG) have been extensively investigated as a potential biomarker of epileptogenic zones. The understanding of the role of HFOs in epilepsy has been advanced considerably over the past decade, and the use of scalp EEG facilitates recordings of HFOs. HFOs were initially applied in large scale in epilepsy surgery and are now being utilized in other applications. In this review, we summarize applications of HFOs in 3 subtopics: (1) HFOs as biomarkers to evaluate epilepsy treatment outcome; (2) HFOs as biomarkers to measure seizure propensity; (3) HFOs as biomarkers to reflect the pathological severity of epilepsy. Nevertheless, knowledge regarding the above clinical applications of HFOs remains limited at present. Further validation through prospective studies is required for its reliable application in the clinical management of individual epileptic patients.

Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach

Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations). To address these issues, we constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model. The ‘purification power’ of deep learning is then used to automatically relabel the high-frequency oscillations to distill epileptogenic high-frequency oscillations. Using 12 958 annotated high-frequency oscillation events from 19 patients, the model achieved 96.3% accuracy on artefact detection (F1 score = 96.8%) and 86.5% accuracy on classifying high-frequency oscillations with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the algorithm trained from 84 602 high-frequency oscillation events from nine patients who achieved seizure-freedom after resection, the majority of such discovered epileptogenic high-frequency oscillations were found to be ones with spikes (78.6%, P < 0.001). While the resection ratio of detected high-frequency oscillations (number of resected events/number of detected events) did not correlate significantly with post-operative seizure freedom (the area under the curve = 0.76, P = 0.06), the resection ratio of epileptogenic high-frequency oscillations positively correlated with post-operative seizure freedom (the area under the curve = 0.87, P = 0.01). We discovered that epileptogenic high-frequency oscillations had a higher signal intensity associated with ripple (80–250 Hz) and fast ripple (250–500 Hz) bands at the high-frequency oscillation onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-epileptogenic high-frequency oscillations. We then designed perturbations on the input of the trained model for non-epileptogenic high-frequency oscillations to determine the model’s decision-making logic. The model confidence significantly increased towards epileptogenic high-frequency oscillations by the artificial introduction of the inverted T-shaped signal template (mean probability increase: 0.285, P < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, P < 0.001). With this deep learning-based framework, we reliably replicated high-frequency oscillation classification tasks by human experts. Using a reverse engineering technique, we distinguished epileptogenic high-frequency oscillations from others and identified its salient features that aligned with current knowledge.

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.

Ultra-fast oscillation detection in EEG signal from deep-brain microelectrodes

For the last decades, ripples 80-200Hz (R)and fast ripples 200-500Hz (FR) were intensively studied as biomarkers of the epileptogenic zone (EZ). Recently, Very fast ripples 500-1000Hz (VFR) and ultra-fast ripples 1000-2000Hz (UFR) recorded using standard clinical macro electrodes have been shown to be more specific for EZ. High-sampled microelectrode recordings can bring new insights into this phenomenon of high frequency, multiunit activity. Unfortunately, visual detection of such events is extremely time consuming and unreliable. Here we present a detector of ultra-fast oscillations (UFO, >1kHz). In an example of two patients, we detected 951 UFOs which were more frequent in epileptic (8.6/min) vs. non-epileptic hippocampus (1.3/min). Our detection method can serve as a tool for exploring extremely high frequency events from microelectrode recordings.

Intracranial EEG Biomarkers for Seizure Lateralization in Rapidly-Bisynchronous Epilepsy After Laser Corpus Callosotomy

Objective: It has been asserted that high-frequency analysis of intracranial EEG (iEEG) data may yield information useful in localizing epileptogenic foci. Methods: We tested whether proposed biomarkers could predict lateralization based on iEEG data collected prior to corpus callosotomy (CC) in three patients with bisynchronous epilepsy, whose seizures lateralized definitively post-CC. Lateralization data derived from algorithmically-computed ictal phase-locked high gamma (PLHG), high gamma amplitude (HGA), and low-frequency (filtered) line length (LFLL), as well as interictal high-frequency oscillation (HFO) and interictal epileptiform discharge (IED) rate metrics were compared against ground-truth lateralization from post-CC ictal iEEG. Results: Pre-CC unilateral IEDs were more frequent on the more-pathologic side in all subjects. HFO rate predicted lateralization in one subject, but was sensitive to detection threshold. On pre-CC data, no ictal metric showed better predictive power than any other. All post-corpus callosotomy seizures lateralized to the pathological hemisphere using PLHG, HGA, and LFLL metrics. Conclusions: While quantitative metrics of IED rate and ictal HGA, PHLG, and LFLL all accurately lateralize based on post-CC iEEG, only IED rate consistently did so based on pre-CC data. Significance: Quantitative analysis of IEDs may be useful in lateralizing seizure pathology. More work is needed to develop reliable techniques for high-frequency iEEG analysis.

Low frequency novel interictal EEG biomarker for localizing seizures and predicting outcomes

Localizing hyperexcitable brain tissue to treat focal seizures remains challenging. We want to identify the seizure onset zone from interictal EEG biomarkers. We hypothesize that a combination of interictal EEG biomarkers, including a novel low frequency marker, can predict mesial temporal involvement and can assist in prognosis related to surgical resections. Interictal direct current wide bandwidth invasive EEG recordings from 83 patients implanted with 5111 electrodes were retrospectively studied. Logistic regression was used to classify electrodes and patient outcomes. A feed-forward neural network was implemented to understand putative mechanisms. Interictal infraslow frequency EEG activity was decreased for seizure onset zone electrodes while faster frequencies such as delta (2-4 Hz) and beta-gamma (20-50 Hz) activity were increased. These spectral changes comprised a novel interictal EEG biomarker that was significantly increased for mesial temporal seizure onset zone electrodes compared to non-seizure onset zone electrodes. Interictal EEG biomarkers correctly classified mesial temporal seizure onset zone electrodes with a specificity of 87% and positive predictive value of 80%. These interictal EEG biomarkers also correctly classified patient outcomes after surgical resection with a specificity of 91% and positive predictive value of 87%. Interictal infraslow EEG activity is decreased near the seizure onset zone while higher frequency power is increased, which may suggest distinct underlying physiologic mechanisms. Narrowband interictal EEG power bands provide information about the seizure onset zone and can help predict mesial temporal involvement in seizure onset. Narrowband interictal EEG power bands may be less useful for predictions related to non-mesial temporal electrodes. Together with interictal epileptiform discharges and high-frequency oscillations, these interictal biomarkers may provide prognostic information prior to surgical resection. Computational modelling suggests changes in neural adaptation may be related to the observed low frequency power changes.

Localizing the Epileptogenic Zone with Novel Biomarkers

Several noninvasive methods, such as high-density EEG or magnetoencephalography, are currently used to delineate the epileptogenic zone (EZ) during the presurgical evaluation of patients with drug resistant epilepsy (DRE). Yet, none of these methods can reliably identify the EZ by their own. In most cases a multimodal approach is needed. Challenging cases often require the implantation of intracranial electrodes, either through stereo-taxic EEG or electro-corticography. Recently, a growing body of literature introduces novel biomarkers of epilepsy that can be used for analyzing both invasive as well as noninvasive electrophysiological data. Some of these biomarkers are able to delineate the EZ with high precision, augment the presurgical evaluation, and predict the surgical outcome of patients with DRE undergoing surgery. However, the use of these epilepsy biomarkers in clinical practice is limited. Here, we summarize and discuss the latest technological advances in the presurgical neurophysiological evaluation of children with DRE with emphasis on electric and magnetic source imaging, high frequency oscillations, and functional connectivity.

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.

Leveraging electrophysiologic correlates of word encoding to map seizure onset zone in focal epilepsy: Task-dependent changes in epileptiform activity, spectral features, and functional connectivity

OBJECTIVE

Verbal memory dysfunction is common in focal, drug-resistant epilepsy (DRE). Unfortunately, surgical removal of seizure-generating brain tissue can be associated with further memory decline. Therefore, localization of both the circuits generating seizures and those underlying cognitive functions is critical in presurgical evaluations for patients who may be candidates for resective surgery. We used intracranial electroencephalographic (iEEG) recordings during a verbal memory task to investigate word encoding in focal epilepsy. We hypothesized that engagement in a memory task would exaggerate local iEEG feature differences between the seizure onset zone (SOZ) and neighboring tissue as compared to wakeful rest ("nontask").

METHODS

Ten participants undergoing presurgical iEEG evaluation for DRE performed a free recall verbal memory task. We evaluated three iEEG features in SOZ and non-SOZ electrodes during successful word encoding and compared them with nontask recordings: interictal epileptiform spike (IES) rates, power in band (PIB), and relative entropy (REN; a functional connectivity measure).

RESULTS

We found a complex pattern of PIB and REN changes in SOZ and non-SOZ electrodes during successful word encoding compared to nontask. Successful word encoding was associated with a reduction in local electrographic functional connectivity (increased REN), which was most exaggerated in temporal lobe SOZ. The IES rates were reduced during task, but only in the non-SOZ electrodes. Compared with nontask, REN features during task yielded marginal improvements in SOZ classification.

SIGNIFICANCE

Previous studies have supported REN as a biomarker for epileptic brain. We show that REN differences between SOZ and non-SOZ are enhanced during a verbal memory task. We also show that IESs are reduced during task in non-SOZ, but not in SOZ. These findings support the hypothesis that SOZ and non-SOZ respond differently to task and warrant further exploration into the use of cognitive tasks to identify functioning memory circuits and localize SOZ.

Improving the ripple classification in focal pediatric epilepsy: identifying pathological high-frequency oscillations by Gaussian mixture model clustering

Objective. High-frequency oscillations (HFOs) have emerged as a promising clinical biomarker for presurgical evaluation in childhood epilepsy. HFOs are commonly classified in stereo-encephalography as ripples (80-200 Hz) and fast ripples (200-500 Hz). Ripples are less specific and not so directly associated with epileptogenic activity because of their physiological and pathological origin. The aim of this paper is to distinguish HFOs in the ripple band and to improve the evaluation of the epileptogenic zone (EZ).Approach. This study constitutes a novel modeling approach evaluated in ten patients from Sant Joan de Deu Pediatric Hospital (Barcelona, Spain), with clearly-defined seizure onset zones (SOZ) during presurgical evaluation. A subject-by-subject basis analysis is proposed: a probabilistic Gaussian mixture model (GMM) based on the combination of specific ripple features is applied for estimating physiological and pathological ripple subpopulations.Main Results. Clear pathological and physiological ripples are identified. Features differ considerably among patients showing within-subject variability, suggesting that individual models are more appropriate than a traditional whole-population approach. The difference in rates inside and outside the SOZ for pathological ripples is significantly higher than when considering all the ripples. These significant differences also appear in signal segments without epileptiform activity. Pathological ripple rates show a sharp decline from SOZ to non-SOZ contacts and a gradual decrease with distance.Significance. This novel individual GMM approach improves ripple classification and helps to refine the delineation of the EZ, as well as being appropriate to investigate the interaction of epileptogenic and propagation networks.

Microscale dynamics of electrophysiological markers of epilepsy

OBJECTIVE

Interictal discharges (IIDs) and high frequency oscillations (HFOs) are established neurophysiologic biomarkers of epilepsy, while microseizures are less well studied. We used custom poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) microelectrodes to better understand these markers' microscale spatial dynamics.

METHODS

Electrodes with spatial resolution down to 50 µm were used to record intraoperatively in 30 subjects. IIDs' degree of spread and spatiotemporal paths were generated by peak-tracking followed by clustering. Repeating HFO patterns were delineated by clustering similar time windows. Multi-unit activity (MUA) was analyzed in relation to IID and HFO timing.

RESULTS

We detected IIDs encompassing the entire array in 93% of subjects, while localized IIDs, observed across < 50% of channels, were seen in 53%. IIDs traveled along specific paths. HFOs appeared in small, repeated spatiotemporal patterns. Finally, we identified microseizure events that spanned 50-100 µm. HFOs covaried with MUA, but not with IIDs.

CONCLUSIONS

Overall, these data suggest that irritable cortex micro-domains may form part of an underlying pathologic architecture which could contribute to the seizure network.

SIGNIFICANCE

These results, supporting the possibility that epileptogenic cortex comprises a mosaic of irritable domains, suggests that microscale approaches might be an important perspective in devising novel seizure control therapies.

Changes in epileptogenicity biomarkers after stereotactic thermocoagulation

OBJECTIVE

Stereo-electroencephalography (SEEG)-guided radiofrequency thermocoagulation (RF-TC) aims at modifying epileptogenic networks to reduce seizure frequency. High-frequency oscillations (HFOs), spikes, and cross-rate are quantifiable epileptogenic biomarkers. In this study, we sought to evaluate, using SEEG signals recorded before and after thermocoagulation, whether a variation in these markers is related to the therapeutic effect of this procedure and to the outcome of surgery.

METHODS

Interictal segments of SEEG signals were analyzed in 38 patients during presurgical evaluation. We used an automatized method to quantify the rate of spikes, rate of HFOs, and cross-rate (a measure combining spikes and HFOs) before and after thermocoagulation. We analyzed the differences both at an individual level with a surrogate approach and at a group level with analysis of variance. We then evaluated the correlation between these variations and the clinical response to RF-TC and to subsequent resective surgery.

RESULTS

After thermocoagulation, 19 patients showed a clinical improvement. At the individual level, clinically improved patients more frequently had a reduction in spikes and cross-rate in the epileptogenic zone than patients without clinical improvement (p = .002, p = .02). At a group level, there was a greater decrease of HFOs in epileptogenic and thermocoagulated zones in patients with clinical improvement (p < .05) compared to those with no clinical benefit. Eventually, a significant decrease of all the markers after RF-TC was found in patients with a favorable outcome of resective surgery (spikes, p = .026; HFOs, p = .03; cross-rate, p = .03).

SIGNIFICANCE

Quantified changes in the rate of spikes, rate of HFOs, and cross-rate can be observed after thermocoagulation, and the reduction of these markers correlates with a favorable clinical outcome after RF-TC and with successful resective surgery. This may suggest that interictal biomarker modifications after RF-TC can be clinically used to predict the effectiveness of the thermocoagulation procedure and the outcome of resective surgery.

MEG detection of high frequency oscillations and intracranial-EEG validation in pediatric epilepsy surgery

OBJECTIVE

To assess the feasibility of automatically detecting high frequency oscillations (HFOs) in magnetoencephalography (MEG) recordings in a group of ten paediatric epilepsy surgery patients who had undergone intracranial electroencephalography (iEEG).

METHODS

A beamforming source-analysis method was used to construct virtual sensors and an automatic algorithm was applied to detect HFOs (80-250 Hz). We evaluated the concordance of MEG findings with the sources of iEEG HFOs, the clinically defined seizure onset zone (SOZ), the location of resected brain structures, and with post-operative outcome.

RESULTS

In 8/9 patients there was good concordance between the sources of MEG HFOs and iEEG HFOs and the SOZ. Significantly more HFOs were detected in iEEG relative to MEG t(71) = 2.85, p < .05. There was good concordance between sources of MEG HFOs and the resected area in patients with good and poor outcome, however HFOs were also detected outside of the resected area in patients with poor outcome.

CONCLUSION

Our findings demonstrate the feasibility of automatically detecting HFOs non-invasively in MEG recordings in paediatric patients, and confirm compatibility of results with invasive recordings.

SIGNIFICANCE

This approach provides support for the non-invasive detection of HFOs to aid surgical planning and potentially reduce the need for invasive monitoring, which is pertinent to paediatric patients.

Ictal Occurrence of High-Frequency Oscillations Correlates With Seizure Severity in a Rat Model of Temporal Lobe Epilepsy

High-frequency oscillations (HFOs, ripples 80–250 Hz, fast ripples 250–500 Hz) are biomarkers of epileptic tissue. They are most commonly observed over areas generating seizures and increase in occurrence during the ictal compared to the interictal period. It has been hypothesized that their rate correlates with the severity of epilepsy and seizure in affected individuals. In the present study, it was aimed to investigate whether the HFO count mirrors the observed behavioral seizure severity using a kainate rat model for temporal lobe epilepsy. Seizures were selected during the chronic epilepsy phase of this model and classified by behavioral severity according to the Racine scale. Seizures with Racine scale 5&6 were considered generalized and severe. HFOs were marked in 24 seizures during a preictal, ictal, and postictal EEG segment. The duration covered by the HFO during these different segments was analyzed and compared between mild and severe seizures. HFOs were significantly increased during ictal periods (p < 0.001) and significantly decreased during postictal periods (p < 0.03) compared to the ictal segment. Ictal ripples (p = 0.04) as well as fast ripples (p = 0.02) were significantly higher in severe seizures compared to mild seizures. The present study demonstrates that ictal HFO occurrence mirrors seizure severity in a chronic focal epilepsy model in rats. This is similar to recent observations in patients with refractory mesio-temporal lobe epilepsy. Moreover, postictal HFO decrease might reflect postictal inhibition of epileptic activity. Overall results provide additional evidence that HFOs can be used as biomarkers for measuring seizure severity in epilepsy.

Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram

BACKGROUND

A reliable biomarker to identify cortical tissue responsible for generating epileptic seizures is required to guide prognosis and treatment in epilepsy. Combined spike ripple events are a promising biomarker for epileptogenic tissue that currently require expert review for accurate identification. This expert review is time consuming and subjective, limiting reproducibility and high-throughput applications.

NEW METHOD

To address this limitation, we develop a fully-automated method for spike ripple detection. The method consists of a convolutional neural network trained to compute the probability that a spectrogram image contains a spike ripple.

RESULTS

We validate the proposed spike ripple detector on expert-labeled data and show that this detector accurately separates subjects with low and high seizure risks.

COMPARISON WITH EXISTING METHOD

The proposed method performs as well as existing methods that require manual validation of candidate spike ripple events. The introduction of a fully automated method reduces subjectivity and increases rigor and reproducibility of this epilepsy biomarker.

CONCLUSION

We introduce and validate a fully-automated spike ripple detector to support utilization of this epilepsy biomarker in clinical and translational work.

Detrended fluctuation analysis in the presurgical evaluation of parietal lobe epilepsy patients

OBJECTIVE

To examine the usability of long-range temporal correlations (LRTCs) in non-invasive localization of the epileptogenic zone (EZ) in refractory parietal lobe epilepsy (RPLE) patients.

METHODS

We analyzed 10 RPLE patients who had presurgical MEG and underwent epilepsy surgery. We quantified LRTCs with detrended fluctuation analysis (DFA) at four frequency bands for 200 cortical regions estimated using individual source models. We correlated individually the DFA maps to the distance from the resection area and from cortical locations of interictal epileptiform discharges (IEDs). Additionally, three clinical experts inspected the DFA maps to visually assess the most likely EZ locations.

RESULTS

The DFA maps correlated with the distance to resection area in patients with type II focal cortical dysplasia (FCD) (p<0.05), but not in other etiologies. Similarly, the DFA maps correlated with the IED locations only in the FCD II patients. Visual analysis of the DFA maps showed high interobserver agreement and accuracy in FCD patients in assigning the affected hemisphere and lobe.

CONCLUSIONS

Aberrant LRTCs correlate with the resection areas and IED locations.

SIGNIFICANCE

This methodological pilot study demonstrates the feasibility of approximating cortical LRTCs from MEG that may aid in the EZ localization and provide new non-invasive insight into the presurgical evaluation of epilepsy.

Distinction of Physiologic and Epileptic Ripples: An Electrical Stimulation Study

Ripple oscillations (80-250 Hz) are a promising biomarker of epileptic activity, but are also involved in memory consolidation, which impairs their value as a diagnostic tool. Distinguishing physiologic from epileptic ripples has been particularly challenging because usually, invasive recordings are only performed in patients with refractory epilepsy. Here, we identified 'healthy' brain areas based on electrical stimulation and hypothesized that these regions specifically generate 'pure' ripples not coupled to spikes. Intracranial electroencephalography (EEG) recorded with subdural grid electrodes was retrospectively analyzed in 19 patients with drug-resistant focal epilepsy. Interictal spikes and ripples were automatically detected in slow-wave sleep using the publicly available Delphos software. We found that rates of spikes, ripples and ripples coupled to spikes ('spike-ripples') were higher inside the seizure-onset zone (p < 0.001). A comparison of receiver operating characteristic curves revealed that spike-ripples slightly delineated the seizure-onset zone channels, but did this significantly better than spikes (p < 0.001). Ripples were more frequent in the eloquent neocortex than in the remaining non-seizure onset zone areas (p < 0.001). This was due to the higher rates of 'pure' ripples (p < 0.001; median rates 3.3/min vs. 1.4/min), whereas spike-ripple rates were not significantly different (p = 0.87). 'Pure' ripples identified 'healthy' channels significantly better than chance (p < 0.001). Our findings suggest that, in contrast to epileptic spike-ripples, 'pure' ripples are mainly physiological. They may be considered, in addition to electrical stimulation, to delineate eloquent cortex in pre-surgical patients. Since we applied open source software for detection, our approach may be generally suited to tackle a variety of research questions in epilepsy and cognitive science.

Noninvasive high-frequency oscillations riding spikes delineates epileptogenic sources

Millions of people affected by epilepsy may undergo surgical resection of the epileptic tissues to stop seizures if such epileptic foci can be accurately delineated. High-frequency oscillations (HFOs), existing in electroencephalography, are highly correlated with epileptic brain, which is promising for guiding successful neurosurgery. However, it is unclear whether and how pathological HFOs can be differentiated to localize the epileptogenic tissues given the presence of various nonepileptic high-frequency activities. Here, we show morphological and source imaging evidence that pathological HFOs can be identified by the concurrence of epileptiform spikes. We describe a framework to delineate the underlying epileptogenicity using this biomarker. Our work may offer translational tools to improve treatments by noninvasively demarking pathological activities and hence epileptic foci.

High-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic brain and guiding successful neurosurgery. However, the utility and translation of noninvasive HFOs, although highly desirable, is impeded by the difficulty in differentiating pathological HFOs from nonepileptiform high-frequency activities and localizing the epileptic tissue using noninvasive scalp recordings, which are typically contaminated with high noise levels. Here, we show that the consistent concurrence of HFOs with epileptiform spikes (pHFOs) provides a tractable means to identify pathological HFOs automatically, and this in turn demarks an epileptiform spike subgroup with higher epileptic relevance than the other spikes in a cohort of 25 temporal epilepsy patients (including a total of 2,967 interictal spikes and 1,477 HFO events). We found significant morphological distinctions of HFOs and spikes in the presence/absence of this concurrent status. We also demonstrated that the proposed pHFO source imaging enhanced localization of epileptogenic tissue by 162% (∼5.36 mm) for concordance with surgical resection and by 186% (∼12.48 mm) with seizure-onset zone determined by invasive studies, compared to conventional spike imaging, and demonstrated superior congruence with the surgical outcomes. Strikingly, the performance of spike imaging was selectively boosted by the presence of spikes with pHFOs, especially in patients with multitype spikes. Our findings suggest that concurrent HFOs and spikes reciprocally discriminate pathological activities, providing a translational tool for noninvasive presurgical diagnosis and postsurgical evaluation in vulnerable patients.

Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome

Researchers have looked for rapidly- and objectively-measurable electrophysiology biomarkers that accurately localize the epileptogenic zone. Promising candidates include interictal high-frequency oscillation and phase-amplitude coupling. Investigators have independently created the toolboxes that compute the high-frequency oscillation rate and the severity of phase-amplitude coupling. This study of 135 patients determined what toolboxes and analytic approaches would optimally classify patients achieving post-operative seizure control. Four different detector toolboxes computed the rate of high-frequency oscillation at ≥80 Hz at intracranial EEG channels. Another toolbox calculated the modulation index reflecting the strength of phase-amplitude coupling between high-frequency oscillation and slow-wave at 3–4 Hz. We defined the completeness of resection of interictally-abnormal regions as the subtraction of high-frequency oscillation rate (or modulation index) averaged across all preserved sites from that averaged across all resected sites. We computed the outcome classification accuracy of the logistic regression-based standard model considering clinical, ictal intracranial EEG and neuroimaging variables alone. We then determined how well the incorporation of high-frequency oscillation/modulation index would improve the standard model mentioned above. To assess the anatomical variability across non-epileptic sites, we generated the normative atlas of detector-specific high-frequency oscillation and modulation index. Each atlas allowed us to compute the statistical deviation of high-frequency oscillation/modulation index from the non-epileptic mean. We determined whether the model accuracy would be improved by incorporating absolute or normalized high-frequency oscillation/modulation index as a biomarker assessing interictally-abnormal regions. We finally determined whether the model accuracy would be improved by selectively incorporating high-frequency oscillation verified to have high-frequency oscillatory components unattributable to a high-pass filtering effect. Ninety-five patients achieved successful seizure control, defined as International League against Epilepsy class 1 outcome. Multivariate logistic regression analysis demonstrated that complete resection of interictally-abnormal regions additively increased the chance of success. The model accuracy was further improved by incorporating z-score normalized high-frequency oscillation/modulation index or selective incorporation of verified high-frequency oscillation. The standard model had a classification accuracy of 0.75. Incorporation of normalized high-frequency oscillation/modulation index or verified high-frequency oscillation improved the classification accuracy up to 0.82. These outcome prediction models survived the cross-validation process and demonstrated an agreement between the model-based likelihood of success and the observed success on an individual basis. Interictal high-frequency oscillation and modulation index had a comparably additive utility in epilepsy presurgical evaluation. Our empirical data support the theoretical notion that the prediction of post-operative seizure outcomes can be optimized with the consideration of both interictal and ictal abnormalities.

Effects of Spatial Memory Processing on Hippocampal Ripples

Human High-Frequency-Oscillations (HFO) in the ripple band are oscillatory brain activity in the frequency range between 80 and 250 Hz. HFOs may comprise different subgroups that either play a role in physiologic or pathologic brain functions. An exact differentiation between physiologic and pathologic HFOs would help elucidate their relevance for cognitive and epileptogenic brain mechanisms, but the criteria for differentiating between physiologic and pathologic HFOs remain controversial. In particular, the separation of pathologic HFOs from physiologic HFOs could improve the identification of epileptogenic brain regions during the pre-surgical evaluation of epilepsy patients. In this study, we performed intracranial electroencephalography recordings from the hippocampus of epilepsy patients before, during, and after the patients completed a spatial navigation task. We isolated hippocampal ripples from the recordings and categorized the ripples into the putative pathologic group iesRipples, when they coincided with interictal spikes, and the putative physiologic group isolRipples, when they did not coincide with interictal spikes. We found that the occurrence of isolRipples significantly decreased during the task as compared to periods before and after the task. The rate of iesRipples was not modulated by the task. In patients who completed the spatial navigation task on two consecutive days, we furthermore examined the occurrence of ripples in the intervening night. We found that the rate of ripples that coincided with sleep spindles and were therefore putatively physiologic correlated with the performance improvement on the spatial navigation task, whereas the rate of all ripples did not show this relationship. Together, our results suggest that the differentiation of HFOs into putative physiologic and pathologic subgroups may help identify their role for spatial memory and memory consolidation processes. Conversely, excluding putative physiologic HFOs from putative pathologic HFOs may improve the HFO-based identification of epileptogenic brain regions in future studies.

Epileptogenic Network Formation

Epilepsy is characterized by specific alterations in network organization. The main parameters at the basis of epileptogenic network formation are alterations of cortical thickness, development of pathologic hubs, modification of hub distribution, and white matter alterations. The effect is a reinforcement of brain connectivity in both the epileptogenic zone and the propagation zone. Moreover, the epileptogenic network is characterized by some specific neurophysiologic biomarkers that evidence the tendency of the network itself to shift from an interictal state to an ictal one. The recognition of these features is crucial in planning epilepsy surgery.

Physiological Ripples Associated With Sleep Spindles Can Be Identified in Patients With Refractory Epilepsy Beyond Mesio-Temporal Structures

Introduction: High frequency oscillations (HFO) are promising biomarkers of epileptic tissue. While group analysis suggested a correlation between surgical removal of HFO generating tissue and seizure free outcome, HFO could not predict seizure outcome on an individual patient level. One possible explanation is the lack of differentiation between physiological and epileptic HFO. In the mesio-temporal lobe, a proportion of physiological ripples can be identified by their association with scalp sleep spindles. Spike associated ripples in contrast can be considered epileptic. This study investigated whether categorizing ripples by the co-occurrence with sleep spindles or spikes improves outcome prediction after surgery. Additionally, it aimed to investigate whether spindle-ripple association is limited to the mesio-temporal lobe structures or visible across the whole brain. Methods: We retrospectively analyzed EEG of 31 patients with chronic intracranial EEG. Sleep spindles in scalp EEG and ripples and epileptic spikes in iEEG were automatically detected. Three ripple subtypes were obtained: SpindleR, Non-SpindleR, and SpikeR. Rate ratios between removed and non-removed brain areas were calculated. We compared the distinct ripple subtypes and their rates in different brain regions, inside and outside seizure onset areas and between patients with good and poor seizure outcome. Results: SpindleR were found across all brain regions. SpikeR had significantly higher rates in the SOZ than in Non-SOZ channels. A significant positive correlation between removal of ripple-events and good outcome was found for the mixed ripple group (r_s = 0.43, p = 0.017) and for ripples not associated with spindles (r_s=0.40, p = 0.044). Also, a significantly high proportion of spikes associated with ripples were removed in seizure free patients (p = 0.036). Discussion: SpindleR are found in mesio-temporal and neocortical structures, indicating that ripple-spindle-coupling might have functional importance beyond mesio-temporal structures. Overall, the proportion of SpindleR was low and separating spindle and spike associated ripples did not improve outcome prediction in our patient group. SpindleR analysis therefore can be a tool to identify physiological events but needs to be used in combination with other methods to have clinical relevance.

Localizing the epileptogenic zone

PURPOSE OF REVIEW

Epilepsy surgery is the therapy of choice for 30-40% of people with focal drug-resistant epilepsy. Currently only ∼60% of well selected patients become postsurgically seizure-free underlining the need for better tools to identify the epileptogenic zone. This article reviews the latest neurophysiological advances for EZ localization with emphasis on ictal EZ identification, interictal EZ markers, and noninvasive neurophysiological mapping procedures.

RECENT FINDINGS

We will review methods for computerized EZ assessment, summarize computational network approaches for outcome prediction and individualized surgical planning. We will discuss electrical stimulation as an option to reduce the time needed for presurgical work-up. We will summarize recent research regarding high-frequency oscillations, connectivity measures, and combinations of multiple markers using machine learning. This latter was shown to outperform single markers. The role of NREM sleep for best identification of the EZ interictally will be discussed. We will summarize recent large-scale studies using electrical or magnetic source imaging for clinical decision-making.

SUMMARY

New approaches based on technical advancements paired with artificial intelligence are on the horizon for better EZ identification. They are ultimately expected to result in a more efficient, less invasive, and less time-demanding presurgical investigation.

High-Frequency Oscillations in Epilepsy: What Have We Learned and What Needs to be Addressed

For the past 2 decades, high-frequency oscillations (HFOs) have been enthusiastically studied by the epilepsy community. Emerging evidence shows that HFOs harbor great promise to delineate epileptogenic brain areas and possibly predict the likelihood of seizures. Investigations into HFOs in clinical epilepsy have advanced from small retrospective studies relying on visual identification and correlation analysis to larger prospective assessments using automatic detection and prediction strategies. Although most studies have yielded promising results, some have revealed significant obstacles to clinical application of HFOs, thus raising debate about the reliability and practicality of HFOs as clinical biomarkers. In this review, we give an overview of the current state of HFO research and pinpoint the conceptual and methodological issues that have hampered HFO translation. We highlight recent insights gained from long-term data, high-density recordings, and multicenter collaborations and discuss the open questions that need to be addressed in future research.

Spatiotemporal Patterns of High-Frequency Activity (80-170 Hz) in Long-Term Intracranial EEG

OBJECTIVE

To determine the utility of high-frequency activity (HFA) and epileptiform spikes as biomarkers for epilepsy, we examined the variability in their rates and locations using long-term ambulatory intracranial EEG (iEEG) recordings.

METHODS

This study used continuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy. HFA was defined as 80- to 170-Hz events with amplitudes clearly larger than the background, which was automatically detected with a custom algorithm. The automatically detected HFA was compared with visually annotated high-frequency oscillations (HFOs). The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases.

RESULTS

HFA included manually annotated HFOs and high-amplitude events occurring in the 80- to 170-Hz range without observable oscillatory behavior. HFA and spike rates had high amounts of intrapatient and interpatient variability. Rates of HFA and spikes had large variability after electrode implantation in most of the patients. Locations of HFA and spikes varied up to weeks in more than one-third of the patients. Both HFA and spike rates showed strong circadian rhythms in all patients, and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes.

CONCLUSION

Analysis of HFA and epileptiform spikes should consider postimplantation variability. HFA and epileptiform spikes, like seizures, show circadian rhythms. However, the circadian profiles can vary spatially within patients, and their correlations to seizures are patient-specific.

Fast oscillations >40 Hz localize the epileptogenic zone: An electrical source imaging study using high-density electroencephalography

OBJECTIVE

Fast Oscillations (FO) >40 Hz are a promising biomarker of the epileptogenic zone (EZ). Evidence using scalp electroencephalography (EEG) remains scarce. We assessed if electrical source imaging of FO using 256-channel high-density EEG (HD-EEG) is useful for EZ identification.

METHODS

We analyzed HD-EEG recordings of 10 focal drug-resistant epilepsy patients with seizure-free postsurgical outcome. We marked FO candidate events at the time of epileptic spikes and verified them by screening for an isolated peak in the time-frequency plot. We performed electrical source imaging of spikes and FO within the Maximum Entropy of the Mean framework. Source localization maps were validated against the surgical cavity.

RESULTS

We identified FO in five out of 10 patients who had a superficial or intermediate deep generator. The maximum of the FO maps was localized inside the cavity in all patients (100%). Analysis with a reduced electrode coverage using the 10-10 and 10-20 system showed a decreased localization accuracy of 60% and 40% respectively.

CONCLUSIONS

FO recorded with HD-EEG localize the EZ. HD-EEG is better suited to detect and localize FO than conventional EEG approaches.

SIGNIFICANCE

This study acts as proof-of-concept that FO localization using 256-channel HD-EEG is a viable marker of the EZ.

Long-range phase synchronization of high-frequency oscillations in human cortex

Inter-areal synchronization of neuronal oscillations at frequencies below ~100 Hz is a pervasive feature of neuronal activity and is thought to regulate communication in neuronal circuits. In contrast, faster activities and oscillations have been considered to be largely local-circuit-level phenomena without large-scale synchronization between brain regions. We show, using human intracerebral recordings, that 100–400 Hz high-frequency oscillations (HFOs) may be synchronized between widely distributed brain regions. HFO synchronization expresses individual frequency peaks and exhibits reliable connectivity patterns that show stable community structuring. HFO synchronization is also characterized by a laminar profile opposite to that of lower frequencies. Importantly, HFO synchronization is both transiently enhanced and suppressed in separate frequency bands during a response-inhibition task. These findings show that HFO synchronization constitutes a functionally significant form of neuronal spike-timing relationships in brain activity and thus a mesoscopic indication of neuronal communication per se.

High-frequency oscillations (HFOs) are common in mammalian brains and have been assumed to be strictly local. Using human intracerebral recordings, the authors find that HFOs can be phase synchronized across long distances between active cortical sites during resting and task states, which may reflect neuronal communication.

HFO to Measure Seizure Propensity and Improve Prognostication in Patients With Epilepsy

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

Impact of predictive, preventive and precision medicine strategies in epilepsy

Over the last decade, advances in genetics, neuroimaging and EEG have enabled the aetiology of epilepsy to be identified earlier in the disease course than ever before. At the same time, progress in the study of experimental models of epilepsy has provided a better understanding of the mechanisms underlying the condition and has enabled the identification of therapies that target specific aetiologies. We are now witnessing the impact of these advances in our daily clinical practice. Thus, now is the time for a paradigm shift in epilepsy treatment from a reactive attitude, treating patients after the onset of epilepsy and the initiation of seizures, to a proactive attitude that is more broadly integrated into a 'P4 medicine' approach. This P4 approach, which is personalized, predictive, preventive and participatory, puts patients at the centre of their own care and, ultimately, aims to prevent the onset of epilepsy. This aim will be achieved by adapting epilepsy treatments not only to a given syndrome but also to a given patient and moving from the usual anti-seizure treatments to personalized treatments designed to target specific aetiologies. In this Review, we present the current state of this ongoing revolution, emphasizing the impact on clinical practice.

Interictal Fast Ripples Are Associated With the Seizure-Generating Lesion in Patients With Dual Pathology

Rationale: Patients with dual pathology have two potentially epileptogenic lesions: One in the hippocampus and one in the neocortex. If epilepsy surgery is considered, stereotactic electroencephalography (SEEG) may reveal which of the lesions is seizure-generating, but frequently, some uncertainty remains. We aimed to investigate whether interictal high-frequency oscillations (HFOs), which are a promising biomarker of epileptogenicity, are associated with the primary focus.

Methods: We retrospectively analyzed 16 patients with dual pathology. They were grouped according to their seizure-generating lesion, as suggested by ictal SEEG. An automated detector was applied to identify interictal epileptic spikes, ripples (80–250 Hz), ripples co-occurring with spikes (IES-ripples) and fast ripples (250–500 Hz). We computed a ratio R to obtain an indicator of whether rates were higher in the hippocampal lesion (R close to 1), higher in the neocortical lesion (R close to −1), or more or less similar (R close to 0).

Results: Spike and HFO rates were higher in the hippocampal than in the neocortical lesion (p < 0.001), particularly in seizure onset zone channels. Seizures originated exclusively in the hippocampus in 5 patients (group 1), in both lesions in 7 patients (group 2), and exclusively in the neocortex in 4 patients (group 3). We found a significant correlation between the patients' primary focus and the ratio R_{fast ripples}, i.e., the proportion of interictal fast ripples detected in this lesion (p < 0.05). No such correlation was observed for interictal epileptic spikes (p = 0.69), ripples (p = 0.60), and IES-ripples (p = 0.54). In retrospect, interictal fast ripples would have correctly “predicted” the primary focus in 69% of our patients (p < 0.01).

Conclusions: We report a correlation between interictal fast ripple rate and the primary focus, which was not found for epileptic spikes. Fast ripple analysis could provide helpful information for generating a hypothesis on seizure-generating networks, especially in cases with few or no recorded seizures.

Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions

High-frequency oscillations are markers of epileptic tissue. Recently, different patterns of EEG background activity were described from which high-frequency oscillations occur: high-frequency oscillations with continuously oscillating background were found to be primarily physiological, those from quiet background were linked to epileptic tissue. It is unclear, whether these interactions remain stable over several days and during different sleep-wake stages. High-frequency oscillation patterns (oscillatory vs. quiet background) were analysed in 23 patients implanted with depth and subdural grid electrodes. Pattern scoring was performed on every channel in 10 s intervals in three separate day- and night-time EEG segments. An entropy value, measuring variability of patterns per channel, was calculated. A low entropy value indicated a stable occurrence of the same pattern in one channel, whereas a high value indicated pattern instability. Differences in pattern distribution and entropy were analysed for 143 280 10 s intervals with allocated patterns from inside and outside the seizure onset zone, different electrode types and brain regions. We found a strong association between high-frequency oscillations out of quiet background activity, and channels of the seizure onset zone (35.2% inside versus 9.7% outside the seizure onset zone, P < 0.001), no association was found for high-frequency oscillations from continuous oscillatory background (P = 0.563). The type of background activity remained stable over the same brain region over several days and was independent of sleep stage and recording technique. Stability of background activity was significantly higher in channels of the seizure onset zone (entropy mean value 0.56 ± 0.39 versus 0.64 ± 0.41; P < 0.001). This was especially true for the presumed epileptic high-frequency oscillations out of quiet background (0.57 ± 0.39 inside versus 0.72 ± 0.37 outside the seizure onset zone; P < 0.001). In contrast, presumed physiological high-frequency oscillations from continuous oscillatory backgrounds were significantly more stable outside the seizure onset zone (0.72 ± 0.45 versus 0.48 ± 0.53; P < 0.001). The overall low entropy values suggest that interactions between high-frequency oscillations and background activity are a stable phenomenon specific to the function of brain regions. High-frequency oscillations occurring from a quiet background are strongly linked to the seizure onset zone whereas high-frequency oscillations from an oscillatory background are not. Pattern stability suggests distinct underlying mechanisms. Analysing short time segments of high-frequency oscillations and background activity could help distinguishing epileptic from physiologically active brain regions.

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.