Self-Supervised Data-Driven Approach Defines Pathological High-Frequency Oscillations in Human

ObjectivePathological high-frequency oscillations (HFOs) in intracranial EEG are promising biomarkers of epileptogenic tissue, and their physiological counterparts play a role in sensorimotor and cognitive function. HFOs have also been found in scalp EEG, but an overview of all studies is lacking. In this systematic review, we assessed the methodology to detect scalp HFOs and their clinical potential. MethodsWe searched PubMed, Embase and the Cochrane Library for studies on HFOs in scalp EEG, and extracted methodological and clinical data. ResultsWe included 60 studies with data from 1149 unique individuals. Two-thirds of studies analyzed HFOs visually in the time or time–frequency domain, and one-third automatically with visual validation. Most studies evaluated interictal ripples during sleep in children. Pathological HFOs were overall better than spikes in localizing the epileptogenic zone and predicting outcome, correlated negatively with cognition and positively with disease activity and severity, and decreased after medical and surgical treatment. ConclusionsThe methodologies of the 60 studies were heterogeneous, but pathological scalp HFOs were clinically valuable as biomarkers in various situations, particularly in children with epilepsy. SignificanceThis systematic review gives an extensive overview of methodological and clinical data on scalp HFOs, establishing their clinical potential and discussing their limitations.

Pathological high frequency oscillations associate with increased GABA synaptic activity in pediatric epilepsy surgery patients

In recent decades, a significant body of evidence based on invasive clinical research has showed that high-frequency oscillations (HFOs) are a promising biomarker for localization of the seizure onset zone (SOZ), and therefore, have the potential to improve postsurgical outcomes in patients with epilepsy. Emerging clinical literature has demonstrated that HFOs can be recorded noninvasively using methods such as scalp electroencephalography (EEG) and magnetoencephalography (MEG). Not only are HFOs considered to be a useful biomarker of the SOZ, they also have the potential to gauge disease severity, monitor treatment, and evaluate prognostic outcomes. In this article, we review recent clinical research on noninvasively detected HFOs in the human brain, with a focus on epilepsy. Noninvasively detected scalp HFOs have been investigated in various types of epilepsy. HFOs have also been studied noninvasively in other pathologic brain disorders, such as migraine and autism. Herein, we discuss the challenges reported in noninvasive HFO studies, including the scarcity of MEG and high-density EEG equipment in clinical settings, low signal-to-noise ratio, lack of clinically approved automated detection methods, and the difficulty in differentiating between physiologic and pathologic HFOs. Additional studies on noninvasive recording methods for HFOs are needed, especially prospective multicenter studies. Further research is fundamental, and extensive work is needed before HFOs can routinely be assessed in clinical settings; however, the future appears promising.

Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.

Objective.Accurate localization of the seizure onset zone (SOZ) is critical for successful surgical intervention in drug-resistant epilepsy. high frequency oscillation (HFO)-based SOZ localization methods often exhibit limited accuracy, which constrains their ability to reliably track large-scale and dynamic brain network activity. Microstate analysis captures dynamic spatiotemporal patterns of epileptogenic networks, improving localization. Propagation zone electrodes can mimic SOZ activity and complicate mapping. This study aims to enhance SOZ precision by combining microstate analysis with a Neuro Rank scoring system to eliminate false positives including propagation zone electrodes while prioritizing true SOZ regions.Approach.We developed a framework integrating HFO detection, microstate analysis, and Neuro Rank-based propagation zone refinement. HFOs were detected and characterized using time-frequency features and clustered via fuzzy C-means. Microstate analysis identified dominant spatiotemporal patterns using global field power (GFP) peaks. Electrodes identified by both methods were refined using Neuro Rank, which integrates PageRank (PR), reverse PR, and hyperlink-induced topic search to down-weight propagation zone electrodes while highlighting true SOZ activity.Main results.Using a leave-one-patient-out evaluation, the proposed pipeline combining HFO, microstate features, and Neuro Rank achieved the best performance on the Freiburg dataset with 95.71% accuracy, 74.83% sensitivity, 98.56% specificity, and an Area Under the receiver operating characteristic Curve (AUC) of 0.83, while demonstrating strong generalization on the external HUP dataset with 96.34% accuracy, 72.34% sensitivity, 97.88% specificity, and an AUC of 0.80.Significance.Combining local HFO biomarkers, microstate-derived network dynamics, and Neuro Rank suppression of propagation zone electrodes provides precise and reliable SOZ localization, supporting improved surgical planning and outcomes in drug-resistant epilepsy.Terms-SOZ localization, intracranial EEG, HFOs, EEG microstate analysis, Neuro Rank graph score.

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.

Ictal wideband ECoG: Direct comparison between ictal slow shifts and high frequency oscillations

Graph index complexity as a novel surrogate marker of high frequency oscillations in delineating the seizure onset zone

Intracranial electroencephalography (iEEG) has been the mainstay of identifying the seizure onset zone (SOZ), a key diagnostic procedure in addition to neuroimaging when considering epilepsy surgery. In many patients, iEEG has been the basis for resective epilepsy surgery, to date still the most successful treatment for drug-resistant epilepsy. Intracranial EEG determines the location and resectability of the SOZ. Advances in recording and implantation of iEEG provide multiple options in the 21st century. This not only includes the choice between subdural electrodes (SDE) and stereoelectroencephalography (SEEG) but also includes the implantation and recordings from microelectrodes. Before iEEG implantation, especially in magnetic resonance imaging -negative epilepsy, a clear hypothesis for seizure generation and propagation should be based on noninvasive methods. Intracranial EEG implantation should be planned by a multidisciplinary team considering epileptic networks. Recordings from SDE and SEEG have both their advantages and disadvantages. Stereo-EEG seems to have a lower rate of complications that are clinically significant, but has limitations in spatial sampling of the cortical surface. Stereo-EEG can sample deeper areas of the brain including deep sulci and hard to reach areas such as the insula. To determine the epileptogenic zone, interictal and ictal information should be taken into consideration. Interictal spiking, low frequency slowing, as well as high frequency oscillations may inform about the epileptogenic zone. Ictally, high frequency onsets in the beta/gamma range are usually associated with the SOZ, but specialized recordings with combined macro and microelectrodes may in the future educate us about onset in higher frequency bands. Stimulation of intracranial electrodes triggering habitual seizures can assist in identifying the SOZ. Advanced computational methods such as determining the epileptogenicity index and similar measures may enhance standard clinical interpretation. Improved techniques to record and interpret iEEG may in the future lead to a greater proportion of patients being seizure free after epilepsy surgery.

The epileptic network hypothesis and epileptogenic zone hypothesis are two theories of ictogenesis. The network hypothesis posits that coordinated activity among interconnected nodes produces seizures. The epileptogenic zone hypothesis posits that distinct regions are necessary and sufficient for seizure generation. High-frequency oscillations, and particularly fast ripples, are thought to be biomarkers of the epileptogenic zone. We sought to test these theories by comparing high-frequency oscillation rates and networks in surgical responders and non-responders, with no appreciable change in seizure frequency or severity, within a retrospective cohort of 48 patients implanted with stereo-EEG electrodes. We recorded inter-ictal activity during non-rapid eye movement sleep and semi-automatically detected and quantified high-frequency oscillations. Each electrode contact was localized in normalized coordinates. We found that the accuracy of seizure onset zone electrode contact classification using high-frequency oscillation rates was not significantly different in surgical responders and non-responders, suggesting that in non-responders the epileptogenic zone partially encompassed the seizure onset zone(s) (P > 0.05). We also found that in the responders, fast ripple on oscillations exhibited a higher spectral content in the seizure onset zone compared with the non-seizure onset zone (P < 1 × 10−5). By contrast, in the non-responders, fast ripple had a lower spectral content in the seizure onset zone (P < 1 × 10−5). We constructed two different networks of fast ripple with a spectral content >350 Hz. The first was a rate–distance network that multiplied the Euclidian distance between fast ripple-generating contacts by the average rate of fast ripple in the two contacts. The radius of the rate–distance network, which excluded seizure onset zone nodes, discriminated non-responders, including patients not offered resection or responsive neurostimulation due to diffuse multifocal onsets, with an accuracy of 0.77 [95% confidence interval (CI) 0.56–0.98]. The second fast ripple network was constructed using the mutual information between the timing of the events to measure functional connectivity. For most non-responders, this network had a longer characteristic path length, lower mean local efficiency in the non-seizure onset zone, and a higher nodal strength among non-seizure onset zone nodes relative to seizure onset zone nodes. The graphical theoretical measures from the rate–distance and mutual information networks of 22 non- responsive neurostimulation treated patients was used to train a support vector machine, which when tested on 13 distinct patients classified non-responders with an accuracy of 0.92 (95% CI 0.75–1). These results indicate patients who do not respond to surgery or those not selected for resection or responsive neurostimulation can be explained by the epileptic network hypothesis that is a decentralized network consisting of widely distributed, hyperexcitable fast ripple-generating nodes.

Event Abstract Back to Event Localization of interictal spikes and high frequency oscillations (HFO) using magnetoencephalography (MEG) Vahe Poghosyan1* 1 King Fahad Medical City, Department of Neurophysiology, National Neuroscience Institute, Saudi Arabia Epidemiological data show that about 40% of the patients with epilepsy are refractory to pharmacological treatment. In many such patients with focal epilepsy, an alternative therapeutic option of choice is the surgical resection of the epileptogenic zone. At present, a variety of diagnostic tools (e.g. analysis of seizure semiology, electrophysiological and neuroimaging tools, functional testing etc.) are used to define the different epilepsy-related cortical zones (symptomatogenic, irritative, seizure onset and functional deficit zones, and epileptogenic lesion), each of which indexes to a certain extent the location and boundaries of the epileptogenic zone. Recently, high frequency oscillations (HFO, 80 – 500 Hz) have been proposed as accurate biomarkers of the epileptogenic zone in intracranial electroencephalography recordings (Jacobs et al. 2012). Magnetoencephalography (MEG) substantially contributes to the pre-surgical evaluation of patients with focal epilepsy. Currently, its primary application is to localize the cortical sources of interictal spikes (irritative zone) as an estimate for the epileptogenic zone. Recent studies have shown that MEG can also be used to identify and localize sources of HFOs (Papadelis et al., 2009; Rampp et al., 2010). Accurate identification and localization of MEG HFOs can provide an important non-invasive biomarker for epileptogenicity. We have used several source localization methods to estimate and compare the sources of MEG interictal spikes and HFOs, and correlate them with the clinical information and post-surgical outcome. In this presentation, I will describe the results of this analysis and provide relevant recommendations. References Jacobs J., Staba R., Asano E., Otsubo H., Wu J.Y., Zijlmans M., Mohamed I., Kahane P., Dubeau F., Navarro V., and Gotman J. (2012). High-frequency oscillations (HFOs) in clinical epilepsy. Progress in neurobiology, 98: 302–315 Papadelis C., Poghosyan V., Fenwick P.B.C., and Ioannides A.A. (2009). MEG’s ability to localize accurately weak transient neural sources. Clinical Neurophysiology, 120: 1958–1970 Rampp S., Kaltenhäuser M., Weigel D., Buchfelder M., Blümcke I., Dörfler A., and Stefan, H. (2010). MEG correlates of epileptic high gamma oscillations in invasive EEG. Epilepsia, 51: 1638–1642. Keywords: Epilepsy, interictal epileptiform discharges, interictal spikes, Magnetoencephalography (MEG), high frequency oscillation Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016. Presentation Type: Oral Presentation in the Symposium Epilepsy and Sleep Topic: Symposium Epilepsy and Sleep Citation: Poghosyan V (2016). Localization of interictal spikes and high frequency oscillations (HFO) using magnetoencephalography (MEG). Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00122 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 01 Aug 2016; Published Online: 01 Aug 2016. * Correspondence: Dr. Vahe Poghosyan, King Fahad Medical City, Department of Neurophysiology, National Neuroscience Institute, Riyadh, Saudi Arabia, vpoghosyan@kfmc.med.sa Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Vahe Poghosyan Google Vahe Poghosyan Google Scholar Vahe Poghosyan PubMed Vahe Poghosyan Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

To investigate the characteristics of intracranial ictal high-frequency oscillations (HFOs). Among neocortical epilepsy patients who underwent intracranial monitoring and surgery, we studied patients with well-defined, unifocal seizure onsets characterized by discrete HFOs (≥70 Hz). Patients with multifocal or bilateral independent seizure onsets, electroencephalography (EEG) acquired at <1,000 Hz sampling rate, and nonresective surgery were excluded. Based on a prospectively defined protocol, we defined the seizure-onset zone (SOZ) presurgically to include only those channels with HFOs that showed subsequent sustained evolution (HFOs+ channels) but not the channels that lacked evolution (HFOs- channels). We then resected the SOZ as defined above, 1 cm of the surrounding cortex, and immediate spread area, modified by the presence of eloquent cortex in the vicinity. For purposes of this study, we also defined the SOZ based on the conventional frequency activity (CFA, <70 Hz) at seizure onset, although that information was not considered for preoperative determination of the surgical boundary. We investigated the temporal and spatial characteristics of the ictal HFOs post hoc by visual and spectral methods, and also compared them to the seizure onset defined by the CFA. Of 14 consecutive neocortical epilepsy patients, six patients met the inclusion criteria. Magnetic resonance imaging (MRI) was normal or showed heterotopia. All had subdural electrodes, with additional intracerebral depth electrodes in some. Electrode coverage was extensive (median 94 channels), including limited contralateral coverage. Seizure onsets were lobar or multilobar. Resections were performed per protocol, except in two patients where complete resection of the SOZ could not be done due to overlap with speech area. Histology was abnormal in all patients. Postoperative outcome was class I/II (n = 5, 83%) or class III over a mean follow-up of 27 months. Post hoc analysis of 15 representative seizures showed that the ictal HFOs were widespread at seizure onset but evolved subsequently with different characteristics. In contrast to HFOs-, the HFOs+ were significantly higher in peak frequency (97.1 vs. 89.1 Hz, p = 0.001), more robust (nearly twofold higher peak power, p < 0.0001), and spatially restricted [mean 12.2 vs. 22.4 channels; odds ratio (OR) 0.51, 95% confidence interval (CI) 0.42-0.62; p < 0.0001]. The seizure onset defined by HFOs+ was earlier (by an average of 0.41 s), and occurred in a significantly different and smaller distribution (OR 0.27, 95% CI 0.21-0.34, p < 0.0001), than the seizure onset defined by the CFA. As intended, the HFOs+ channels were 10 times more likely to have been resected than the HFOs- channels (OR 9.7, 95% CI 5-17, p < 0.0001). Our study demonstrates the widespread occurrence of ictal HFOs at seizure onset, outlines a practical method to localize the SOZ based on their restricted pattern of evolution, and highlights the differences between the SOZs defined by HFOs and CFA. We show that smaller resections, restricted mainly to the HFOs channels with evolution, can lead to favorable seizure outcome. Our findings support the notion of widespread epileptic networks underlying neocortical epilepsy.

Interictal high-frequency oscillations over 200 Hz have been recorded with microelectrodes in the seizure onset zone of epileptic patients suffering from mesial temporal lobe epilepsy. Recent work suggests that similar high-frequency oscillations can be detected in the seizure onset zone using standard diagnostic macroelectrodes. However, only a few channels were examined in these studies, so little information is available on the spatial extent of high-frequency oscillations. Here, we present data on high-frequency oscillations recorded from a larger number of intracerebral contacts spatial (mean 38) in 16 patients. Data were obtained from 1 h of interictal recording sampled at 1024 Hz and was analysed using a new semi-automatic detection procedure based on a wavelet decomposition. A detailed frequency analysis permitted a rapid and reliable discrimination of high-frequency oscillations from other high-frequency events. A total of 1932 high-frequency oscillations were detected with an average frequency of 261 +/- 53 Hz, amplitude of 11.9 +/- 6.7 microV and duration of 22.7 +/- 11.6 ms. Records from a patient often showed several different high-frequency oscillation patterns. We classified 24 patterns from 11 patients. Usually (20/24 patterns) high-frequency oscillations were nested in an epileptic paroxysm, such as a spike or a sharp wave, and typically high-frequency oscillations (19/24) were recorded from just one recording contact. Unexpectedly in other cases, high-frequency oscillations (5/24) were detected simultaneously on two or three contacts, sometimes separated by large distances. This large spatial extent suggests that high-frequency oscillations may sometimes result from a neuronal synchrony manifest on a scale of centimetres. High-frequency oscillations were almost always recorded in seizure-generating structures of patients suffering from mesial (9/9) or polar (1/3) temporal lobe epilepsy. They were never found in the epileptic or healthy basal, lateral temporal or extra temporal neocortex nor in the healthy amygdalo-hippocampal complex. These findings confirm that the generation of oscillations at frequencies higher that 200 Hz is, at this scale, a specific, intrinsic property of seizure-generating networks in medial and polar temporal lobes, which have a common archaic phylogenetic origin. We show that this activity can be detected and its spatial extent determined with conventional intracranial electroencephalography electrodes in records from patients with temporal lobe epilepsy. It is a reliable marker of the seizure onset zone that should be considered in decisions on surgical treatment.

Objective. High-frequency oscillations (HFOs) are promising biomarkers for localizing epileptogenic brain tissue. Previous studies have revealed that HFOs that present concurrence with interictal epileptic discharges (IEDs) better delineate epileptogenic brain tissue, particularly for epilepsy patients with multitype interictal discharges. However, the analysis of noninvasively recorded epileptic HFOs involves many complex procedures, such as data preprocessing, detection and source localization, impeding the translation of this approach to clinical practice. Approach. To address these problems, we developed a graphical user interface (GUI)-based pipeline called EMHapp, which can be used for the automatic detection, source localization and visualization of HFO events concurring with IEDs in magnetoencephalography (MEG) signals by using a beamformer-based virtual sensor (VS) technique. An improved VS reconstruction method was developed to enhance the amplitudes of both HFO and IED VS signals. To test the capability of our pipeline, we collected MEG data from 11 complex focal epilepsy patients with surgical resections or seizure onset zones (SOZs) that were identified by intracranial electroencephalography. Main results. Our results showed that the HFO sources of eight patients were concordant with their resection margins or SOZs. Our proposed VS signal reconstruction approach achieved an 83.2% improvement regarding the number of detected HFO events and a 17.3% improvement in terms of the spatial overlaps between the HFO sources and the resection margins or SOZs in comparison with conventional VS reconstruction approaches. Significance. EMHapp is the first GUI-based pipeline for the analysis of epileptic magnetoencephalographic HFOs, which conveniently obtains HFO source locations using clinical data and enables direct translation to clinical applications.

The seizure onset zone (SOZ) and propagation pathways in supplementary motor area (SMA) seizures are difficult to identify because of the short duration and swift propagations of this seizure type. Herein, we studied ictal cerebral blood flow changes, seizure electrical activities, and clinical signs employing simultaneous near-infrared spectroscopy (NIRS) and electrocorticography (ECoG) recordings in a brain tumor patient with SMA seizures. Increased cerebral blood flow was observed from the SOZ in the ipsilateral SMA and spread to the ipsilateral premotor cortex, ipsilateral sensorimotor cortex, and also the contralateral hemisphere. These propagation patterns were concordant with ictal ECoG seizure activities and clinical signs. Removal of the SOZ and surrounding areas, where the higher blood flow and higher frequency oscillations had been identified, achieved a good outcome for this patient. Our ictal NIRS-ECoG technique is robust and useful for detecting the SOZ and seizure propagations.