Spatial characterization of interictal high frequency oscillations in epileptic neocortex

High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?

Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.

The hemodynamic response to co-occurring interictal epileptiform discharges and high-frequency oscillations localizes the seizure-onset zone

OBJECTIVE

To use intracranial electroencephalography (EEG) to characterize functional magnetic resonance imaging (fMRI) activation maps associated with high-frequency oscillations (HFOs) (80-250 Hz) and examine their proximity to HFO- and seizure-generating tissue.

METHODS

Forty-five patients implanted with intracranial depth electrodes underwent a simultaneous EEG-fMRI study at 3 T. HFOs were detected algorithmically from cleaned EEG and visually confirmed by an experienced electroencephalographer. HFOs that co-occurred with interictal epileptiform discharges (IEDs) were subsequently identified. fMRI activation maps associated with HFOs were generated that occurred either independently of IEDs or within ±200 ms of an IED. For all significant analyses, the Maximum, Second Maximum, and Closest activation clusters were identified, and distances were measured to both the electrodes where the HFOs were observed and the electrodes involved in seizure onset.

RESULTS

We identified 108 distinct groups of HFOs from 45 patients. We found that HFOs with IEDs produced fMRI clusters that were closer to the local field potentials of the corresponding HFOs observed within the EEG than HFOs without IEDs. In addition to the fMRI clusters being closer to the location of the EEG correlate, HFOs with IEDs generated Maximum clusters with greater z-scores and larger volumes than HFOs without IEDs. We also observed that HFOs with IEDs resulted in more discrete activation maps.

SIGNIFICANCE

Intracranial EEG-fMRI can be used to probe the hemodynamic response to HFOs. The hemodynamic response associated with HFOs that co-occur with IEDs better identifies known epileptic tissue than HFOs that occur independently.

Magnetoencephalography for Epilepsy Presurgical Evaluation

PURPOSE OF THE REVIEW

Magnetoencephalography (MEG) is a functional neuroimaging technique that records neurophysiology data with millisecond temporal resolution and localizes it with subcentimeter accuracy. Its capability to provide high resolution in both of these domains makes it a powerful tool both in basic neuroscience as well as clinical applications. In neurology, it has proven useful in its ability to record and localize epileptiform activity. Epilepsy workup typically begins with scalp electroencephalography (EEG), but in many situations, EEG-based localization of the epileptogenic zone is inadequate. The complementary sensitivity of MEG can be crucial in such cases, and MEG has been adopted at many centers as an important resource in building a surgical hypothesis. In this paper, we review recent work evaluating the extent of MEG influence of presurgical evaluations, novel analyses of MEG data employed in surgical workup, and new MEG instrumentation that will likely affect the field of clinical MEG.

RECENT FINDINGS

MEG consistently contributes to presurgical evaluation and these contributions often change the plan for epilepsy surgery. Extensive work has been done to develop new analytic methods for localizing the source of epileptiform activity with MEG. Systems using optically pumped magnetometry (OPM) have been successfully deployed to record and localize epileptiform activity. MEG remains an important noninvasive tool for epilepsy presurgical evaluation. Continued improvements in analytic methodology will likely increase the diagnostic yield of the test. Novel instrumentation with OPM may contribute to this as well, and may increase accessibility of MEG by decreasing cost.

High-resolution neural recordings improve the accuracy of speech decoding

Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.

Flexible, high-resolution cortical arrays with large coverage capture microscale high-frequency oscillations in patients with epilepsy

OBJECTIVE

Effective surgical treatment of drug-resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High-frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (μECoG) arrays, which provide high spatial resolution over large cortical surface areas.

METHODS

We used novel liquid crystal polymer thin-film μECoG arrays (.76-1.72-mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80-250 Hz) and fast ripple (250-600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal-to-noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data.

RESULTS

We found visually delineable subregions of elevated HFO activity within each μECoG recording. Forty-seven percent of HFOs occurred on single 200-μm-diameter recording contacts, with minimal high-frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a .95-mm radius. Through spatial averaging, we estimated that macrocontacts with 2-3-mm diameter would only capture 44% of the HFOs detected in our μECoG recordings.

SIGNIFICANCE

These results demonstrate that thin-film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug-resistant epilepsy.

Chloride ion dysregulation in epileptogenic neuronal networks

GABA is the major inhibitory neurotransmitter in the mature CNS. When GABA_A receptors are activated the membrane potential is driven towards hyperpolarization due to chloride entry into the neuron. However, chloride ion dysregulation that alters the ionic gradient can result in depolarizing GABAergic post-synaptic potentials instead. In this review, we highlight that GABAergic inhibition prevents and restrains focal seizures but then reexamine this notion in the context of evidence that a static and/or a dynamic chloride ion dysregulation, that increases intracellular chloride ion concentrations, promotes epileptiform activity and seizures. To reconcile these findings, we hypothesize that epileptogenic pathologically interconnected neuron (PIN) microcircuits, representing a small minority of neurons, exhibit static chloride dysregulation and should exhibit depolarizing inhibitory post-synaptic potentials (IPSPs). We speculate that chloride ion dysregulation and PIN cluster activation may generate fast ripples and epileptiform spikes as well as initiate the hypersynchronous seizure onset pattern and microseizures. Also, we discuss the genetic, molecular, and cellular players important in chloride dysregulation which regulate epileptogenesis and initiate the low-voltage fast seizure onset pattern. We conclude that chloride dysregulation in neuronal networks appears to be critical for epileptogenesis and seizure genesis, but feed-back and feed-forward inhibitory GABAergic neurotransmission plays an important role in preventing and restraining seizures as well.

Functional Characterization of Human Pluripotent Stem Cell-Derived Models of the Brain with Microelectrode Arrays

Human pluripotent stem cell (hPSC)-derived neuron cultures have emerged as models of electrical activity in the human brain. Microelectrode arrays (MEAs) measure changes in the extracellular electric potential of cell cultures or tissues and enable the recording of neuronal network activity. MEAs have been applied to both human subjects and hPSC-derived brain models. Here, we review the literature on the functional characterization of hPSC-derived two- and three-dimensional brain models with MEAs and examine their network function in physiological and pathological contexts. We also summarize MEA results from the human brain and compare them to the literature on MEA recordings of hPSC-derived brain models. MEA recordings have shown network activity in two-dimensional hPSC-derived brain models that is comparable to the human brain and revealed pathology-associated changes in disease models. Three-dimensional hPSC-derived models such as brain organoids possess a more relevant microenvironment, tissue architecture and potential for modeling the network activity with more complexity than two-dimensional models. hPSC-derived brain models recapitulate many aspects of network function in the human brain and provide valid disease models, but certain advancements in differentiation methods, bioengineering and available MEA technology are needed for these approaches to reach their full potential.

In vitro Oscillation Patterns Throughout the Hippocampal Formation in a Rodent Model of Epilepsy

Specific oscillatory patterns are considered biomarkers of pathological neuronal network in brain diseases, such as epilepsy. However, the dynamics of underlying oscillations during the epileptogenesis throughout the hippocampal formation in the temporal lobe epilepsy is not clear. Here, we characterized in vitro oscillatory patterns within the hippocampal formation of epileptic rats, under 4-aminopyridine (4-AP)-induced hyperexcitability and during the spontaneous network activity, at two periods of epileptogenesis. First, at the beginning of epileptic chronic phase, 30 days post-pilocarpine-induced Status Epilepticus (SE). Second, at the established epilepsy, 60 days post-SE. The 4-AP-bathed slices from epileptic rats had increased susceptibility to ictogenesis in CA1 at 30 days post-SE, and in entorhinal cortex and dentate gyrus at 60 days post-SE. Higher power and phase coherence were detected mainly for gamma and/or high frequency oscillations (HFOs), in a region- and stage-specific manner. Interestingly, under spontaneous network activity, even without 4-AP-induced hyperexcitability, slices from epileptic animals already exhibited higher power of gamma and HFOs in different areas of hippocampal formation at both periods of epileptogenesis, and higher phase coherence in fast ripples at 60 days post-SE. These findings reinforce the critical role of gamma and HFOs in each one of the hippocampal formation areas during ongoing neuropathological processes, tuning the neuronal network to epilepsy.

Ultra-fast oscillation detection in EEG signal from deep-brain microelectrodes

For the last decades, ripples 80-200Hz (R)and fast ripples 200-500Hz (FR) were intensively studied as biomarkers of the epileptogenic zone (EZ). Recently, Very fast ripples 500-1000Hz (VFR) and ultra-fast ripples 1000-2000Hz (UFR) recorded using standard clinical macro electrodes have been shown to be more specific for EZ. High-sampled microelectrode recordings can bring new insights into this phenomenon of high frequency, multiunit activity. Unfortunately, visual detection of such events is extremely time consuming and unreliable. Here we present a detector of ultra-fast oscillations (UFO, >1kHz). In an example of two patients, we detected 951 UFOs which were more frequent in epileptic (8.6/min) vs. non-epileptic hippocampus (1.3/min). Our detection method can serve as a tool for exploring extremely high frequency events from microelectrode recordings.

Localizing the Epileptogenic Zone with Novel Biomarkers

Several noninvasive methods, such as high-density EEG or magnetoencephalography, are currently used to delineate the epileptogenic zone (EZ) during the presurgical evaluation of patients with drug resistant epilepsy (DRE). Yet, none of these methods can reliably identify the EZ by their own. In most cases a multimodal approach is needed. Challenging cases often require the implantation of intracranial electrodes, either through stereo-taxic EEG or electro-corticography. Recently, a growing body of literature introduces novel biomarkers of epilepsy that can be used for analyzing both invasive as well as noninvasive electrophysiological data. Some of these biomarkers are able to delineate the EZ with high precision, augment the presurgical evaluation, and predict the surgical outcome of patients with DRE undergoing surgery. However, the use of these epilepsy biomarkers in clinical practice is limited. Here, we summarize and discuss the latest technological advances in the presurgical neurophysiological evaluation of children with DRE with emphasis on electric and magnetic source imaging, high frequency oscillations, and functional connectivity.

Long-term brain network reorganization predicts responsive neurostimulation outcomes for focal epilepsy

Responsive neurostimulation (RNS) devices, able to detect imminent seizures and to rapidly deliver electrical stimulation to the brain, are effective in reducing seizures in some patients with focal epilepsy. However, therapeutic response to RNS is often slow, is highly variable, and defies prognostication based on clinical factors. A prevailing view holds that RNS efficacy is primarily mediated by acute seizure termination; yet, stimulations greatly outnumber seizures and occur mostly in the interictal state, suggesting chronic modulation of brain networks that generate seizures. Here, using years-long intracranial neural recordings collected during RNS therapy, we found that patients with the greatest therapeutic benefit undergo progressive, frequency-dependent reorganization of interictal functional connectivity. The extent of this reorganization scales directly with seizure reduction and emerges within the first year of RNS treatment, enabling potential early prediction of therapeutic response. Our findings reveal a mechanism for RNS that involves network plasticity and may inform development of next-generation devices for epilepsy.

Microscale dynamics of electrophysiological markers of epilepsy

OBJECTIVE

Interictal discharges (IIDs) and high frequency oscillations (HFOs) are established neurophysiologic biomarkers of epilepsy, while microseizures are less well studied. We used custom poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) microelectrodes to better understand these markers' microscale spatial dynamics.

METHODS

Electrodes with spatial resolution down to 50 µm were used to record intraoperatively in 30 subjects. IIDs' degree of spread and spatiotemporal paths were generated by peak-tracking followed by clustering. Repeating HFO patterns were delineated by clustering similar time windows. Multi-unit activity (MUA) was analyzed in relation to IID and HFO timing.

RESULTS

We detected IIDs encompassing the entire array in 93% of subjects, while localized IIDs, observed across < 50% of channels, were seen in 53%. IIDs traveled along specific paths. HFOs appeared in small, repeated spatiotemporal patterns. Finally, we identified microseizure events that spanned 50-100 µm. HFOs covaried with MUA, but not with IIDs.

CONCLUSIONS

Overall, these data suggest that irritable cortex micro-domains may form part of an underlying pathologic architecture which could contribute to the seizure network.

SIGNIFICANCE

These results, supporting the possibility that epileptogenic cortex comprises a mosaic of irritable domains, suggests that microscale approaches might be an important perspective in devising novel seizure control therapies.

Brain injuries can set up an epileptogenic neuronal network

Development of epilepsy or epileptogenesis promotes recurrent seizures. As of today, there are no effective prophylactic therapies to prevent the onset of epilepsy. Contributing to this deficiency of preventive therapy is the lack of clarity in fundamental neurobiological mechanisms underlying epileptogenesis and lack of reliable biomarkers to identify patients at risk for developing epilepsy. This limits the development of prophylactic therapies in epilepsy. Here, neural network dysfunctions reflected by oscillopathies and microepileptiform activities, including neuronal hyperexcitability and hypersynchrony, drawn from both clinical and experimental epilepsy models, have been reviewed. This review suggests that epileptogenesis reflects a progressive and dynamic dysfunction of specific neuronal networks which recruit further interconnected groups of neurons, with this resultant pathological network mediating seizure occurrence, recurrence, and progression. In the future, combining spatial and temporal resolution of neuronal non-invasive recordings from patients at risk of developing epilepsy, together with analytics and computational tools, may contribute to determining whether the brain is undergoing epileptogenesis in asymptomatic patients following brain injury.

Robot-assisted stereoelectroencephalography electrode placement in twenty-three pediatric patients: a high-resolution analysis of individual lead placement time and accuracy at a single institution

PURPOSE

We describe a detailed evaluation of predictors associated with individual lead placement efficiency and accuracy for 261 stereoelectroencephalography (sEEG) electrodes placed for epilepsy monitoring in twenty-three children at our institution.

METHODS

Intra- and post-operative data was used to generate a linear mixed model to investigate predictors associated with three outcomes (lead placement time, lead entry error, lead target error) while accounting for correlated observations from the same patients. Lead placement time was measured using electronic time-stamp records stored by the ROSA software for each individual electrode; entry and target site accuracy was measured using postoperative stereotactic CT images fused with preoperative electrode trajectory planning images on the ROSA computer software. Predictors were selected from a list of variables that included patient demographics, laterality of leads, anatomic location of lead, skull thickness, bolt cap device used, and lead sequence number.

RESULTS

Twenty-three patients (11 female, 48%) of mean age 11.7 (± 6.1) years underwent placement of intracranial sEEG electrodes (median 11 electrodes) at our institution over a period of 1 year. There were no associated infections, hemorrhages, or other adverse events, and successful seizure capture was obtained in all monitored patients. The mean placement time for individual electrodes across all patients was 6.56 (± 3.5) min; mean target accuracy was 4.5 (± 3.5) mm. Lesional electrodes were associated with 25.7% (95% CI: 6.7-40.9%, p = 0.02) smaller target point errors. Larger skull thickness was associated with larger error: for every 1-mm increase in skull thickness, there was a 4.3% (95% CI: 1.2-7.5%, p = 0.007) increase in target error. Bilateral lead placement was associated with 26.0% (95% CI: 9.9-44.5%, p = 0.002) longer lead placement time. The relationship between placement time and lead sequence number was nonlinear: it decreased consistently for the first 4 electrodes, and became less pronounced thereafter.

CONCLUSIONS

Variation in sEEG electrode placement efficiency and accuracy can be explained by phenomena both within and outside of operator control. It is important to keep in mind the factors that can lead to better or worse lead placement efficiency and/or accuracy in order to maximize patient safety while maintaining the standard of care.

Flexible, high-resolution thin-film electrodes for human and animal neural research

Objective.Brain functions such as perception, motor control, learning, and memory arise from the coordinated activity of neuronal assemblies distributed across multiple brain regions. While major progress has been made in understanding the function of individual neurons, circuit interactions remain poorly understood. A fundamental obstacle to deciphering circuit interactions is the limited availability of research tools to observe and manipulate the activity of large, distributed neuronal populations in humans. Here we describe the development, validation, and dissemination of flexible, high-resolution, thin-film (TF) electrodes for recording neural activity in animals and humans.Approach.We leveraged standard flexible printed-circuit manufacturing processes to build high-resolution TF electrode arrays. We used biocompatible materials to form the substrate (liquid crystal polymer; LCP), metals (Au, PtIr, and Pd), molding (medical-grade silicone), and 3D-printed housing (nylon). We designed a custom, miniaturized, digitizing headstage to reduce the number of cables required to connect to the acquisition system and reduce the distance between the electrodes and the amplifiers. A custom mechanical system enabled the electrodes and headstages to be pre-assembled prior to sterilization, minimizing the setup time required in the operating room. PtIr electrode coatings lowered impedance and enabled stimulation. High-volume, commercial manufacturing enables cost-effective production of LCP-TF electrodes in large quantities.Main Results. Our LCP-TF arrays achieve 25× higher electrode density, 20× higher channel count, and 11× reduced stiffness than conventional clinical electrodes. We validated our LCP-TF electrodes in multiple human intraoperative recording sessions and have disseminated this technology to >10 research groups. Using these arrays, we have observed high-frequency neural activity with sub-millimeter resolution.Significance.Our LCP-TF electrodes will advance human neuroscience research and improve clinical care by enabling broad access to transformative, high-resolution electrode arrays.

Noninvasive Mapping of Ripple Onset Predicts Outcome in Epilepsy Surgery

OBJECTIVE

Intracranial electroencephalographic (icEEG) studies show that interictal ripples propagate across the brain of children with medically refractory epilepsy (MRE), and the onset of this propagation (ripple onset zone [ROZ]) estimates the epileptogenic zone. It is still unknown whether we can map this propagation noninvasively. The goal of this study is to map ripples (ripple zone [RZ]) and their propagation onset (ROZ) using high-density EEG (HD-EEG) and magnetoencephalography (MEG), and to estimate their prognostic value in pediatric epilepsy surgery.

METHODS

We retrospectively analyzed simultaneous HD-EEG and MEG data from 28 children with MRE who underwent icEEG and epilepsy surgery. Using electric and magnetic source imaging, we estimated virtual sensors (VSs) at brain locations that matched the icEEG implantation. We detected ripples on VSs, defined the virtual RZ and virtual ROZ, and estimated their distance from icEEG. We assessed the predictive value of resecting virtual RZ and virtual ROZ for postsurgical outcome. Interictal spike localization on HD-EEG and MEG was also performed and compared with ripples.

RESULTS

We mapped ripple propagation in all patients with HD-EEG and in 27 (96%) patients with MEG. The distance from icEEG did not differ between HD-EEG and MEG when mapping the RZ (26-27mm, p = 0.6) or ROZ (22-24mm, p = 0.4). Resecting the virtual ROZ, but not virtual RZ or the sources of spikes, was associated with good outcome for HD-EEG (p = 0.016) and MEG (p = 0.047).

INTERPRETATION

HD-EEG and MEG can map interictal ripples and their propagation onset (virtual ROZ). Noninvasively mapping the ripple onset may augment epilepsy surgery planning and improve surgical outcome of children with MRE. ANN NEUROL 2021;89:911-925.

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^-1and 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^-1and 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.

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.

Magnetoencephalography imaging of high frequency oscillations strengthens presurgical localization and outcome prediction

In patients with medically refractory epilepsy, resective surgery is the mainstay of therapy to achieve seizure freedom. However, ∼20-50% of cases have intractable seizures post-surgery due to the imprecise determination of epileptogenic zone. Recent intracranial studies suggest that high frequency oscillations between 80 and 200 Hz could serve as one of the consistent epileptogenicity biomarkers for localization of the epileptogenic zone. However, these high frequency oscillations are not adopted in the clinical setting because of difficult non-invasive detection. Here, we investigated non-invasive detection and localization of high frequency oscillations and its clinical utility in accurate pre-surgical assessment and post-surgical outcome prediction. We prospectively recruited 52 patients with medically refractory epilepsy who underwent standard pre-surgical workup including magnetoencephalography (MEG) followed by resective surgery after determination of the epileptogenic zone. The post-surgical outcome was assessed after 22.14 ± 10.05 months. Interictal epileptic spikes were expertly identified, and interictal epileptic oscillations across the neural activity frequency spectrum from 8 to 200 Hz were localized using adaptive spatial filtering methods. Localization results were compared with epileptogenic zone and resected cortex for congruence assessment and validated against the clinical outcome. The concordance rate of high frequency oscillations sources (80-200 Hz) with the presumed epileptogenic zone and the resected cortex were 75.0% and 78.8%, respectively, which is superior to that of other frequency bands and standard dipole fitting methods. High frequency oscillation sources corresponding with the resected cortex, had the best sensitivity of 78.0%, positive predictive value of 100% and an accuracy of 78.84% to predict the patient's surgical outcome, among all other frequency bands. If high frequency oscillation sources were spatially congruent with resected cortex, patients had an odds ratio of 5.67 and 82.4% probability of achieving a favourable surgical outcome. If high frequency oscillations sources were discordant with the epileptogenic zone or resection area, patient has an odds ratio of 0.18 and only 14.3% probability of achieving good outcome, and mostly tended to have an unfavourable outcome (χ2 = 5.22; P = 0.02; φ = -0.317). In receiver operating characteristic curve analyses, only sources of high-frequency oscillations demonstrated the best sensitivity and specificity profile in determining the patient's surgical outcome with area under the curve of 0.76, whereas other frequency bands indicate a poor predictive performance. Our study is the first non-invasive study to detect high frequency oscillations, address the efficacy of high frequency oscillations over the different neural oscillatory frequencies, localize them and clinically validate them with the post-surgical outcome in patients with medically refractory epilepsy. The evidence presented in the current study supports the fact that HFOs might significantly improve the presurgical assessment, and post-surgical outcome prediction, where it could widely be used in a clinical setting as a non-invasive biomarker.

Scalp EEG high frequency oscillations as a biomarker of treatment response in epileptic encephalopathy with continuous spike-and-wave during sleep (CSWS)

PURPOSE

We investigated whether the presence of interictal scalp EEG high frequency oscillations (HFOs) in children with epileptic encephalopathy with continuous spike-and-wave during sleep (CSWS) can predict seizure and cognitive outcome after steroid therapy.

METHODS

Twenty-two children with CSWS were prospectively enrolled and received methylprednisolone therapy. Interictal scalp HFOs, spike wave index (SWI) and intelligence quotient (IQ) were assessed before and after the treatment. The children were divided into two groups based on the early seizure reduction ratio at 2 weeks (≥50%, "response group"; otherwise "non-response group"). The "response group" was further divided into two subgroups ("relapse" and "non-relapse" subgroups) according to the late seizure outcome (after 3 months).

RESULTS

Interictal HFOs and electrical status epilepticus in sleep (ESES) (defined as SWI ≥ 85%) were detected in all children at the baseline. In the response with relapse group (n = 11), the detection ratio of HFOs was significantly higher than that of ESES at 2 weeks (81.2 vs. 27.3%), 3 months (90.9 vs. 36.4%), and 6 months (100 vs. 54.5%) post-therapy. In the non-response group (n = 4), both HFOs and ESES persisted in all children. The average IQ improved significantly only in the response with non-relapse group. The persistence of HFOs negatively correlated with both the average IQ, yet the persistence of ESES did not.

CONCLUSION

Interictal scalp HFOs may be a favorable non-invasive biomarker of predicting seizure and cognitive outcome in CSWS.

High Frequency Oscillations in Epilepsy: Detection Methods and Considerations in Clinical Application

High frequency oscillations (HFOs) is a brain activity observed in electroencephalography (EEG) in frequency ranges between 80–500 Hz. HFOs can be classified into ripples (80–200 Hz) and fast ripples (200–500 Hz) by their distinctive characteristics. Recent studies reported that both ripples and fast fipples can be regarded as a new biomarker of epileptogenesis and ictogenesis. Previous studies verified that HFOs are clinically important both in patients with mesial temporal lobe epilepsy and neocortical epilepsy. Also, in epilepsy surgery, patients with higher resection ratio of brain regions with HFOs showed better outcome than a group with lower resection ratio. For clinical application of HFOs, it is important to delineate HFOs accurately and discriminate them from artifacts. There have been technical improvements in detecting HFOs by developing various detection algorithms. Still, there is a difficult issue on discriminating clinically important HFOs among detected HFOs, where both quantitative and subjective approaches are suggested. This paper is a review on published HFO studies focused on clinical findings and detection techniques of HFOs as well as tips for clinical applications.

Early Detection of Human Epileptic Seizures Based on Intracortical Microelectrode Array Signals

OBJECTIVE

We examine, for the first time, the use of intracortical microelectrode array (MEA) signals for early detection of human epileptic seizures.

METHODS

4×4 mm² 96-channel-MEA recordings were obtained during neuro-monitoring preceding resective surgery in five participants. The participant-specific seizure-detection framework consisted of: first, feature extraction from local field potentials (LFPs) and multiunit activity (MUA); second, nonlinear cost-sensitive support vector machine (SVM) classification of ictal and interictal states based on LFP, MUA, and combined LFP-MUA (a SVM was trained for each participant separately); and third, Kalman filter postprocessing of SVM scoring functions. Performance was assessed on data including 17 seizures and 39.0 h interictal and preictal recordings.

RESULTS

The use of combined LFP-MUA features resulted in 100% sensitivity with short detection latency (average: 2.7 s; median: 2.5 s) and five false alarms (0.13/h). The average detection performance based on the area under the receiver operating characteristic corresponded to 0.97. Importantly, technically false alarms were related to epileptiform activity, subclinical seizures, and recording artifacts. Extreme gradient boosting classifiers ranked features based on LFP spectral coherence or MUA count among the top features for seizures characterized by spike-wave complexes, whereas features related to LFP power spectra were ranked higher for seizures characterized by sustained gamma LFP oscillations.

CONCLUSION

The combination of intracortical LFP and MUA signals may allow reliable detection of human epileptic seizures by improving latency and false alarm rate.

SIGNIFICANCE

Intracortical MEAs provide promising signals for closed-loop seizure-control systems based on seizure early-detection in people with pharmacologically resistant epilepsies.

The impact of pathological high-frequency oscillations on hippocampal network activity in rats with chronic epilepsy

In epilepsy, brain networks generate pathological high-frequency oscillations (pHFOs) during interictal periods. To understand how pHFOs differ from normal oscillations in overlapping frequency bands and potentially perturb hippocampal processing, we performed high-density single unit and local field potential recordings from hippocampi of behaving rats with and without chronic epilepsy. In epileptic animals, we observed two types of co-occurring fast oscillations, which by comparison to control animals we could classify as ‘ripple-like’ or ‘pHFO’. We compared their spectral characteristics, brain state dependence, and cellular participants. Strikingly, pHFO occurred irrespective of brain state, were associated with interictal spikes, engaged distinct subnetworks of principal neurons compared to ripple-like events, increased the sparsity of network activity, and initiated both general and immediate disruptions in spatial information coding. Taken together, our findings suggest that events that result in pHFOs have an immediate impact on memory processes, corroborating the need for proper classification of pHFOs to facilitate therapeutic interventions that selectively target pathological activity.

Localization of the Epileptogenic Zone Using High Frequency Oscillations

For patients with drug-resistant focal epilepsy, surgery is the therapy of choice in order to achieve seizure freedom. Epilepsy surgery foremost requires the identification of the epileptogenic zone (EZ), defined as the brain area indispensable for seizure generation. The current gold standard for identification of the EZ is the seizure-onset zone (SOZ). The fact, however that surgical outcomes are unfavorable in 40-50% of well-selected patients, suggests that the SOZ is a suboptimal biomarker of the EZ, and that new biomarkers resulting in better postsurgical outcomes are needed. Research of recent years suggested that high-frequency oscillations (HFOs) are a promising biomarker of the EZ, with a potential to improve surgical success in patients with drug-resistant epilepsy without the need to record seizures. Nonetheless, in order to establish HFOs as a clinical biomarker, the following issues need to be addressed. First, evidence on HFOs as a clinically relevant biomarker stems predominantly from retrospective assessments with visual marking, leading to problems of reproducibility and reliability. Prospective assessments of the use of HFOs for surgery planning using automatic detection of HFOs are needed in order to determine their clinical value. Second, disentangling physiologic from pathologic HFOs is still an unsolved issue. Considering the appearance and the topographic location of presumed physiologic HFOs could be immanent for the interpretation of HFO findings in a clinical context. Third, recording HFOs non-invasively via scalp electroencephalography (EEG) and magnetoencephalography (MEG) is highly desirable, as it would provide us with the possibility to translate the use of HFOs to the scalp in a large number of patients. This article reviews the literature regarding these three issues. The first part of the article focuses on the clinical value of invasively recorded HFOs in localizing the EZ, the detection of HFOs, as well as their separation from physiologic HFOs. The second part of the article focuses on the current state of the literature regarding non-invasively recorded HFOs with emphasis on findings and technical considerations regarding their localization.

Long-term recording reliability of liquid crystal polymer µECoG arrays

OBJECTIVE

The clinical use of microsignals recorded over broad cortical regions is largely limited by the chronic reliability of the implanted interfaces.

APPROACH

We evaluated the chronic reliability of novel 61-channel micro-electrocorticographic (µECoG) arrays in rats chronically implanted for over one year and using accelerated aging. Devices were encapsulated with polyimide (PI) or liquid crystal polymer (LCP), and fabricated using commercial manufacturing processes. In vitro failure modes and predicted lifetimes were determined from accelerated soak testing. Successful designs were implanted epidurally over the rodent auditory cortex. Trends in baseline signal level, evoked responses and decoding performance were reported for over one year of implantation.

MAIN RESULTS

Devices fabricated with LCP consistently had longer in vitro lifetimes than PI encapsulation. Our accelerated aging results predicted device integrity beyond 3.4 years. Five implanted arrays showed stable performance over the entire implantation period (247-435 d). Our regression analysis showed that impedance predicted signal quality and information content only in the first 31 d of recordings and had little predictive value in the chronic phase (>31 d). In the chronic phase, site impedances slightly decreased yet decoding performance became statistically uncorrelated with impedance. We also employed an improved statistical model of spatial variation to measure sensitivity to locally varying fields, which is typically concealed in standard signal power calculations.

SIGNIFICANCE

These findings show that µECoG arrays can reliably perform in chronic applications in vivo for over one year, which facilitates the development of a high-density, clinically viable interface.

Surgical resection of ripple onset predicts outcome in pediatric epilepsy

OBJECTIVE

In patients with medically refractory epilepsy (MRE), interictal ripples (80-250Hz) are observed in large brain areas whose resection may be unnecessary for seizure freedom. This limits their utility as epilepsy biomarkers for surgery. We assessed the spatiotemporal propagation of interictal ripples on intracranial electroencephalography (iEEG) in children with MRE, compared it with the propagation of spikes, identified ripples that initiated propagation (onset-ripples), and evaluated their clinical value as epilepsy biomarkers.

METHODS

Twenty-seven children who underwent epilepsy surgery were studied. We identified propagation sequences of ripples and spikes across multiple iEEG contacts and calculated each ripple or spike latency from the propagation onset. We classified ripples and spikes into categories (ie, onset, spread, and isolated) based on their spatiotemporal characteristics and correlated their mean rate inside and outside resection with outcome (good outcome, Engel 1 versus poor outcome, Engel≥2). We determined, as onset-zone, spread-zone, and isolated-zone, the areas generating the corresponding ripple or spike category and evaluated the predictive value of their resection.

RESULTS

We observed ripple propagation in all patients and spike propagation in 25 patients. Mean rate of onset-ripples inside resection predicted the outcome (odds ratio = 5.37; p = 0.02) and correlated with Engel class (rho = -0.55; p = 0.003). Resection of the onset-ripple-zone was associated with good outcome (p = 0.047). No association was found for the spread-ripple-zone, isolated-ripple-zone, or any spike-zone.

INTERPRETATION

Interictal ripples propagate across iEEG contacts in children with MRE. The association between the onset-ripple-zone resection and good outcome indicates that onset-ripples are promising epilepsy biomarkers, which estimate the epileptogenic tissue better than spread-ripples or onset-spikes. Ann Neurol 2018;84:331-346.

Multivariate regression methods for estimating velocity of ictal discharges from human microelectrode recordings

OBJECTIVE

Epileptiform discharges, an electrophysiological hallmark of seizures, can propagate across cortical tissue in a manner similar to traveling waves. Recent work has focused attention on the origination and propagation patterns of these discharges, yielding important clues to their source location and mechanism of travel. However, systematic studies of methods for measuring propagation are lacking.

APPROACH

We analyzed epileptiform discharges in microelectrode array recordings of human seizures. The array records multiunit activity and local field potentials at 400 micron spatial resolution, from a small cortical site free of obstructions. We evaluated several computationally efficient statistical methods for calculating traveling wave velocity, benchmarking them to analyses of associated neuronal burst firing.

MAIN RESULTS

Over 90% of discharges met statistical criteria for propagation across the sampled cortical territory. Detection rate, direction and speed estimates derived from a multiunit estimator were compared to four field potential-based estimators: negative peak, maximum descent, high gamma power, and cross-correlation. Interestingly, the methods that were computationally simplest and most efficient (negative peak and maximal descent) offer non-inferior results in predicting neuronal traveling wave velocities compared to the other two, more complex methods. Moreover, the negative peak and maximal descent methods proved to be more robust against reduced spatial sampling challenges. Using least absolute deviation in place of least squares error minimized the impact of outliers, and reduced the discrepancies between local field potential-based and multiunit estimators.

SIGNIFICANCE

Our findings suggest that ictal epileptiform discharges typically take the form of exceptionally strong, rapidly traveling waves, with propagation detectable across millimeter distances. The sequential activation of neurons in space can be inferred from clinically-observable EEG data, with a variety of straightforward computation methods available. This opens possibilities for systematic assessments of ictal discharge propagation in clinical and research settings.

Intracranial Electrophysiology of the Human Default Network

The human default network (DN) plays a critical role in internally directed cognition, behavior, and neuropsychiatric disease. Despite much progress with functional neuroimaging, persistent questions still linger concerning the electrophysiological underpinnings, fast temporal dynamics, and causal importance of the DN. Here, we review how direct intracranial recording and stimulation of the DN provides a unique combination of high spatiotemporal resolution and causal information that speaks directly to many of these outstanding questions. Our synthesis highlights the electrophysiological basis of activation, suppression, and connectivity of the DN, each key areas of debate in the literature. Integrating these unique electrophysiological data with extant neuroimaging findings will help lay the foundation for a mechanistic account of DN function in human behavior and cognition.

Promises and limitations of human intracranial electroencephalography

Intracranial electroencephalography (iEEG), also known as electrocorticography when using subdural grid electrodes or stereotactic EEG when using depth electrodes, is blossoming in various fields of human neuroscience. In this article, we highlight the potentials of iEEG in exploring functions of the human brain while also considering its limitations. The iEEG signal provides anatomically precise information about the selective engagement of neuronal populations at the millimeter scale and the temporal dynamics of their engagement at the millisecond scale. If several nodes of a given network are monitored simultaneously with implanted electrodes, the iEEG signals can also reveal information about functional interactions within and across networks during different stages of neural computation. As such, human iEEG can complement other methods of neuroscience beyond simply replicating what is already known, or can be known, from noninvasive lines of research in humans or from invasive recordings in nonhuman mammalian brains.

A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings

OBJECTIVE

To develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80-200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG.

METHODS

A custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals.

RESULTS

The detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ± 0.03. The detector successfully distinguished ripple from high spectral frequency 'fast ripple' oscillations (200-600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30-80 Hz) activity in 7.31 ± 6.09% of trials, and in 30.2 ± 14.4% of the true RonS detections ripple duration was underestimated.

CONCLUSIONS

Characterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing.

SIGNIFICANCE

Categorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker.

How to record high-frequency oscillations in epilepsy: A practical guideline

OBJECTIVE

Technology for localizing epileptogenic brain regions plays a central role in surgical planning. Recent improvements in acquisition and electrode technology have revealed that high-frequency oscillations (HFOs) within the 80-500 Hz frequency range provide the neurophysiologist with new information about the extent of the epileptogenic tissue in addition to ictal and interictal lower frequency events. Nevertheless, two decades after their discovery there remain questions about HFOs as biomarkers of epileptogenic brain and there use in clinical practice.

METHODS

In this review, we provide practical, technical guidance for epileptologists and clinical researchers on recording, evaluation, and interpretation of ripples, fast ripples, and very high-frequency oscillations.

RESULTS

We emphasize the importance of low noise recording to minimize artifacts. HFO analysis, either visual or with automatic detection methods, of high fidelity recordings can still be challenging because of various artifacts including muscle, movement, and filtering. Magnetoencephalography and intracranial electroencephalography (iEEG) recordings are subject to the same artifacts.

SIGNIFICANCE

High-frequency oscillations are promising new biomarkers in epilepsy. This review provides interested researchers and clinicians with a review of current state of the art of recording and identification and potential challenges to clinical translation.

Target-specific alterations in the VIP inhibitory drive to hippocampal GABAergic cells after status epilepticus

Status epilepticus (SE) is associated with complex reorganization of hippocampal circuits involving a significant loss of specific subtypes of GABAergic interneurons. While adaptive circuit plasticity may increase the chances for recruitment of surviving interneurons, the underlying mechanisms remain largely unknown. We studied the alterations in the inhibitory tone received by the hippocampal CA1 oriens/alveus (O/A) interneurons from the vasoactive intestinal peptide (VIP)- and calretinin (CR)-expressing interneurons using the pilocarpine-induced status epilepticus (SE) model of epilepsy. Our data showed that, while the overall density of the VIP/CR-co-expressing interneurons remained preserved, the number of axonal boutons made by these cells within the CA1 O/A was significantly lower after SE. Furthermore, VIP/CR interneurons exhibited significant alterations in their dendritic morphology and passive membrane properties. Subsequently, while all O/A interneuron types, including oriens-lacunosum moleculare (OLM), bistratified (Bis) and basket cells, exhibited decrease in spontaneous inhibitory drive, Bis and basket cells showed a smaller amplitude of light-evoked IPSCs mediated by the selective activation of VIP-positive interneurons. These data point to the target cell-specific changes in the inhibitory tone provided by the VIP cells to O/A interneurons following SE. Given that basket, Bis and OLM cells coordinate different subcellular domains of pyramidal neurons, significant disinhibition of basket and Bis cells along with a previously reported loss of the OLMs may result in a redistribution of inhibition converging onto pyramidal neurons, with a direct impact onto their recruitment to epileptiform network activity and seizure propagation.

Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy

Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network.

Interictal high-frequency oscillations in focal human epilepsy

PURPOSE OF REVIEW

Localization of focal epileptic brain is critical for successful epilepsy surgery and focal brain stimulation. Despite significant progress, roughly half of all patients undergoing focal surgical resection, and most patients receiving focal electrical stimulation, are not seizure free. There is intense interest in high-frequency oscillations (HFOs) recorded with intracranial electroencephalography as potential biomarkers to improve epileptogenic brain localization, resective surgery, and focal electrical stimulation. The present review examines the evidence that HFOs are clinically useful biomarkers.

RECENT FINDINGS

Performing the PubMed search 'High-Frequency Oscillations and Epilepsy' for 2013-2015 identifies 308 articles exploring HFO characteristics, physiological significance, and potential clinical applications.

SUMMARY

There is strong evidence that HFOs are spatially associated with epileptic brain. There remain, however, significant challenges for clinical translation of HFOs as epileptogenic brain biomarkers: Differentiating true HFO from the high-frequency power changes associated with increased neuronal firing and bandpass filtering sharp transients. Distinguishing pathological HFO from normal physiological HFO. Classifying tissue under individual electrodes as normal or pathological. Sharing data and algorithms so research results can be reproduced across laboratories. Multicenter prospective trials to provide definitive evidence of clinical utility.

Organic electronics for high-resolution electrocorticography of the human brain

Localizing neuronal patterns that generate pathological brain signals may assist with tissue resection and intervention strategies in patients with neurological diseases. Precise localization requires high spatiotemporal recording from populations of neurons while minimizing invasiveness and adverse events. We describe a large-scale, high-density, organic material-based, conformable neural interface device ("NeuroGrid") capable of simultaneously recording local field potentials (LFPs) and action potentials from the cortical surface. We demonstrate the feasibility and safety of intraoperative recording with NeuroGrids in anesthetized and awake subjects. Highly localized and propagating physiological and pathological LFP patterns were recorded, and correlated neural firing provided evidence about their local generation. Application of NeuroGrids to brain disorders, such as epilepsy, may improve diagnostic precision and therapeutic outcomes while reducing complications associated with invasive electrodes conventionally used to acquire high-resolution and spiking data.

Investigation of automatically detected high frequency oscillations (HFOs) as an early predictor of seizure onset zone

High frequency oscillations (HFOs) during inter-ictal state have been considered as a potential biomarker of epileptogenic regions in brain. The purpose of the current study is to improve and automatize the detection of HFOs basing on HFO distinguishing features followed by unsupervised clustering method, and to predict seizure onset zone (SOZ) using the clustered HFOs. The algorithm successfully separated HFOs of different sub-categories from noise, artifacts, and inter-ictal spikes. We tested this technique on two subjects, and assessed the performance of SOZ prediction by computing the overlapping rate of HFO generative channels and seizure onset channels. In both subjects, we were able to localize the seizure onset area 3 to 4 days before the actual onset of the seizure, with high specificity over 95%. The algorithm showed significant improvement comparing to another existing technique.

Dysfunctional epileptic neuronal circuits and dysmorphic dendritic spines are mitigated by platelet-activating factor receptor antagonism

Temporal lobe epilepsy or limbic epilepsy lacks effective therapies due to a void in understanding the cellular and molecular mechanisms that set in motion aberrant neuronal network formations during the course of limbic epileptogenesis (LE). Here we show in in vivo rodent models of LE that the phospholipid mediator platelet-activating factor (PAF) increases in LE and that PAF receptor (PAF-r) ablation mitigates its progression. Synthetic PAF-r antagonists, when administered intraperitoneally in LE, re-establish hippocampal dendritic spine density and prevent formation of dysmorphic dendritic spines. Concomitantly, hippocampal interictal spikes, aberrant oscillations, and neuronal hyper-excitability, evaluated 15–16 weeks after LE using multi-array silicon probe electrodes implanted in the dorsal hippocampus, are reduced in PAF-r antagonist-treated mice. We suggest that over-activation of PAF-r signaling induces aberrant neuronal plasticity in LE and leads to chronic dysfunctional neuronal circuitry that mediates epilepsy.

Sparse asynchronous cortical generators can produce measurable scalp EEG signals

We investigate to what degree the synchronous activation of a smooth patch of cortex is necessary for observing EEG scalp activity. We perform extensive simulations to compare the activity generated on the scalp by different models of cortical activation, based on intracranial EEG findings reported in the literature. The spatial activation is modeled as a cortical patch of constant activation or as random sets of small generators (0.1 to 3cm(2) each) concentrated in a cortical region. Temporal activation models for the generation of oscillatory activity are either equal phase or random phase across the cortical patches. The results show that smooth or random spatial activation profiles produce scalp electric potential distributions with the same shape. Also, in the generation of oscillatory activity, multiple cortical generators with random phase produce scalp activity attenuated on average only 2 to 4 times compared to generators with equal phase. Sparse asynchronous cortical generators can produce measurable scalp EEG. This is a possible explanation for seemingly paradoxical observations of simultaneous disorganized intracranial activity and scalp EEG signals. Thus, the standard interpretation of scalp EEG might constitute an oversimplification of the underlying brain activity.

High-frequency brain networks undergo modular breakdown during epileptic seizures

OBJECTIVE

Cortical high-frequency oscillations (HFOs; 100-500 Hz) play a critical role in the pathogenesis of epilepsy; however, whether they represent a true epileptogenic process remains largely unknown. HFOs have been recorded in the human cortex but their network dynamics during the transitional period from interictal to ictal phase remain largely unknown. We sought to determine the high-frequency network dynamics of these oscillations in patients with epilepsy who were undergoing intracranial electroencephalographic recording for seizure localization.

METHODS

We applied a graph theoretical analysis framework to high-resolution intracranial electroencephalographic recordings of 24 interictal and 24 seizure periods to identify the spatiotemporal evolution of community structure of high-frequency cortical networks at rest and during multiple seizure episodes in patients with intractable epilepsy.

RESULTS

Cortical networks at all examined frequencies showed temporally stable community architecture in all 24 interictal periods. During seizure periods, high-frequency networks showed a significant breakdown of their community structure, which was characterized by the emergence of numerous small nodal communities, not limited to seizure foci and encompassing the entire recorded network. Such network disorganization was observed on average 225 s before the electrographic seizure onset and extended on average 190 s after termination of the seizure. Gamma networks were characterized by stable community dynamics during resting and seizure periods.

SIGNIFICANCE

Our findings suggest that the modular breakdown of high-frequency cortical networks represents a distinct functional pathology that underlies epileptogenesis and corresponds to a cortical state of highest propensity to generate seizures.

Multiscale Aspects of Generation of High-Gamma Activity during Seizures in Human Neocortex

High-gamma (HG; 80-150 Hz) activity in macroscopic clinical records is considered a marker for critical brain regions involved in seizure initiation; it is correlated with pathological multiunit firing during neocortical seizures in the seizure core, an area identified by correlated multiunit spiking and low frequency seizure activity.

High-gamma (HG; 80-150 Hz) activity in macroscopic clinical records is considered a marker for critical brain regions involved in seizure initiation; it is correlated with pathological multiunit firing during neocortical seizures in the seizure core, an area identified by correlated multiunit spiking and low frequency seizure activity. However, the effects of the spatiotemporal dynamics of seizure on HG power generation are not well understood. Here, we studied HG generation and propagation, using a three-step, multiscale signal analysis and modeling approach. First, we analyzed concurrent neuronal and microscopic network HG activity in neocortical slices from seven intractable epilepsy patients. We found HG activity in these networks, especially when neurons displayed paroxysmal depolarization shifts and network activity was highly synchronized. Second, we examined HG activity acquired with microelectrode arrays recorded during human seizures (n = 8). We confirmed the presence of synchronized HG power across microelectrode records and the macroscale, both specifically associated with the core region of the seizure. Third, we used volume conduction-based modeling to relate HG activity and network synchrony at different network scales. We showed that local HG oscillations require high levels of synchrony to cross scales, and that this requirement is met at the microscopic scale, but not within macroscopic networks. Instead, we present evidence that HG power at the macroscale may result from harmonics of ongoing seizure activity. Ictal HG power marks the seizure core, but the generating mechanism can differ across spatial scales.

Seizure Prediction: Science Fiction or Soon to Become Reality?

This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.

The ictal wavefront is the spatiotemporal source of discharges during spontaneous human seizures

The extensive distribution and simultaneous termination of seizures across cortical areas has led to the hypothesis that seizures are caused by large-scale coordinated networks spanning these areas. This view, however, is difficult to reconcile with most proposed mechanisms of seizure spread and termination, which operate on a cellular scale. We hypothesize that seizures evolve into self-organized structures wherein a small seizing territory projects high-intensity electrical signals over a broad cortical area. Here we investigate human seizures on both small and large electrophysiological scales. We show that the migrating edge of the seizing territory is the source of travelling waves of synaptic activity into adjacent cortical areas. As the seizure progresses, slow dynamics in induced activity from these waves indicate a weakening and eventual failure of their source. These observations support a parsimonious theory for how large-scale evolution and termination of seizures are driven from a small, migrating cortical area.

Epileptic brains display inhibitory restraint as manifested by the spread of synchronized activities being delayed in timing. Here, Elliot Smith and colleagues show fast-moving traveling wave that originates from the edge of ictal wavefront with subsequent depolarization and multiunit firing in the seizing brain regions in epileptic patients.

Conundrums of high-frequency oscillations (80-800 Hz) in the epileptic brain

Pathological high-frequency oscillations (HFOs) (80-800 Hz) are considered biomarkers of epileptogenic tissue, but the underlying complex neuronal events are not well understood. Here, we identify and discuss several outstanding issues or conundrums in regards to the recording, analysis, and interpretation of HFOs in the epileptic brain to critically highlight what is known and what is not about these enigmatic events. High-frequency oscillations reflect a range of neuronal processes contributing to overlapping frequencies from the lower 80 Hz to the very fast spectral frequency bands. Given their complex neuronal nature, HFOs are extremely sensitive to recording conditions and analytical approaches. We provide a list of recommendations that could help to obtain comparable HFO signals in clinical and basic epilepsy research. Adopting basic standards will facilitate data sharing and interpretation that collectively will aid in understanding the role of HFOs in health and disease for translational purpose.

Detectability of Fast Ripples (>250 Hz) on the Scalp EEG: A Proof-of-Principle Study with Subdermal Electrodes

To evaluate the possibility of detecting fast ripples (FRs) on the surface EEG of patients with focal pharmacoresistant epilepsy, and to investigate the relationship between scalp FRs and localization of the seizure onset zone (SOZ). We included 10 patients undergoing combined surface-intracranial EEG with ≥10 spikes in the surface EEG during the first 30 consecutive minutes of N3 sleep. FRs (≥4 consecutive oscillations above 250 Hz with an amplitude clearly exceeding that of the background) on the surface EEG (F3-C3, C3-P3, Fz-Cz, Cz-Pz, F4-C4, C4-P4) were visually marked, and verified by two EEG experts. FRs were categorized as related to the SOZ, if localized in the brain lobe of the SOZ. Low-amplitude FRs with a rate of 0.09/min were found in 6/10 patients: two exhibited events related to the SOZ, three showed no relationship with the SOZ, and in one patient the SOZ was not identified. It may be possible to detect FRs with surface EEG using subdermal electrodes in patients with focal epilepsy. The relationship between surface FRs and the SOZ remains unclear. Future studies aiming at a higher spatial EEG coverage are needed to elucidate their significance.

Dynamic Network Drivers of Seizure Generation, Propagation and Termination in Human Neocortical Epilepsy

The epileptic network is characterized by pathologic, seizure-generating 'foci' embedded in a web of structural and functional connections. Clinically, seizure foci are considered optimal targets for surgery. However, poor surgical outcome suggests a complex relationship between foci and the surrounding network that drives seizure dynamics. We developed a novel technique to objectively track seizure states from dynamic functional networks constructed from intracranial recordings. Each dynamical state captures unique patterns of network connections that indicate synchronized and desynchronized hubs of neural populations. Our approach suggests that seizures are generated when synchronous relationships near foci work in tandem with rapidly changing desynchronous relationships from the surrounding epileptic network. As seizures progress, topographical and geometrical changes in network connectivity strengthen and tighten synchronous connectivity near foci-a mechanism that may aid seizure termination. Collectively, our observations implicate distributed cortical structures in seizure generation, propagation and termination, and may have practical significance in determining which circuits to modulate with implantable devices.

Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning

Sharp wave ripples (SPW-Rs) represent the most synchronous population pattern in the mammalian brain. Their excitatory output affects a wide area of the cortex and several subcortical nuclei. SPW-Rs occur during "off-line" states of the brain, associated with consummatory behaviors and non-REM sleep, and are influenced by numerous neurotransmitters and neuromodulators. They arise from the excitatory recurrent system of the CA3 region and the SPW-induced excitation brings about a fast network oscillation (ripple) in CA1. The spike content of SPW-Rs is temporally and spatially coordinated by a consortium of interneurons to replay fragments of waking neuronal sequences in a compressed format. SPW-Rs assist in transferring this compressed hippocampal representation to distributed circuits to support memory consolidation; selective disruption of SPW-Rs interferes with memory. Recently acquired and pre-existing information are combined during SPW-R replay to influence decisions, plan actions and, potentially, allow for creative thoughts. In addition to the widely studied contribution to memory, SPW-Rs may also affect endocrine function via activation of hypothalamic circuits. Alteration of the physiological mechanisms supporting SPW-Rs leads to their pathological conversion, "p-ripples," which are a marker of epileptogenic tissue and can be observed in rodent models of schizophrenia and Alzheimer's Disease. Mechanisms for SPW-R genesis and function are discussed in this review.