Maeike Zijlmans

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.

Heart Rate Changes and ECG Abnormalities During Epileptic Seizures: Prevalence and Definition of an Objective Clinical Sign

Summary:  Purpose: To determine the prevalence of heart rate changes and ECG abnormalities during epileptic seizures and to determine the timing of heart rate changes compared to the first electrographic and clinical signs. To assess the risk factors for the occurrence of ECG abnormalities.

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 (HFOs) in clinical epilepsy

Epilepsy is one of the most frequent neurological diseases. In focal medically refractory epilepsies, successful surgical treatment largely depends on the identification of epileptogenic zone. High-frequency oscillations (HFOs) between 80 and 500Hz, which can be recorded with EEG, may be novel markers of the epileptogenic zone. This review discusses the clinical importance of HFOs as markers of epileptogenicity and their application in different types of epilepsies. HFOs are clearly linked to the seizure onset zone, and the surgical removal of regions generating them correlates with a seizure free post-surgical outcome. Moreover, HFOs reflect the seizure-generating capability of the underlying tissue, since they are more frequent after the reduction of antiepileptic drugs. They can be successfully used in pediatric epilepsies such as epileptic spasms and help to understand the generation of this specific type of seizures. While mostly recorded on intracranial EEGs, new studies suggest that identification of HFOs on scalp EEG or magnetoencephalography (MEG) is possible as well. Thus not only patients with refractory epilepsies and invasive recordings but all patients might profit from the analysis of HFOs. Despite these promising results, the analysis of HFOs is not a routine clinical procedure; most results are derived from relatively small cohorts of patients and many aspects are not yet fully understood. Thus the review concludes that even if HFOs are promising biomarkers of epileptic tissue, there are still uncertainties about mechanisms of generation, methods of analysis, and clinical applicability. Large multicenter prospective studies are needed prior to widespread clinical application.

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.

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.

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.

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.

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

Epilepsy surgery depends on reliable pre-surgical markers of epileptogenic tissue. The current gold standard is the seizure onset zone in ictal, i.e. chronic, electrocorticography recordings. Single pulse electrical stimulation can evoke epileptic, spike-like responses in areas of seizure onset also recorded by electrocorticography. Recently, spontaneous pathological high-frequency oscillations (80-520 Hz) have been observed in the electrocorticogram that are related to epileptic spikes, but seem more specific for epileptogenic cortex. We wanted to see whether a quantitative electroencephalography analysis using time-frequency information including the higher frequency range could be applied to evoked responses by single pulse electrical stimulation, to enhance its specificity and clinical use. Electrocorticography data were recorded at a 2048-Hz sampling rate from 13 patients. Single pulse electrical stimulation (10 stimuli, 1 ms, 8 mA, 0.2 Hz) was performed stimulating pairs of adjacent electrodes. A time-frequency analysis based on Morlet wavelet transformation was performed in a [-1 s : 1 s] time interval around the stimulus and a frequency range of 10-520 Hz. Significant (P = 0.05) changes in power spectra averaged for 10 epochs were computed, resulting in event-related spectral perturbation images. In these images, time-frequency analysis of single pulse-evoked responses, in the range of 10-80 Hz for spikes, 80-250 Hz for ripples and 250-520 Hz for fast ripples, were scored by two observers independently. Sensitivity, specificity and predictive value of time-frequency single pulse-evoked responses in the three frequency ranges were compared with seizure onset zone and post-surgical outcome. In all patients, evoked responses included spikes, ripples and fast ripples. For the seizure onset zone, the median sensitivity of time-frequency single pulse-evoked responses decreased from 100% for spikes to 67% for fast ripples and the median specificity increased from 17% for spikes to 79% for fast ripples. A median positive predictive value for the evoked responses in the seizure onset zone of 17% was found for spikes, 26% for ripples and 37% for fast ripples. Five out of seven patients with <50% of fast ripples removed by resection had a poor outcome. A wavelet transform-based time-frequency analysis of single pulse electrical stimulation reveals evoked responses in the frequency range of spikes, ripples and fast ripples. We demonstrate that time-frequency analysis of single pulse electrical stimulation can assist in delineation of the epileptogenic cortex using time-frequency single pulse-evoked fast ripples as a potential new marker.

Modality-specific spike identification in simultaneous magnetoencephalography/electroencephalography: A methodological approach

Summary Epileptiform spikes may have a different morphology and signal-to-noise ratio in simultaneously recorded EEGs and magnetoencephalograms (MEGs) that may lead to differences in the identification of spikes if both the modalities are presented separately. Moreover, there are no criteria for MEG spikes. It is unknown to which extent the visual assessment of MEG data yields consistent and meaningful results. Nineteen patients were selected with mesial temporal lobe epilepsy who underwent whole-head simultaneous MEG/EEG. These data were split into MEG and EEG files and were assessed independently by three observers for the occurrence of spikes. Interobserver kappa values were calculated. A mean kappa value greater than 0.5 was taken as a criterion for the presence of unequivocal spikes. Index cases from the resulting four subgroups were studied further. One patient had unequivocal spikes in both modalities, one in EEG only, one in MEG only, and one did not show any unequivocal spike. Spikes on which at least two observers agreed were then subjected to a template match algorithm to test for equal morphology and distribution. Equal spikes were averaged and electrical and magnetic field maps were plotted. Unequivocal spikes were found in both MEG and EEG in one patient, in MEG only in two patients, in EEG only in two patients, and no spikes in either modality were seen in 14 patients. In the four index patients, MEG showed 50 to 80% more spikes than EEG. After averaging identical consensus spikes, MEG spikes revealed a concomitant spike in the EEG, but the reverse was not always true. Even in the patient with MEG and EEG spikes that met all selection criteria, simultaneous field maps showed unexpected inconsistencies. In most patients with mesial temporal lobe epilepsy, there are no unequivocal spikes during MEG/EEG. In some cases, however, experienced electroencephalographers can identify MEG spikes reliably. Because of a better signal-to-noise ratio, more spikes could be identified in MEG than in EEG. Simultaneous MEG/EEG recordings do not simply ensure the best of both, but one modality may improve the identification of spikes in the other. In addition, different aspects of a complex source can be revealed. Our three-step approach to combined data ensures a reproducible selection of spikes for source modeling.