Rate Of High Frequency Oscillations Research Articles

The high frequency oscillations in the amygdala, hippocampus, and temporal cortex during mesial temporal lobe epilepsy.

The mesial temporal lobe epilepsy (MTLE) seizures are believed to originate from medial temporal structures, including the amygdala, hippocampus, and temporal cortex. Thus, the seizures onset zones (SOZs) of MTLE locate in these regions. However, whether the neural features of SOZs are specific to different medial temporal structures are still unclear and need more investigation. To address this question, the present study tracked the features of two different high frequency oscillations (HFOs) in the SOZs of these regions during MTLE seizures from 10 drug-resistant MTLE patients, who received the stereo electroencephalography (SEEG) electrodes implantation surgery in the medial temporal structures. Remarkable difference of HFOs features, including the proportions of HFOs contacts, percentages of HFOs contacts with significant coupling and firing rates of HFOs, could be observed in the SOZs among three medial temporal structures during seizures. Specifically, we found that the amygdala might contribute to the generation of MTLE seizures, while the hippocampus plays a critical role for the propagation of MTLE seizures. In addition, the HFOs firing rates in SOZ regions were significantly larger than those in NonSOZ regions, suggesting the potential biomarkers of HFOs for MTLE seizure. Moreover, there existed higher percentages of SOZs contacts in the HFOs contacts than in all SEEG contacts, especially those with significant coupling to slow oscillations, implying that specific HFOs features would help identify the SOZ regions. Taken together, our results displayed the features of HFOs in different medial temporal structures during MTLE seizures, and could deepen our understanding concerning the neural mechanism of MTLE.

Temporal and morphological characteristics of high-frequency oscillations in an acute in vivo model of epilepsy.

Approximately 30% of patients with epilepsy do not respond to anti-epileptogenic drugs. Surgical removal of the epileptogenic zone (EZ), the brain regions where the seizures originate and spread, can be a possible therapy for these patients, but localizing the EZ is challenging due to a variety of clinical factors. High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the EZ, but it is currently unknown whether HFO rates and HFO morphology modulate as pathological brain networks evolve in a way that gives rise to seizures. To address this question, we assessed the temporal evolution of the duration of HFO events, amplitude of HFO events, and rates of HFOs per minute. HFO events were quantified using the 4AP in vivo rodent model of epilepsy, inducing seizures in two different brain areas. We found that the duration and amplitude of HFO events were significantly increased for the cortex model when compared to the hippocampus model. Additionally, the duration and amplitude increased significantly between baseline and pre-ictal HFOs in both models. On the other hand, the two models did not display a consistent increasing or decreasing trend in amplitude, duration or rate when comparing ictal and postictal intervals. Clinical Relevance- We assessed the amplitude, duration, and rate of HFOs in two acute in vivo rodent models of epilepsy. The significant modulation of HFO morphology from baseline to pre-ictal periods suggests that these features may be a robust biomarker for pathological tissue involved in epileptogenesis. Moreover, the differences in HFO morphology observed between cortex and hippocampus animal models possibly indicate that different structural network characteristics of the EZ cause this modulation. In all, we found that HFO features modulate significantly with the onset of seizures, further highlighting the need to consider of HFO morphology in EZ-localizing studies.

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.

Changes in epileptogenicity biomarkers after stereotactic thermocoagulation.

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. 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. 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). 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.

Interictal high frequency background activity as a biomarker of epileptogenic tissue.

High frequency oscillations (HFOs) are very brief events that are a well-established biomarker of the epileptogenic zone (EZ) but are rare and comprise only a tiny fraction of the total recorded EEG. We hypothesize that the interictal high frequency ‘background’ data, which has received little attention but represents the majority of the EEG record, also may contain additional, novel information for identifying the EZ. We analysed intracranial EEG (30–500 Hz frequency range) acquired from 24 patients who underwent resective surgery. We computed 38 quantitative features based on all usable, interictal data (63–307 h per subject), excluding all detected HFOs. We assessed association between each feature and the seizure onset zone (SOZ) and resected volume (RV) using logistic regression. A pathology score per channel was also created via principle component analysis and logistic regression, using hold-out-one-patient cross-validation to avoid in-sample training. Association of the pathology score with the SOZ and RV was quantified using an asymmetry measure. Many features were associated with the SOZ: 23/38 features had odds ratios >1.3 or <0.7 and 17/38 had odds ratios different than zero with high significance (P < 0.001/39, logistic regression with Bonferroni Correction). The pathology score, the rate of HFOs, and their channel-wise product were each strongly associated with the SOZ [median asymmetry ≥0.44, good surgery outcome patients; median asymmetry ≥0.40, patients with other outcomes; 95% confidence interval (CI) > 0.27 in both cases]. The pathology score and the channel-wise product also had higher asymmetry with respect to the SOZ than the HFO rate alone (median difference in asymmetry ≥0.18, 95% CI >0.05). These results support that the high frequency background data contains useful information for determining the EZ, distinct and complementary to information from detected HFOs. The concordance between the high frequency activity pathology score and the rate of HFOs appears to be a better biomarker of epileptic tissue than either measure alone.

Effects of a stable concentration of propofol on interictal high-frequency oscillations in drug-resistant epilepsy.

The aim of this study was to clarify the effect of a stable concentration of propofol on interictal high-frequency oscillations (HFOs), which may contribute to identifying the epileptogenic zone intraoperatively for resection surgery. Nine patients with drug-resistant focal epilepsy who underwent invasive pre-surgical evaluation with chronic subdural electrodes were recruited. Five-minute electrocorticograms during wakefulness, slow-wave sleep, and under a stable brain concentration of propofol were recorded with the same electrodes. In each patient, 1-10 pairs of electrodes were selected for both electrodes with EEG changes within 5 seconds from the ictal onset (ictal pattern for 5 seconds [IP5]) and those outside the area of IP5 with no interictal epileptiform discharges (non-epileptiform [nEPI]). The numbers of ripples (80-250Hz) and fast ripples (>250Hz) were measured semi-automatically using an established algorithm. Statistical testing was performed with a mixed effect model. Thirty-seven pairs of electrodes from nine patients were analysed for IP5 and 29 pairs from seven patients were analysed for nEPI. The numbers of HFOs differed between the areas (IP5 and nEPI) and among the conditions (wakefulness, slow-wave sleep, propofol anaesthesia) (all p <0.01). The HFO occurrence rates were significantly higher for IP5 than those for nEPI in all conditions (for both ripples and fast ripples in all conditions; p <0.01). The occurrence rates of HFOs for IP5 were significantly higher than those for nEPI under propofol anaesthesia. These are fundamental findings for intraoperative HFO analysis, however, the following limitations should be considered: physiological HFOs could not be completely differentiated from pathological HFOs; in order to apply an HFO detector, an appropriate cut-off threshold is needed; an artefact of the impulse response filter appears as an HFO; and the series was comprised of a small number of heterogeneous patients.

Are HFOs in the Intra-operative ECoG Related to Hippocampal Sclerosis, Volume and IQ?

Temporal lobe epilepsy (TLE) is the most common form of refractory focal epilepsy and is often associated with hippocampal sclerosis (HS) and cognitive disturbances. Over the last decade, high frequency oscillations (HFOs) in the intraoperative electrocorticography (ioECoG) have been proposed to be biomarkers for the delineation of epileptic tissue but hippocampal ripples have also been associated with memory consolidation. Healthy hippocampi can show prolonged ripple activity in stereo- EEG. We aimed to identify how the HFO rates [ripples (80–250 Hz, fast ripples (250–500 Hz); prolonged ripples (80–250 Hz, 200–500 ms)] in the pre-resection ioECoG over subtemporal area (hippocampus) and lateral temporal neocortex relate to presence of hippocampal sclerosis, the hippocampal volume quantified on MRI and the severity of cognitive impairment in TLE patients. Volumetric measurement of hippocampal subregions was performed in 47 patients with TLE, who underwent ioECoG. Ripples, prolonged ripples, and fast ripples were visually marked and rates of HFOs were calculated. The intellectual quotient (IQ) before resection was determined. There was a trend toward higher rates of ripples and fast ripples in subtemporal electrodes vs. the lateral neocortex (ripples: 2.1 vs. 1.3/min; fast ripples: 0.9 vs. 0.2/min). Patients with HS showed higher rates of subtemporal fast ripples than other patients (Z = −2.51, p = 0.012). Prolonged ripples were only found in the lateral temporal neocortex. The normalized ratio (smallest/largest) of hippocampal volume was correlated to pre-resection IQ (r = 0.45, p = 0.015). There was no correlation between HFO rates and hippocampal volumes or HFO rates and IQ. To conclude, intra-operative fast ripples were a marker for HS, but ripples and fast ripples were not linearly correlated with either the amount of hippocampal atrophy, nor for pre-surgical IQ.

Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms

Objective. Scalp high-frequency oscillations (HFOs) are a promising biomarker of epileptogenicity in infantile spasms (IS) and many other epilepsy syndromes, but prior studies have relied on visual analysis of short segments of data due to the prevalence of artifacts in EEG. Here we set out to robustly characterize the rate and spatial distribution of HFOs in large datasets from IS subjects using fully automated HFO detection techniques. Approach. We prospectively collected long-term scalp EEG data from 12 subjects with IS and 18 healthy controls. For patients with IS, recording began prior to diagnosis and continued through initiation of treatment with adrenocorticotropic hormone (ACTH). The median analyzable EEG duration was 18.2 h for controls and 84.5 h for IS subjects (∼1300 h total). Ripples (80–250 Hz) were detected in all EEG data using an automated algorithm. Main results. HFO rates were substantially higher in patients with IS compared to controls. In IS patients, HFO rates were higher during sleep compared to wakefulness (median 5.5 min−1 and 2.9 min−1, respectively; p = 0.002); controls did not exhibit a difference in HFO rate between sleep and wakefulness (median 0.98 min−1 and 0.82 min−1, respectively). Spatially, IS patients exhibited significantly higher rates of HFOs in the posterior parasaggital region and significantly lower HFO rates in frontal channels, and this difference was more pronounced during sleep. In IS subjects, ACTH therapy significantly decreased the rate of HFOs. Significance. Here we provide a detailed characterization of the spatial distribution and rates of HFOs associated with IS, which may have relevance for diagnosis and assessment of treatment response. We also demonstrate that our fully automated algorithm can be used to detect HFOs in long-term scalp EEG with sufficient accuracy to clearly discriminate healthy subjects from those with IS.

Scalp EEG interictal high frequency oscillations as an objective biomarker of infantile spasms

ObjectiveTo investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms. MethodsWe retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000 Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80–500 Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3–4 Hz and 0.5–1 Hz as quantified with modulation indices (MIs). ResultsTwenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p < 0.001 by HFO rate; p < 0.01 by MIs at 3–4 Hz; p = 0.02 by MIs at 0.5–1 Hz). The presence of active spasms was predicted by the logistic regression models incorporating HFO-related metrics (AUC: 0.80–0.98) better than that incorporating hypsarrhythmia (AUC: 0.61). The predictive performance of the best model remained favorable (87.5% accuracy) after a cross-validation procedure. ConclusionsIncreased rate of HFOs and coupling between HFOs and SWA are associated with active epileptic spasms. SignificanceScalp-recorded HFOs may serve as an objective EEG biomarker for active epileptic spasms.

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

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

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

To investigate whether high-frequency oscillations (HFOs) show spatiotemporal propagation and assess the relevance of the earliest oscillations in relation to the seizure onset zone (SOZ) and postsurgical outcome. We retrospectively investigated the intracerebral electroencephalography (EEG) of patients who became seizure free after subsequent surgery. We marked HFOs during 1 hour of recordings. We calculated the time delay between pairs of channels as the median delay between their HFOs and constructed a time line of the delay of each channel with respect to the earliest channel (first source channel). A network was defined when a temporal order could be established among the channels based on the existence of statistically significant delays. Fifteen patients with good surgical outcome were included. We found ripple networks in all patients, and fast ripple networks in 9. For ripples, first source channels were found in a higher proportion in the SOZ than the rest of the network channels (15 of 27 [56%] versus 93 of 262 [35%]; p = 0.04). For both ripples and fast ripples, first source channels were resected more often that the rest of the network channels (ripples: 13 of 27 [48%] versus 65 of 262 [25%]; p = 0.01; fast ripples: 8 of 9 [89%] versus 17 of 40 [43%]; p = 0.002); channels with the highest rates of ripples and fast ripples were resected in a similar proportion. These results demonstrate that interictal HFOs are organized in networks and indicate a possible need for the resection of first source channels. However, resecting them is not superior to resecting channels with highest rates of HFOs. Ann Neurol 2019;85:485-494.

T115. Magnetoencephalographic imaging of ictal high-frequency oscillations (80–200 Hz) in medically refractory epilepsy

Specificity of ictal high-frequency oscillations (HFOs) in identifying epileptogenic abnormality is significant, compared to the spikes and interictal HFOs. The objectives of the study were to detect and to localize ictal HFOs by magnetoencephalography (MEG) for identifying the seizure onset zone (SOZ), evaluate the cortical excitability from preictal to ictal transition, and establish HFO concordance rates with other modalities and postsurgical resection. Sixty-seven patients with drug-resistant epilepsy had at least 1 spontaneous seizure each during MEG acquisition, and analysis was carried out on 20 seizures from 20 patients. Ictal MEG data were bandpass filtered (80–200 Hz) to visualize, review, and analyze the HFOs co-occurring with ictal spikes. Source montages were generated on both hemispheres, mean fast Fourier transform was computed on virtual time series for determining the preictal to ictal spectral power transition, and source reconstruction was performed with sLORETA and beamformers. The concordance rates of ictal MEG HFOs (SOZ) was estimated with 4 reference epileptogenic regions. In each subject, transient bursts of high-frequency oscillatory cycles, distinct from the background activity, were observed in the periictal continuum. Time–frequency analysis showed significant spectral power surge (85–160 Hz) during ictal state (P < .05) compared to preictal state, but there was no variation in the peak HFO frequencies (P > .05) for each subgroup and at each source montage. HFO source localization was consistent between algorithms (k = 0.8570 ± 0.138), with presumed epileptogenic zone (EZ) comparable to other modalities. In patients who underwent surgery (n = 6), MEG HFO SOZ was concordant with the presumed EZ HFOs could reliably be detected in the MEG periictal state, and its sources were accurately localized in almost 95% of the patients with medically refractory epilepsy. During preictal to ictal transition, HFOs exhibited dynamic augmentation in intrinsic epileptogenicity. Spatial overlap of ictal HFO sources was consistent with EZ determinants including the presumed EZ and the surgical resection area.

Extrahippocampal high-frequency oscillations during epileptogenesis.

The current study aimed to investigate the spatial and temporal patterns of high-frequency oscillations (HFOs) in the intra-/extrahippocampal areas during epileptogenesis. Local field potentials were bilaterally recorded from hippocampus (CA1), thalamus, motor cortex, and prefrontal cortex in 13 rats before and after intrahippocampal kainic acid (KA) lesions. HFOs in the ripple (100-200 Hz) and fast ripple (250-500 Hz) ranges were detected and their rates were computed during different time periods (1-5 weeks) after KA-induced status epilepticus (SE). Recurrent spontaneous seizures were observed in 7 rats after SE, and the other 6 rats did not develop epilepsy. During the latent period, the rate of hippocampal HFOs increased at the ipsilateral site of the KA lesion in both groups, and the HFO rate was significantly higher in the animals that later developed epilepsy. Animals that later developed epilepsy also demonstrated widespread appearance of HFOs, in both the ripple and the fast ripple range, whereas animals that did not develop epilepsy only exhibited changes in the ipsilateral intrahippocampal HFO rate. This study demonstrates an association between an increased rate of widespread HFOs and the later development of epilepsy, suggesting the formation of large-scale distributed pathological networks during epileptogenesis.

High-frequency oscillations in awake patients undergoing brain tumor-related epilepsy surgery.

To examine the relationship between high-frequency oscillations (HFOs) and the presence of preoperative seizures, World Health Organization tumor grade, and isocitrate dehydrogenase 1 (IDH1) mutational status in gliomas. We retrospectively studied intraoperative electrocorticography recorded in 16 patients with brain tumor (12 presenting with seizures) who underwent awake craniotomy and surgical resection between September 2016 and June 2017. The number and distribution of HFOs were determined and quantified visually and with an automated HFO detector. Five patients had low-grade (1 with grade I and 4 with grade II) and 11 had high-grade (6 with grade III and 5 with grade IV) brain tumors. An IDH1 mutation was found in 6 patients. Patients with a history of preoperative seizures were more likely to have HFOs than those without preoperative seizures (9 of 12 vs 0 of 4, p = 0.02). The rate of HFOs was higher in patients with IDH1 mutant (mean 7.2 per minute) than IDH wild-type (mean 2.3 per minute) genotype (p = 0.03). HFOs are common in brain tumor-related epilepsy, and HFO rate may be a useful measure of epileptogenicity in gliomas. Our findings further support the notion that IDH1 mutant genotype is more epileptogenic than IDH1 wild-type genotype gliomas.

Strong coupling between slow oscillations and wide fast ripples in children with epileptic spasms: Investigation of modulation index and occurrence rate.

Epileptic spasms (ES) often become drug-resistant. To reveal the electrophysiological difference between children with ES (ES+) and without ES (ES-), we compared the occurrence rate (OR) of high-frequency oscillations (HFOs) and the modulation index (MI) of coupling between slow and fast oscillations. In ES+, we hypothesized that (1) pathological HFOs are more widely distributed and (2) slow oscillations show stronger coupling with pathological HFOs than in ES-. We retrospectively reviewed 24 children with drug-resistant multilobar onset epilepsy, who underwent intracranial video electroencephalography prior to multilobar resections. We measured the OR of HFOs and determined the electrodes with a high rate of HFOs by cluster analysis. We calculated MI, which reflects the degree of coupling between HFO (ripple/fast ripple [FR]) amplitude and 5 different frequency bands of delta and theta activities (0.5-1 Hz, 1-2 Hz, 2-3 Hz, 3-4 Hz, 4-8 Hz). In ES+ (n = 10), the OR(FRs) , the number of electrodes with high-rate FRs, and the MI(FRs & 3-4 Hz) in all electrodes were significantly higher than in ES- (n = 14). In both the ES+ and ES- groups, MI(ripples/FRs & 3-4 Hz) was the highest among the 5 frequency bands. Within the good seizure outcome group, the OR(FRs) and the MI(FRs & 3-4 Hz) in the resected area in ES+ were significantly higher than in ES- (OR[FRs] , P = .04; MI[FRs & 3-4 Hz] , P = .04). In ES+, the larger number of high-rate FR electrodes indicates more widespread epileptogenicity than in ES-. High values of OR(FRs) and MI(FRs & 3-4 Hz) in ES+ compared to ES- are a signature of the severity of epileptogenicity. We proved that ES+ children who achieved seizure freedom following multilobar resections exhibited strong coupling between slow oscillations and FRs.

Magnetoencephalographic imaging of ictal high-frequency oscillations (80-200Hz) in pharmacologically resistant focal epilepsy.

Specificity of ictal high-frequency oscillations (HFOs) in identifying epileptogenic abnormality is significant, compared to the spikes and interictal HFOs. The objectives of the study were to detect and to localize ictal HFOs by magnetoencephalography (MEG) for identifying the seizure onset zone (SOZ), evaluate the cortical excitability from preictal to ictal transition, and establish HFO concordance rates with other modalities and postsurgical resection. Sixty-seven patients with drug-resistant epilepsy had at least 1 spontaneous seizure each during MEG acquisition, and analysis was carried out on 20 seizures from 20 patients. Ictal MEG data were bandpass filtered (80-200Hz) to visualize, review, and analyze the HFOs co-occurring with ictal spikes. Source montages were generated on both hemispheres, mean fast Fourier transform was computed on virtual time series for determining the preictal to ictal spectral power transition, and source reconstruction was performed with sLORETA and beamformers. The concordance rates of ictal MEG HFOs (SOZ) was estimated with 4 reference epileptogenic regions. In each subject, transient bursts of high-frequency oscillatory cycles, distinct from the background activity, were observed in the periictal continuum. Time-frequency analysis showed significant spectral power surge (85-160Hz) during ictal state (P<.05) compared to preictal state, but there was no variation in the peak HFO frequencies (P>.05) for each subgroup and at each source montage. HFO source localization was consistent between algorithms (k=0.857±0.138), with presumed epileptogenic zone (EZ) comparable to other modalities. In patients who underwent surgery (n=6), MEG HFO SOZ was concordant with the presumed EZ and the surgical resection site (100%), and all were seizure-free during follow-up. HFOs could be detected in the MEG periictal state, and its sources were accurately localized. During preictal to ictal transition, HFOs exhibited dynamic augmentation in intrinsic epileptogenicity. Spatial overlap of ictal HFO sources was consistent with EZ determinants and the surgical resection area.

Physiological and pathological high-frequency oscillations have distinct sleep-homeostatic properties.

ObjectiveThe stage of sleep is a known modulator of high-frequency oscillations (HFOs). For instance, high amplitude slow waves during NREM sleep and the subtypes of REM sleep were shown to contribute to a better separation between physiological and pathological HFOs. This study investigated rates and spatial spread of the different HFO types (physiological and pathological ripples in the 80–250 Hz frequency band, and fast ripples above 250 Hz) depending on time spent in sleep across the different sleep cycles.MethodsFifteen patients with focal pharmaco-resistant epilepsy underwent one night of video-polysomnography during chronic intracranial EEG recording for presurgical epilepsy evaluation. The HFO rate and spread across the different sleep cycles were determined with an automatic HFO detector. We built models to explain the observed rate and spread based on time in sleep and other variables i.e. sleep stage, delta band and sigma band activity, and slow wave amplitude. Statistical significance of the different variables was determined by a model comparison using the Akaike information criterion.ResultsThe rate of HFOs depends significantly on the accumulated time of sleep. As the night advanced, the rate of pathological ripples and fast ripples decreased during NREM sleep (up to 15% per hour spent in the respective sleep stages), while the rate of physiological ripples increased during REM sleep (8% per hour spent in REM sleep). Interestingly, the stage of sleep but not the sleep cycle determined the extent of spread of HFOs, showing a larger field during NREM sleep and a more restricted field during REM sleep.ConclusionThe different dependence with sleep time for physiological and pathological ripples is in keeping with their distinct underlying generating mechanisms. From a practical point of view, the first sleep cycle seems to be best suitable for studying HFOs in epilepsy, given that the contrast between physiological and pathological ripple rates is largest during this time.

Interictal high-frequency oscillations generated by seizure onset and eloquent areas may be differentially coupled with different slow waves.

High-frequency oscillations (HFOs) can be spontaneously generated by seizure-onset and functionally-important areas. We determined if consideration of the spectral frequency bands of coupled slow-waves could distinguish between epileptogenic and physiological HFOs. We studied a consecutive series of 13 children with focal epilepsy who underwent extraoperative electrocorticography. We measured the occurrence rate of HFOs during slow-wave sleep at each electrode site. We subsequently determined the performance of HFO rate for localization of seizure-onset sites and undesirable detection of nonepileptic sensorimotor-visual sites defined by neurostimulation. We likewise determined the predictive performance of modulation index: MI(XHz)&(YHz), reflecting the strength of coupling between amplitude of HFOsXHz and phase of slow-waveYHz. The predictive accuracy was quantified using the area under the curve (AUC) on receiver-operating characteristics analysis. Increase in HFO rate localized seizure-onset sites (AUC⩾0.72; p<0.001), but also undesirably detected nonepileptic sensorimotor-visual sites (AUC⩾0.58; p<0.001). Increase in MI(HFOs)&(3-4Hz) also detected both seizure-onset (AUC⩾0.74; p<0.001) and nonepileptic sensorimotor-visual sites (AUC⩾0.59; p<0.001). Increase in subtraction-MIHFOs [defined as subtraction of MI(HFOs)&(0.5-1Hz) from MI(HFOs)&(3-4Hz)] localized seizure-onset sites (AUC⩾0.71; p<0.001), but rather avoided detection of nonepileptic sensorimotor-visual sites (AUC⩽0.42; p<0.001). Our data suggest that epileptogenic HFOs may be coupled with slow-wave3-4Hz more preferentially than slow-wave0.5-1Hz, whereas physiologic HFOs with slow-wave0.5-1Hz more preferentially than slow-wave3-4Hz during slow-wave sleep. Further studies in larger samples are warranted to determine if consideration of the spectral frequency bands of slow-waves coupled with HFOs can positively contribute to presurgical evaluation of patients with focal epilepsy.

1-A-D-22. Sleep stages affect the occurrence of high frequency oscillations (HFOs) differently between hippocampus and neocortical areas

This study was aimed to reveal that influence of sleep stages on the occurrence of HFOs was different between anatomical areas. Nine patients underwent extraoperative intracranial EEG monitoring from both hippocampal depth and neocortical subdural electrodes. Interictal HFOs were detected semi-automatically on EEGs taken during different sleep stages. The difference in the occurrence rate of HFOs between NREM3 and REM sleep stages was normalized per patient, and compared between hippocampus and neocortical electrodes. Number of electrodes showing HFOs at higher rate than standard range was compared between sleep stages and anatomical sites. Hippocampal electrodes ( n = 57) showed higher rate of HFOs during NREM3 sleep than neocortical electrodes ( n = 638) ( p p

Temporal and spatial characteristics of high frequency oscillations as a new biomarker in epilepsy.

Interictal high frequency oscillations (HFOs) are a promising candidate as a biomarker in epilepsy as well as for defining the seizure-onset zone as for the prediction of the surgical outcome after epilepsy surgery. The purpose of the study is to investigate properties of HFOs in long-term recordings with respect to the sleep-wake cycle and anatomic regions to verify previous results based on observations from short intervals and patients mainly with temporal lobe epilepsy to the analysis of hours of recordings and focal epilepsies with extratemporal origin. Automatic HFO detection using a radial basis function neural network detector was performed in long-term recordings of 15 presurgical patients investigated with subdural strip, grid, and depth contacts. Periods with visual marked sleep stages based on parallel scalp recordings from two consecutive nights were compared to awake intervals. Statistical analysis was based on the Kruskal-Wallis test, Mann-Whitney U-test and Spearman's rank correlations. HFO rates in seizure-onset contacts differed from other brain regions independent of the sleep-wake cycle. For temporal contacts, the HFO rate increased significantly with sleep stage. In addition, contacts covering the parietal lobe, including rolandic cortex, showed a significant increase of HFO rates during sleep. However, no significant HFO rate changes depending on the sleep-wake cycle were found for frontal contacts. The rate of interictal HFOs predicted the SOZ with statistical significance at the group level, but properties other than the HFO rate may need to be considered to improve the diagnostic utility of HFOs. This study gives evidence that the modulation of HFO rates by states of the sleep-wake cycle has particular characteristics within different neocortical regions and in mesiotemporal structures, and contributes to the establishment of HFOs as a biomarker in epilepsy.