Interictal High Frequency Oscillations Research Articles

Classifying High-Frequency Oscillations by Morphologic Contrast to Background, With Surgical Outcome Correlates.

Ictal high-frequency oscillations (HFOs) are a reliable indicator of a seizure onset zone for intracranial EEG recordings. Interictal HFOs often are also observed and may be a useful biomarker to supplement ictal data, but distinguishing pathologic from physiologic HFOs continues to be a challenging task. We present a method of classifying HFOs based on morphologic contrast to the background. We retrospectively screened 31 consecutive patients who underwent intracranial recordings for epilepsy at Stanford Medical Center during a 2-year period, and 13 patients met the criteria for inclusion. Interictal EEG data were analyzed using an automated event detector followed by morphologic feature extraction and k-means clustering. Instead of only using event features, the algorithm also incorporated features of the background adjacent to the events. High-frequency oscillations with higher morphologic contrast to the background were labeled as pathologic, and "hotspots" with the most active pathologic HFOs were identified and compared with clinically determined seizure onset zones. Clustering with contrast features produced groups with better separation and more consistent boundaries. Eleven of the 13 patients proceeded to surgery, and patients whose hotspots matched seizure onset zones had better outcomes, with 4 out of 5 "match" patients having no disabling seizures at 1+ year postoperatively (Engel I or International League Against Epilepsy Class 1-2), while all "mismatch" patients continued to have disabling seizures (Fisher exact test P-value = 0.015). High-frequency oscillations with higher contrast to background more likely represent paroxysmal bursts of pathologic activity. Patients with HFO hotspots outside of identified seizure onset zones may not respond as well to surgery.

Automated Detection of Interictal High-frequency Oscillations for Epileptogenic Zone Localization

Automated Detection of Interictal High-frequency Oscillations for Epileptogenic Zone Localization

Interictal High-Frequency Oscillations, Spikes, and Connectivity Profiles: a Fingerprint of Epileptogenic Brain Pathologies.

Focal cortical dysplasia (FCD), hippocampal sclerosis (HS), nonspecific gliosis (NG), and normal tissue (NT) comprise the majority of histopathological results of surgically treated drug-resistant epilepsy patients. Epileptic spikes, high-frequency oscillations (HFOs), and connectivity measures are valuable biomarkers of epileptogenicity. The question remains whether they could also be utilized for pre-resective differentiation of the underlying brain pathology. This study explored spikes and HFOs together with functional connectivity in various epileptogenic pathologies. Interictal awake SEEG recordings of 33 patients with focal drug-resistant epilepsy with seizure-free postoperative outcomes were analyzed (15 FCD, 8 HS, 6 NT, and 4 NG). Interictal spikes and HFOs were automatically identified in the channels contained in the overlap of seizure onset zone (SOZ) and resected tissue. Functional connectivity measures (relative entropy, linear correlation, cross-correlation, and phase consistency) were computed for neighboring electrode pairs. Statistically significant differences were found between the individual pathologies in HFO rates, spikes, and their characteristics, together with functional connectivity measures with the highest values in the case of HS and NG/NT. A model to predict brain pathology based on all interictal measures achieved up to 84.0% prediction accuracy. The electrophysiological profile of the various epileptogenic lesions in epilepsy surgery patients was analyzed. Based on this profile, a predictive model was developed. This model offers excellent potential to identify the nature of the underlying lesion prior to resection. If validated, this model may be particularly valuable for counseling patients, as depending on the lesion type, different outcomes are achieved after epilepsy surgery.

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy

ObjectiveThis study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery. MethodsWe analyzed interictal HFOs (80–500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time–frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. ResultsThe MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. ConclusionsHFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SignificanceEnhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.

Intracranial electrophysiological recordings on a swine model of mesial temporal lobe epilepsy

To test the feasibility and reliability of intracranial electrophysiological recordings in an acute status epilepticus model on laboratory swine. Intrahippocampal injection of kainic acid (KA) was performed on 17 male Bama pigs (Sus scrofa domestica) weighing between 25 and 35 kg. Two stereoelectroencephalography (SEEG) electrodes with a total of 16 channels were implanted bilaterally along the sensorimotor cortex to the hippocampus. Brain electrical activity was recorded 2 h daily for 9-28 days. Three KA dosages were tested to evaluate the quantities capable of evoking status epilepticus. Local field potentials (LFPs) were recorded and compared before and after the KA injection. We quantified the epileptic patterns, including the interictal spikes, seizures, and high-frequency oscillations (HFOs), up to 4 weeks after the KA injection. Test-retest reliability using intraclass correlation coefficients (ICCs) were performed on interictal HFO rates to evaluate the recording stability of this model. The KA dosage test suggested that a 10 μl (1.0 μg/μl) intrahippocampal injection could successfully evoke status epilepticus lasting from 4 to 12 h. At this dosage, eight pigs (50% of total) had prolonged epileptic events (tonic-chronic seizures + interictal spikes n = 5, interictal spikes alone n = 3) in the later 4 weeks of the video-SEEG recording period. Four pigs (25% of total) had no epileptic activities, and another four (25%) had lost the cap or did not complete the experiments. Animals that showed epileptiform events were grouped as E + (n = 8) and the four animals showing no signs of epileptic events were grouped as E- (n = 4). A total of 46 electrophysiological seizures were captured in the 4-week post-KA period from 4 E + animals, with the earliest onset on day 9. The seizure durations ranged from 12 to 45 s. A significant increase of hippocampal HFOs rate (num/min) was observed in the E+ group during the post-KA period (weeks 1, 2,4, p < 0.05) compared to the baseline. But the E-showed no change or a decrease (in week 2, p = 0.43) compared to their baseline rate. The between-group comparison showed much higher HFO rates in E + vs. E - (F = 35, p < 0.01). The high ICC value [ICC (1, k) = 0.81, p < 0.05] quantified from the HFO rate suggested that this model had a stable measurement of HFOs during the four-week post-KA periods. This study measured intracranial electrophysiological activity in a swine model of KA-induced mesial temporal lobe epilepsy (mTLE). Using the clinical SEEG electrode, we distinguished abnormal EEG patterns in the swine brain. The high test-retest reliability of HFO rates in the post-KA period suggests the utility of this model for studying mechanisms of epileptogenesis. The use of swine may provide satisfactory translational value for clinical epilepsy research.

Interictal HFO and FDG-PET correlation predicts surgical outcome following SEEG.

This study aimed to investigate the quantitative relationship between interictal 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and interictal high-frequency oscillations (HFOs) from stereo-electroencephalography (SEEG) recordings in patients with refractory epilepsy. We retrospectively included 32 patients. FDG-PET data were quantified through statistical parametric mapping (SPM) t test modeling with normal controls. Interictal SEEG segments with four, 10-min segments were selected randomly. HFO detection and classification procedures were automatically performed. Channel-based HFOs separating ripple (80-250 Hz) and fast ripple (FR; 250-500 Hz) counts were correlated with the surrounding metabolism T score at the individual and group level, respectively. The association was further validated across anatomic seizure origins and sleep vs wake states. We built a joint feature FR × T reflecting the FR and hypometabolism concordance to predict surgical outcomes in 28 patients who underwent surgery. We found a negative correlation between interictal FDG-PET and HFOs through the linear mixed-effects model (R2 =.346 and .457 for ripples and FRs, respectively, p < .001); these correlations were generalizable to different epileptogenic-zone lobar localizations and vigilance states. The FR × T inside the resection volume could be used as a predictor for surgical outcomes with an area under the curve of 0.81. The degree of hypometabolism is associated with HFO generation rate, especially for FRs. This relationship would be meaningful for selection of SEEG candidates and for optimizing SEEG scheme planning. The concordance between FRs and hypometabolism inside the resection volume could provide prognostic information regarding surgical outcome.

Characterizing physiological high-frequency oscillations using deep learning

Objective. Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning (DL). Approach. We studied children with neocortical epilepsy who underwent intracranial strip/grid evaluation. Time-series EEG data were transformed into DL training inputs. The eloquent cortex (EC) was defined by functional cortical mapping and used as a DL label. Morphological characteristics of HFOs obtained from EC (ecHFOs) were distilled and interpreted through a novel weakly supervised DL model. Main results. A total of 63 379 interictal intracranially-recorded HFOs from 18 children were analyzed. The ecHFOs had lower amplitude throughout the 80–500 Hz frequency band around the HFO onset and also had a lower signal amplitude in the low frequency band throughout a one-second time window than non-ecHFOs, resembling a bell-shaped template in the time–frequency map. A minority of ecHFOs were HFOs with spikes (22.9%). Such morphological characteristics were confirmed to influence DL model prediction via perturbation analyses. Using the resection ratio (removed HFOs/detected HFOs) of non-ecHFOs, the prediction of postoperative seizure outcomes improved compared to using uncorrected HFOs (area under the ROC curve of 0.82, increased from 0.76). Significance. We characterized salient features of physiological HFOs using a DL algorithm. Our results suggested that this DL-based HFO classification, once trained, might help separate physiological from pathological HFOs, and efficiently guide surgical resection using HFOs.

0147 Electrophysiologic Localization of Pathological Brain Tissue Is Accomplished with Intracranial Sleep Staging

0147 Electrophysiologic Localization of Pathological Brain Tissue Is Accomplished with Intracranial Sleep Staging

A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG

Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long-term EEG recording. Spiking neural networks (SNN) have emerged as optimal architectures for embedding in compact low-power signal processing hardware. We analyzed 20 scalp EEG recordings from 11 pediatric focal lesional epilepsy patients. We designed a custom SNN to detect events of interest (EoI) in the 80–250 Hz ripple band and reject artifacts in the 500–900 Hz band. We identified the optimal SNN parameters to detect EoI and reject artifacts automatically. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.90 CI [0.75 0.96], p < 0.0001, Spearman’s correlation). The fully automated SNN detected clinically relevant HFO in the scalp EEG. This study is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.

High-frequency oscillations recorded with surface EEG in neonates with seizures

ObjectiveNeonatal seizures are often the first symptom of perinatal brain injury. High-frequency oscillations (HFOs) are promising new biomarkers for epileptogenic tissue and can be found in intracranial and surface EEG. To date, we cannot reliably predict which neonates with seizures will develop childhood epilepsy. We questioned whether epileptic HFOs can be generated by the neonatal brain and potentially predict epilepsy. MethodsWe selected 24 surface EEGs sampled at 2048 Hz with 175 seizures from 16 neonates and visually reviewed them for HFOs. Interictal epochs were also reviewed. ResultsWe found HFOs in thirteen seizures (7%) from four neonates (25%). 5025 ictal ripples (rate 10 to 1311/min; mean frequency 135 Hz; mean duration 66 ms) and 1427 fast ripples (rate 8 to 356/min; mean frequency 298 Hz; mean duration 25 ms) were marked. Two neonates (13%) showed interictal HFOs (285 ripples and 25 fast ripples). Almost all HFOs co-occurred with sharp transients. We could not find a relationship between neonatal HFOs and outcome yet. ConclusionsNeonatal HFOs co-occur with ictal and interictal sharp transients. SignificanceThe neonatal brain can generate epileptic ripples and fast ripples, particularly during seizures, though their occurrence is not common and potential clinical value not evident yet.

Detecting High Frequency Oscillations for Stereoelectroencephalography in Epilepsy via Hypergraph Learning.

Successful epilepsy surgeries depend highly on pre-operative localization of epileptogenic zones. Stereoelectroencephalography (SEEG) records interictal and ictal activities of the epilepsy in order to precisely find and localize epileptogenic zones in clinical practice. While it is difficult to find distinct ictal onset patterns generated the seizure onset zone from SEEG recordings in a confined region, high frequency oscillations are commonly considered as putative biomarkers for the identification of epileptogenic zones. Therefore, automatic and accurate detection of high frequency oscillations in SEEG signals is crucial for timely clinical evaluation. This work formulates the detection of high frequency oscillations as a signal segment classification problem and develops a hypergraph-based detector to automatically detect high frequency oscillations such that human experts can visually review SEEG signals. We evaluated our method on 4,000 signal segments from clinical SEEG recordings that contain both ictal and interictal data obtained from 19 patients who suffer from refractory focal epilepsy. The experimental results demonstrate the effectiveness of the proposed detector that can successfully localize interictal high frequency oscillations and outperforms multiple peer machine learning methods. In particular, the proposed detector achieved 90.7% in accuracy, 80.9% in sensitivity, and 96.9% in specificity.

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.

Association Between Interictal High-Frequency Oscillations and Slow Wave in Refractory Focal Epilepsy With Good Surgical Outcome.

High-frequency oscillations (HFOs) have been proposed as a promising biomarker of the epileptogenic zone (EZ). But accurate delineation of EZ based on HFOs is still challenging. Our study compared HFOs from EZ and non-EZ on the basis of their associations with interictal slow waves, aiming at exploring a new way to localize EZ. Nineteen medically intractable epilepsy patients with good surgical outcome were included. Five minute interictal intracranial electroencephalography (EEG) epochs of slow-wave sleep were randomly selected; then ripples (80–200 Hz), fast ripples (FRs; 200–500 Hz), and slow waves (0.1–4 Hz) were automatically analyzed. The EZ and non-EZ were identified by resection range during the surgeries. We found that both ripples and FRs superimposed more frequently on slow waves in EZ than in non-EZ (P < 0.01). Although ripples preferred to occur on the down state of slow waves in both two groups, ripples in EZ tended to be closer to the down-state peak of slow wave than in non-EZ (-174 vs. -231 ms, P = 0.008). As for FR, no statistical difference was found between the two groups (P = 0.430). Additionally, slow wave-containing ripples in EZ had a steeper slope (1.7 vs. 1.5 μV/ms, P < 0.001) and wider distribution ratio (32.3 vs. 30.1%, P < 0.001) than those in the non-EZ. But for slow wave-containing FR, only a steeper slope (1.7 vs. 1.4 μV/ms, P < 0.001) was observed. Our study innovatively compared the different features of association between HFOs and slow wave in EZ and non-EZ from refractory focal epilepsy with good surgical outcome, proposing a new method to localize EZ and facilitating the surgical plan.

Altered effective connectivity network in patients with insular epilepsy: A high-frequency oscillations magnetoencephalography study

ObjectiveThe project aimed to determine the alterations in the effective connectivity (EC) neural network in patients with insular epilepsy based on interictal high-frequency oscillations (HFOs) from magnetoencephalography (MEG) data. MethodsWe studied MEG data from 22 insular epilepsy patients and 20 normal subjects. Alterations in spatial pattern and connection properties of the patients with insular epilepsy were investigated in the entire brain network and insula-based network. ResultsAnalyses of the parameters of graph theory revealed the over-connectivity and small-world configuration of the global connectivity patterns observed in the patients. In the insula-based network, the insular cortex ipsilateral to the seizure onset displayed increased efferent and afferentEC. Left insular epilepsy featured strong connectivity with the bilateral hemispheres, whereas right insular epilepsy featured increased connectivity with only the ipsilateral hemisphere. ConclusionsPatients with insular epilepsy display alterations in the EC network in terms of both whole-brain connectivity and the insula-based network during interictal HFOs. SignificanceAlterations of interictal HFO-based networks provide evidence that epilepsy networks, instead of epileptic foci, play a key role in the complex pathophysiological mechanisms of insular epilepsy. The dysfunction of HFO networks may prove to be a novel promising biomarker and the cause of interictal brain dysfunctions in insular epilepsy.

Focal cortical dysplasia II-related seizures originate from the bottom of the dysplastic sulcus: A stereoelectroencephalography study

ObjectivesFocal cortical dysplasia (FCD) II is a frequently observed histopathological substrate in epilepsy surgery. In the present study, we explored the spatial distribution of epileptogenic activities across FCD II lesions using stereoelectroencephalography. MethodsPatients with histopathologically confirmed type II FCDs and who had at least one depth electrode that go through the wall of the dysplastic sulcus from the surface to the bottom were included. The dysplastic sulci were divided into the bottom and non-bottom parts manually, and contacts were defined as bottom or non-bottom contacts according to their locations. Factors (bottom location, pathological subtype, magnetic resonance imaging manifestation, and presence of bottom-of-sulcus dysplasia) potentially associated with earliest onset identified by conventional visual analysis, epileptogenicity index (EI), and standardized number of high-frequency oscillations (HFOs) were analyzed. Linear regression analyses between distance (from the location of the analyzed contact to the bottom of the sulcus) and EI value and HFO number were performed. ResultsSixteen patients with 19 depth electrodes containing 112 valid contacts were included. Bottom location was the sole factor significantly associated with earliest onset (P < 0.001), EI value (P < 0.001), and HFO number (P < 0.001). Most earliest onsets were recorded by the bottom contacts, bottom contacts had higher EI value (0.81 ± 0.28 vs. 0.31 ± 0.24, P < 0.001) and more HFOs (0.78 ± 0.28 vs. 0.35 ± 0.31, P < 0.001) than non-bottom contacts. Moreover, the EI value (R = −0.72, P < 0.001) and HFO number (R = −0.64, P < 0.001) were significantly negatively correlated with distance, regardless of histopathological subtype, MRI manifestation, or absence of bottom-of-sulcus dysplasia. ConclusionSeizure onsets and interictal HFOs most often arise from the bottom part of a sulcus with type II FCD. SignificanceThe findings of the present study contribute to intracranial electrode selection, trajectory planning, and, later on, resection of this kind of malformation.

Hippocampal high frequency oscillations in unilateral and bilateral mesial temporal lobe epilepsy

ObjectiveThe main aim of this study was to investigate the potential differences in terms of interictal high frequency oscillations (HFOs) between both hippocampi in unilateral (U-MTLE) and bilateral mesial temporal lobe epilepsy (B-MTLE). MethodsSixteen patients with MTLE underwent bilateral hippocampal depth electrode implantation as part of epilepsy surgery evaluation. Interictal HFOs were detected automatically. The analyses entail comparisons of the rates and spatial distributions of ripples and fast ripples (FR) in hippocampi and amygdalae, with respect to the eventual finding of hippocampal sclerosis (HS). ResultsIn U-MTLE, higher ripple and FR rates were found in the hippocampi ipsilateral to the seizure onset than in the contralateral hippocampi. Non-epileptic hippocampi in U-MTLE were distinguished by significantly lower ripple rate than in the remaining analyzed hippocampi. There were not differences between the hippocampi in B-MTLE. In the hippocampi with proven HS, higher FR rates were observed in the ventral than in the dorsal parts. ConclusionsNon-epileptic hippocampi in U-MTLE demonstrated significantly lower ripple rates than those epileptic in U-MTLE and B-MTLE. SignificanceLow interictal HFO occurrence might be considered as a marker of the non-epileptic hippocampi in MTLE.

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.

High-frequency oscillations and spikes running down after SEEG-guided thermocoagulations in the epileptogenic network of periventricular nodular heterotopia

ObjectiveEpilepsy associated with periventricular nodular heterotopia (PNH) is characterized by complex relationships between the heterotopic and the normotopic cortex during the interictal state and at seizure onset. High-frequency oscillations (HFO) have been proposed as a marker of epileptogenicity that might reflect disease activity. The effects of thermocoagulations on epileptogenicity in this context remain unknown. We aimed to investigate the interictal HFO- and spike profiles of different cortical structures before and after two consecutive SEEG-guided thermocoagulations, in correlation with seizure outcome, in a patient with PNH-related drug-resistant epilepsy. MethodsThe epileptogenic zone (EZ) was defined by SEEG analysis based on the Epileptogenicity Index. Interictal spikes, ripples (80–250 Hz) and fast ripples (FR, 250-330 Hz) were analyzed within the heterotopia, the temporal neocortex and the hippocampus. ResultsThe SEEG recordings revealed a distributed EZ involving the heterotopia and the posterior temporal neocortex. Both structures were targeted by thermocoagulations. Background spikes, ripples and FR-rates were significantly higher in PNH compared to the normotopic cortex. A drastic reduction of spikes (by over 80%) and absence of FR were demonstrated both in the PNH and in the neocortex during the second SEEG exploration 6 months after the first thermocoagulation, whereas no significant difference was observed in the posterior hippocampus. Ripples were significantly reduced by the first and suppressed by the second thermocoagulation within the three structures. Seizures relapsed after two months but decreased in frequency after the first thermocoagulation. Sustained seizure-freedom was achieved only after the second procedure. ConclusionsOur data demonstrate the running down of interictal HFO and spikes within the epileptogenic network following thermocoagulations of heterotopic and normotopic sites involved at seizure onset. This dynamics was in good correlation with significantly improved seizure control. SignificanceCombination of ictal and different interictal markers of epileptogenicity, including HFO and spike analysis, is important to get the full picture of the epileptogenic zone and could help to evaluate the disease activity.

Detection and localization of interictal ripples with magnetoencephalography in the presurgical evaluation of drug-resistant insular epilepsy

Precise noninvasive presurgical localization of insular epilepsy is important. The objective of the present study was to detect and localize interictal high-frequency oscillations (HFOs) in patients with insular epilepsy at the source levels using magnetoencephalography (MEG). We investigated whether HFOs can delineate epileptogenic areas. We analysed MEG data with new accumulated source imaging (HFOs, 80–250 Hz ripples during spikes) and conventional dipole modelling (spikes) methods for localizing epileptic foci. We evaluated the relationship of the resection of focal brain regions containing interictal HFOs and the spikes with the postsurgical seizure outcome. Interictal HFOs were localized in the insular epileptogenic zone (EZ) in 18 out of 21 patients undergoing surgical treatment for clinically diagnosed insular epilepsy. While dipole clusters of spikes were involved in the insular EZ in 15 patients. Both the HFOs and the dipole cluster were localized in the insula in 14 patients. The seizure-free percentage was 87% for the resection of brain regions generating HFOs, whereas 80% for the resection of brain regions generating spikes. There was a much higher chance of freedom from seizures with complete resection of the HFO-generating regions than with partial resection or no resection (P = 0.031). No such difference was seen for spike-generating regions. Our results suggest that HFOs from insular epilepsy could be noninvasively detected and quantitatively assessed with MEG technology. MEG HFOs (ripples during spikes) may be valuable for the localization of the epileptogenic zone in insular epilepsy.

Using Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery.

For the more than 15 million patients who have drug-resistant epilepsy, surgical resection of the region where seizure arise is often the only alternative therapy. However, the identification of this epileptogenic zone (EZ) is often imprecise. Generally, insufficient EZ identification and resection may cause seizures to continue and too much resection may lead to unnecessary neurological deficits. In this paper, an automatic high frequency oscillations (HFOs) detection method based on noise-assisted multivariate EMD (NA-MEMD) is proposed to improve the localization of the EZ for epilepsy patients. In this method, different detected HFO types such as fast-ripple (FR), ripple (R), and fast-ripple concurrent with ripple (FRandR) are utilized to investigate their clinical relevance in identifying EZ. The proposed method may significantly improve the precision by which pathological brain tissue can be identified.