Rina Zelmann

High-Frequency Electroencephalographic Oscillations Correlate With Outcome of Epilepsy Surgery

High‐frequency oscillations (HFOs) in the intracerebral electroencephalogram (EEG) have been linked to the seizure onset zone (SOZ). We investigated whether HFOs can delineate epileptogenic areas even outside the SOZ by correlating the resection of HFO‐generating areas with surgical outcome.

High-Frequency Oscillations as a New Biomarker in Epilepsy

The discovery that electroencephalography (EEG) contains useful information at frequencies above the traditional 80Hz limit has had a profound impact on our understanding of brain function. In epilepsy, high‐frequency oscillations (HFOs, >80Hz) have proven particularly important and useful. This literature review describes the morphology, clinical meaning, and pathophysiology of epileptic HFOs. To record HFOs, the intracranial EEG needs to be sampled at least at 2,000Hz. The oscillatory events can be visualized by applying a high‐pass filter and increasing the time and amplitude scales, or EEG time‐frequency maps can show the amount of high‐frequency activity. HFOs appear excellent markers for the epileptogenic zone. In patients with focal epilepsy who can benefit from surgery, invasive EEG is often required to identify the epileptic cortex, but current information is sometimes inadequate. Removal of brain tissue generating HFOs has been related to better postsurgical outcome than removing the seizure onset zone, indicating that HFOs may mark cortex that needs to be removed to achieve seizure control. The pathophysiology of epileptic HFOs is challenging, probably involving populations of neurons firing asynchronously. They differ from physiological HFOs in not being paced by rhythmic inhibitory activity and in their possible origin from population spikes. Their link to the epileptogenic zone argues that their study will teach us much about the pathophysiology of epileptogenesis and ictogenesis. HFOs show promise for improving surgical outcome and accelerating intracranial EEG investigations. Their potential needs to be assessed by future research. Ann Neurol 2012;71:169–178

High-frequency oscillations mirror disease activity in patients with epilepsy

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

Interictal scalp fast oscillations as a marker of the seizure onset zone

Objective: This study aims to identify if oscillations at frequencies higher than the traditional EEG can be recorded on the scalp EEG of patients with focal epilepsy and to analyze the association of these oscillations with interictal discharges and the seizure onset zone (SOZ). Methods: The scalp EEG of 15 patients with focal epilepsy was studied. We analyzed the rates of gamma (40–80 Hz) and ripple (>80 Hz) oscillations, their co-occurrence with spikes, the number of channels with fast oscillations inside and outside the SOZ, and the specificity, sensitivity, and accuracy of gamma, ripples, and spikes to determine the SOZ. Results: Gamma and ripples frequently co-occurred with spikes (77.5% and 63% of cases). For all events, the proportion of channels with events was consistently higher inside than outside the SOZ: spikes (100% vs 70%), gamma (82% vs 33%), and ripples (48% vs 11%); p < 0.0001. The mean rates (events/min) were higher inside than outside the SOZ: spikes (2.64 ± 1.70 vs 0.69 ± 0.26, p = 0.02), gamma (0.77 ± 0.71 vs 0.20 ± 0.25, p = 0.02), and ripples (0.08 ± 0.12 vs 0.04 ± 0.09, p = 0.04). The sensitivity to identify the SOZ was spikes 100%, gamma 82%, and ripples 48%; the specificity was spikes 30%, gamma 68%, and ripples 89%; and the accuracy was spikes 43%, gamma 70%, and ripples 81%. Conclusion: The rates and the proportion of channels with gamma and ripple fast oscillations are higher inside the SOZ, indicating that they can be used as interictal scalp EEG markers for the SOZ. These fast oscillations are less sensitive but much more specific and accurate than spikes to delineate the SOZ.

Recording and analysis techniques for high-frequency oscillations

In recent years, new recording technologies have advanced such that, at high temporal and spatial resolutions, high-frequency oscillations (HFO) can be recorded in human partial epilepsy. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings depends on the development of new data mining techniques to extract meaningful information relating to time, frequency and space. Here, we aim to bridge this gap by focusing on up-to-date recording techniques for measurement of HFO and new analysis tools for their quantitative assessment. In particular, we emphasize how these methods can be applied, what property might be inferred from neuronal signals, and potentially productive future directions.

Ictal and interictal high frequency oscillations in patients with focal epilepsy

OBJECTIVE High frequency oscillations (HFOs) can be recorded with depth electrodes in focal epilepsy patients. They occur during seizures and interictally and seem important in seizure genesis. We investigated whether interictal and ictal HFOs occur in the same regions and how they relate to epileptiform spikes. METHODS In 25 patients, spikes, ripples (80-250 Hz) and fast ripples (FR: 250-500 Hz) and their co-occurrences were marked during interictal slow wave sleep (5-10 min), during 10 pre-ictal seconds and 5s following seizure onset. We compared occurrence and spatial distribution between these periods. RESULTS HFOs and spikes increased from interictal to ictal periods: the percentage of time occupied by ripples increased from 2.3% to 6.5%, FR from 0.2% to 0.8%, spikes from 1.1% to 4.8%. HFOs increased from interictal to pre-ictal periods in contrast to spikes. Spikes were in different channels in the interictal, pre-ictal and ictal periods whereas HFOs largely remained in the same channels. CONCLUSIONS HFOs remain confined to the same, possibly epileptogenic, area, during interictal and ictal periods, while spikes are more widespread during seizures than interictally. SIGNIFICANCE Ictal and interictal HFOs represent the same (epileptogenic) area and are probably similar phenomena.

High-frequency oscillations, extent of surgical resection, and surgical outcome in drug-resistant focal epilepsy.

Removal of areas generating high‐frequency oscillations (HFOs) recorded from the intracerebral electroencephalography (iEEG) of patients with medically intractable epilepsy has been found to be correlated with improved surgical outcome. However, whether differences exist according to the type of epilepsy is largely unknown. We performed a comparative assessment of the impact of removing HFO‐generating tissue on surgical outcome between temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE). We also assessed the relationship between the extent of surgical resection and surgical outcome.

Improving the identification of High Frequency Oscillations

OBJECTIVE High Frequency Oscillations (HFOs), including Ripples (80-250Hz) and Fast Ripples (250-500Hz), can be recorded from intracranial macroelectrodes in patients with intractable epilepsy. We implemented a procedure to establish the duration for which a stable measurement of rate of HFOs is achieved. METHODS To determine concordance, Kappa coefficient was computed. The information gained when increasing the duration was analyzed in terms of HFO rates and ranking of channels with respect to HFO and spike rates. RESULTS In a group of 30 patients, Kappa was 0.7 for ripples, 0.7 for fast ripples and 0.67 for spikes. Five minutes provided the same information as 10min in terms of rates in 9/10 patients and with respect to ranking of channels in 8/10 patients; 5/30 patients did not achieve stable measurements of HFOs or spikes and needed marking for 10min. CONCLUSION We propose that 5min provides in most cases the same information as a longer interval when identifying HFOs and spikes in slow wave sleep, and present methods to identify when this is not the case. SIGNIFICANCE This procedure is useful to control for consistency between readers and to evaluate if the selected interval provides stable information, for automatic and visual identification of events.

High frequency oscillations (80–500 Hz) in the preictal period in patients with focal seizures

Purpose:  Intracranial depth macroelectrode recordings from patients with focal seizures demonstrate interictal and ictal high frequency oscillations (HFOs, 80–500 Hz). These HFOs are more frequent in the seizure‐onset zone (SOZ) and reported to be linked to seizure genesis. We evaluated whether HFO activity changes in a systematic way during the preictal period.

A comparison between detectors of high frequency oscillations

OBJECTIVE High frequency oscillations (HFOs) are a biomarker of epileptogenicity. Visual marking of HFOs is highly time-consuming and inevitably subjective, making automatic detection necessary. We compare four existing detectors on the same dataset. METHODS HFOs and baselines were identified by experienced reviewers in intracerebral EEGs from 20 patients. A new feature of our detector to deal with channels where baseline cannot be found is presented. The original and an optimal configuration are implemented. Receiver operator curves, false discovery rate, and channel ranking are used to evaluate performance. RESULTS All detectors improve performance with the optimal configuration. Our detector had higher sensitivity, lower false positives than the others, and similar false detections. The main difference in performance was in very active channels. CONCLUSIONS Each detector was developed for different recordings and with different aims. Our detector performed better in this dataset, but was developed on data similar to the test data. Moreover, optimizing on a particular data type improves performance in any detector. SIGNIFICANCE Automatic HFO detection is crucial to propel their clinical use as biomarkers of epileptogenic tissue. Comparing detectors on a single dataset is important to analyze their performance and to emphasize the issues involved in validation.