Visual and semi-automatic non-invasive detection of interictal fast ripples: A potential biomarker of epilepsy in children with tuberous sclerosis complex

Spontaneous HFO Sequences Reveal Propagation Pathways for Precise Delineation of Epileptogenic Networks

Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy.

One Sentence Summary

Pathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.

Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning

Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep learning models were designed, respectively. Finally, the weighted voting method is used to combine the outputs of the two model. In experiments, the precision, recall, specificity and F1-score were 83.44%, 83.60%, 96.61% and 83.42%, respectively, on average and the kappa coefficient was 80.02%. In addition, the proposed detector showed a stable performance on multi-centre datasets. Our sHFOs detector demonstrated high robustness and generalisation ability, which indicates its potential applicability as a clinical assistance tool. The proposed sHFOs detector achieves an accurate and robust method via deep learning algorithm.

Scalp EEG-recorded high-frequency oscillations can predict seizure activity in Panayiotopoulos syndrome

OBJECTIVE

We studied the relationship between the clinical course of Panayiotopoulos syndrome (PS) and high-frequency oscillations (HFOs) captured during interictal scalp electroencephalography (EEG) to determine the feasibility of using HFOs to detect seizure activity in PS.

METHODS

We analyzed the interictal scalp EEGs of 18 children with PS. Age parameters, seizure frequencies, and antiepileptic drugs were compared between the HFO-positive (HFOPG) and HFO-negative (HFONG) groups.

RESULTS

Thirteen patients (72.2%) had HFOs while five patients (27.8%) had no HFOs in 194 interictal EEG records. We found no statistically significant differences in the mean age of epilepsy onset and last seizure, seizure frequency, or frequency of status epilepticus. However, the seizure activity period of the HFOPG was significantly longer than that of the HFONG. Patients with an HFO duration longer than 2 years were intractable to treatment. In most cases, seizures did not occur in the absence of HFOs, even when the spikes remained.

CONCLUSIONS

HFOs are related to the seizure activity period in patients with PS.

SIGNIFICANCE

We propose that HFOs are a biomarker of epileptogenicity and an indicator for drug reduction because seizures did not occur if HFOs disappeared even if the spikes remained.

Scalp high-frequency oscillations differentiate neonates with seizures from healthy neonates

OBJECTIVE

We aimed to investigate (1) whether an automated detector can capture scalp high-frequency oscillations (HFO) in neonates and (2) whether scalp HFO rates can differentiate neonates with seizures from healthy neonates.

METHODS

We considered 20 neonates with EEG-confirmed seizures and four healthy neonates. We applied a previously validated automated HFO detector to determine scalp HFO rates in quiet sleep.

RESULTS

Etiology in neonates with seizures included hypoxic-ischemic encephalopathy in 11 cases, structural vascular lesions in 6, and genetic causes in 3. The HFO rates were significantly higher in neonates with seizures (0.098 ± 0.091 HFO/min) than in healthy neonates (0.038 ± 0.025 HFO/min; P = 0.02) with a Hedge's g value of 0.68 indicating a medium effect size. The HFO rate of 0.1 HFO/min/ch yielded the highest Youden index in discriminating neonates with seizures from healthy neonates. In neonates with seizures, etiology, status epilepticus, EEG background activity, and seizure patterns did not significantly impact HFO rates.

SIGNIFICANCE

Neonatal scalp HFO can be detected automatically and differentiate neonates with seizures from healthy neonates. Our observations have significant implications for neuromonitoring in neonates. This is the first step in establishing neonatal HFO as a biomarker for neonatal seizures.

Sevoflurane-induced high-frequency oscillations, effective connectivity and intraoperative classification of epileptic brain areas

OBJECTIVE

To determine how sevoflurane anesthesia modulates intraoperative epilepsy biomarkers on electrocorticography, including high-frequency oscillation (HFO) effective connectivity (EC), and to investigate their relation to epileptogenicity and anatomical white matter.

METHODS

We studied eight pediatric drug-resistant focal epilepsy patients who achieved seizure control after invasive monitoring and resective surgery. We visualized spatial distributions of the electrocorticography biomarkers at an oxygen baseline, three time-points while sevoflurane was increasing, and at a plateau of 2 minimum alveolar concentration (MAC) sevoflurane. HFO EC was combined with diffusion-weighted imaging, in dynamic tractography.

RESULTS

Intraoperative HFO EC diffusely increased as a function of sevoflurane concentration, although most in epileptogenic sites (defined as those included in the resection); their ability to classify epileptogenicity was optimized at sevoflurane 2 MAC. HFO EC could be visualized on major white matter tracts, as a function of sevoflurane level.

CONCLUSIONS

The results strengthened the hypothesis that sevoflurane-activated HFO biomarkers may help intraoperatively localize the epileptogenic zone.

SIGNIFICANCE

Our results help characterize how HFOs at non-epileptogenic and epileptogenic networks respond to sevoflurane. It may be warranted to establish a normative HFO atlas incorporating the modifying effects of sevoflurane and major white matter pathways, as critical reference in epilepsy presurgical evaluation.

Scalp HFO rates are higher for larger lesions

High‐frequency oscillations (HFO) in scalp EEG are a new and promising noninvasive epilepsy biomarker, providing added prognostic value, particularly in pediatric lesional epilepsy. However, it is unclear if lesion characteristics, such as lesion volume, depth, type, and localization, impact scalp HFO rates. We analyzed scalp EEG from 13 children and adolescents with focal epilepsy associated with focal cortical dysplasia (FCD), low‐grade tumors, or hippocampal sclerosis. We applied a validated automated detector to determine HFO rates in bipolar channels. We identified the lesion characteristics in MRI. Larger lesions defined by MRI volumetric analysis corresponded to higher cumulative scalp HFO rates (P = .01) that were detectable in a higher number of channels (P = .05). Both superficial and deep lesions generated HFO detectable in the scalp EEG. Lesion type (FCD vs tumor) and lobar localization (temporal vs extratemporal) did not affect scalp HFO rates in our study. Our observations support that all lesions may generate HFO detectable in scalp EEG, irrespective of their characteristics, whereas larger epileptogenic lesions generate higher scalp HFO rates over larger areas that are thus more accessible to detection. Our study provides crucial insight into scalp HFO detectability in pediatric lesional epilepsy, facilitating their implementation as an epilepsy biomarker in a clinical setting.

Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone

Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics.

Can EEG Abnormalities be a Biomarker of Epileptogenesis?

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High-frequency oscillations detected by electroencephalography as biomarkers to evaluate treatment outcome, mirror pathological severity and predict susceptibility to epilepsy

High-frequency oscillations (HFOs) in the electroencephalography (EEG) have been extensively investigated as a potential biomarker of epileptogenic zones. The understanding of the role of HFOs in epilepsy has been advanced considerably over the past decade, and the use of scalp EEG facilitates recordings of HFOs. HFOs were initially applied in large scale in epilepsy surgery and are now being utilized in other applications. In this review, we summarize applications of HFOs in 3 subtopics: (1) HFOs as biomarkers to evaluate epilepsy treatment outcome; (2) HFOs as biomarkers to measure seizure propensity; (3) HFOs as biomarkers to reflect the pathological severity of epilepsy. Nevertheless, knowledge regarding the above clinical applications of HFOs remains limited at present. Further validation through prospective studies is required for its reliable application in the clinical management of individual epileptic patients.

Automatic detection of interictal ripples on scalp EEG to evaluate the effect and prognosis of ACTH therapy in patients with infantile spasms

OBJECTIVE

We aimed to explore the feasibility of using scalp-recorded high-frequency oscillations (HFOs) to evaluate the efficacy and prognosis of adrenocorticotropic hormone (ACTH) treatment in patients with infantile spasms.

METHODS

Thirty-nine children with infantile spasms were enrolled and divided into seizure-free and non-seizure-free groups after ACTH treatment. Patients who were seizure-free were further divided into relapse and non-relapse subgroups based on the observations made during a 6-month follow-up period. Scalp ripples were detected and compared during the interictal periods before and after 2 weeks of treatment.

RESULTS

After ACTH treatment, the number and channels of ripples were significantly lower, whereas the percentage decrease in the number, spectral power, and channels of ripples was significantly higher in the seizure-free group than in the non-seizure-free group. In addition, the relapse subgroup showed higher number and spectral power and wider distribution of ripples than did the non-relapse subgroup. Changes in HFOs in terms of number, spectral power, and channel of ripples were closely related to the severity of epilepsy and can indicate disease susceptibility.

SIGNIFICANCE

Scalp HFOs can be used as an effective biomarker to monitor the effect and evaluate the prognosis of ACTH therapy in patients with infantile spasms.

MEG detection of high frequency oscillations and intracranial-EEG validation in pediatric epilepsy surgery

OBJECTIVE

To assess the feasibility of automatically detecting high frequency oscillations (HFOs) in magnetoencephalography (MEG) recordings in a group of ten paediatric epilepsy surgery patients who had undergone intracranial electroencephalography (iEEG).

METHODS

A beamforming source-analysis method was used to construct virtual sensors and an automatic algorithm was applied to detect HFOs (80-250 Hz). We evaluated the concordance of MEG findings with the sources of iEEG HFOs, the clinically defined seizure onset zone (SOZ), the location of resected brain structures, and with post-operative outcome.

RESULTS

In 8/9 patients there was good concordance between the sources of MEG HFOs and iEEG HFOs and the SOZ. Significantly more HFOs were detected in iEEG relative to MEG t(71) = 2.85, p < .05. There was good concordance between sources of MEG HFOs and the resected area in patients with good and poor outcome, however HFOs were also detected outside of the resected area in patients with poor outcome.

CONCLUSION

Our findings demonstrate the feasibility of automatically detecting HFOs non-invasively in MEG recordings in paediatric patients, and confirm compatibility of results with invasive recordings.

SIGNIFICANCE

This approach provides support for the non-invasive detection of HFOs to aid surgical planning and potentially reduce the need for invasive monitoring, which is pertinent to paediatric patients.

Exclusion of the Possibility of "False Ripples" From Ripple Band High-Frequency Oscillations Recorded From Scalp Electroencephalogram in Children With Epilepsy

Aim

Ripple-band epileptic high-frequency oscillations (HFOs) can be recorded by scalp electroencephalography (EEG), and tend to be associated with epileptic spikes. However, there is a concern that the filtration of steep waveforms such as spikes may cause spurious oscillations or “false ripples.” We excluded such possibility from at least some ripples by EEG differentiation, which, in theory, enhances high-frequency signals and does not generate spurious oscillations or ringing.

Methods

The subjects were 50 pediatric patients, and ten consecutive spikes during sleep were selected for each patient. Five hundred spike data segments were initially reviewed by two experienced electroencephalographers using consensus to identify the presence or absence of ripples in the ordinary filtered EEG and an associated spectral blob in time-frequency analysis (Session A). These EEG data were subjected to numerical differentiation (the second derivative was denoted as EEG″). The EEG″ trace of each spike data segment was shown to two other electroencephalographers who judged independently whether there were clear ripple oscillations or uncertain ripple oscillations or an absence of oscillations (Session B).

Results

In Session A, ripples were identified in 57 spike data segments (Group A-R), but not in the other 443 data segments (Group A-N). In Session B, both reviewers identified clear ripples (strict criterion) in 11 spike data segments, all of which were in Group A-R (p < 0.0001 by Fisher’s exact test). When the extended criterion that included clear and/or uncertain ripples was used in Session B, both reviewers identified 25 spike data segments that fulfilled the criterion: 24 of these were in Group A-R (p < 0.0001).

Discussion

We have demonstrated that real ripples over scalp spikes exist in a certain proportion of patients. Ripples that were visualized consistently using both ordinary filters and the EEG″ method should be true, but failure to clarify ripples using the EEG″ method does not mean that true ripples are absent.

Conclusion

The numerical differentiation of EEG data provides convincing evidence that HFOs were detected in terms of the presence of such unusually fast oscillations over the scalp and the importance of this electrophysiological phenomenon.

Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children

Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.

Noninvasive high-frequency oscillations riding spikes delineates epileptogenic sources

Millions of people affected by epilepsy may undergo surgical resection of the epileptic tissues to stop seizures if such epileptic foci can be accurately delineated. High-frequency oscillations (HFOs), existing in electroencephalography, are highly correlated with epileptic brain, which is promising for guiding successful neurosurgery. However, it is unclear whether and how pathological HFOs can be differentiated to localize the epileptogenic tissues given the presence of various nonepileptic high-frequency activities. Here, we show morphological and source imaging evidence that pathological HFOs can be identified by the concurrence of epileptiform spikes. We describe a framework to delineate the underlying epileptogenicity using this biomarker. Our work may offer translational tools to improve treatments by noninvasively demarking pathological activities and hence epileptic foci.

High-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic brain and guiding successful neurosurgery. However, the utility and translation of noninvasive HFOs, although highly desirable, is impeded by the difficulty in differentiating pathological HFOs from nonepileptiform high-frequency activities and localizing the epileptic tissue using noninvasive scalp recordings, which are typically contaminated with high noise levels. Here, we show that the consistent concurrence of HFOs with epileptiform spikes (pHFOs) provides a tractable means to identify pathological HFOs automatically, and this in turn demarks an epileptiform spike subgroup with higher epileptic relevance than the other spikes in a cohort of 25 temporal epilepsy patients (including a total of 2,967 interictal spikes and 1,477 HFO events). We found significant morphological distinctions of HFOs and spikes in the presence/absence of this concurrent status. We also demonstrated that the proposed pHFO source imaging enhanced localization of epileptogenic tissue by 162% (∼5.36 mm) for concordance with surgical resection and by 186% (∼12.48 mm) with seizure-onset zone determined by invasive studies, compared to conventional spike imaging, and demonstrated superior congruence with the surgical outcomes. Strikingly, the performance of spike imaging was selectively boosted by the presence of spikes with pHFOs, especially in patients with multitype spikes. Our findings suggest that concurrent HFOs and spikes reciprocally discriminate pathological activities, providing a translational tool for noninvasive presurgical diagnosis and postsurgical evaluation in vulnerable patients.

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

OBJECTIVE

Neonatal seizures are often the first symptom of perinatal brain injury. High-frequency oscillations (HFOs) are promising new biomarkers for epileptogenic tissue and can be found in intracranial and surface EEG. To date, we cannot reliably predict which neonates with seizures will develop childhood epilepsy. We questioned whether epileptic HFOs can be generated by the neonatal brain and potentially predict epilepsy.

METHODS

We selected 24 surface EEGs sampled at 2048 Hz with 175 seizures from 16 neonates and visually reviewed them for HFOs. Interictal epochs were also reviewed.

RESULTS

We found HFOs in thirteen seizures (7%) from four neonates (25%). 5025 ictal ripples (rate 10 to 1311/min; mean frequency 135 Hz; mean duration 66 ms) and 1427 fast ripples (rate 8 to 356/min; mean frequency 298 Hz; mean duration 25 ms) were marked. Two neonates (13%) showed interictal HFOs (285 ripples and 25 fast ripples). Almost all HFOs co-occurred with sharp transients. We could not find a relationship between neonatal HFOs and outcome yet.

CONCLUSIONS

Neonatal HFOs co-occur with ictal and interictal sharp transients.

SIGNIFICANCE

The neonatal brain can generate epileptic ripples and fast ripples, particularly during seizures, though their occurrence is not common and potential clinical value not evident yet.

Scalp high-frequency oscillation rates are higher in younger children

High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the paediatric population. We prospectively recorded whole-night scalp EEG in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80-250 Hz) in bipolar channels. Children < 7 years had higher high-frequency oscillation rates (P = 0.021) when compared with older children. The median test-retest reliability of high-frequency oscillation rates reached 100% (iqr 50) for a data interval duration of 10 min. Scalp high-frequency oscillation frequency decreased with age (r = -0.558, P = 0.002), whereas scalp high-frequency oscillation duration and amplitude were unaffected. The signal-to-noise ratio improved with age (r = 0.37, P = 0.048), and the background ripple band activity decreased with age (r = -0.463, P = 0.011). We characterize the relationship of scalp high-frequency oscillation features and age in paediatric patients. EEG intervals of ≥ 10 min duration are required for reliable measurements of high-frequency oscillation rates. This study is a further step towards establishing scalp high-frequency oscillations as a valid epileptogenicity biomarker in this vulnerable age group.

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.

Seizures in the neonate: A review of etiologies and outcomes

Neonatal seizures occur in their majority in close temporal relation to an acute brain injury or systemic insult, and are accordingly defined as acute symptomatic or provoked seizures. However less frequently, unprovoked seizures may also present in the neonatal period as secondary to structural brain abnormalities, thus corresponding to structural epilepsies, or to genetic conditions, thus corresponding to genetic epilepsies. Unprovoked neonatal seizures should be thus considered as the clinical manifestation of early onset structural or genetic epilepsies that often have the characteristics of early onset epileptic encephalopathies. In this review, we address the conundrum of neonatal seizures including acute symptomatic, remote symptomatic, provoked, and unprovoked seizures, evolving to post-neonatal epilepsies, and neonatal onset epilepsies. The different clinical scenarios involving neonatal seizures, each with their distinct post-neonatal evolution are presented. The structural and functional impact of neonatal seizures on brain development and the concept of secondary epileptogenesis, with or without a following latent period after the acute seizures, are addressed. Finally, we underline the need for an early differential diagnosis between an acute symptomatic seizure and an unprovoked seizure, since it is associated with fundamental differences in clinical evolution. These are crucial aspects for neonatal management, counselling and prognostication. In view of the above aspects, we provide an outlook on future strategies and potential lines of research in this field.

Recent advances in the noninvasive detection of high-frequency oscillations in the human brain

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.

Detection of high-frequency oscillations in electroencephalography: A scoping review and an adaptable open-source framework

PURPOSE

High frequency oscillations (HFOs) are putative biomarkers of epileptogenicity. These electrophysiological phenomena can be effectively detected in electroencephalography using automated methods. Nonetheless, the implementation of these methods into clinical practice remains challenging as significant variability exists between algorithms and their characterizations of HFOs. Here, we perform a scoping review of the literature pertaining to automated HFO detection methods. In addition, we propose a framework for defining and detecting HFOs based on a simplified single-stage time-frequency based detection algorithm with clinically-familiar parameters.

METHODS

Several databases (OVID Medline, Web of Science, PubMed) were searched for articles presenting novel, automated HFO detection methods. Details related to the algorithm and various stages of data acquisition, pre-processing, and analysis were abstracted from included studies.

RESULTS

From the 261 records screened, 57 articles presented novel, automated HFO detection methods and were included in the scoping review. These algorithms were categorized into 3 groups based on their most salient features: energy thresholding, time-frequency analysis, and data mining/machine learning. Algorithms were optimized for specific datasets and suffered from low specificity. A framework for user-constrained inputs is proposed to circumvent some of the weaknesses of highly performant detectors.

CONCLUSIONS

Further efforts are required to optimize and validate existing automated HFO detection methods for clinical utility. The proposed framework may be applied to understand and standardize the variations in HFO definitions across institutions.

Longitudinal correspondence of epilepsy and scalp EEG fast (40-200 Hz) oscillations in pediatric patients with tuberous sclerosis complex

INTRODUCTION

Epilepsy associated with tuberous sclerosis complex (TSC) has very complex clinical characteristics. Scalp electroencephalogram (EEG) fast (40-200 Hz) oscillations (FOs) were recently suggested to indicate epilepsy severity. Epileptic FOs may undergo age-dependent longitudinal change in individual patients, however, and the typical pattern of such change is not yet fully clarified. We therefore investigated the age-related correspondence between clinical courses and FOs in pediatric patients with TSC-associated epilepsy.

SUBJECTS AND METHODS

FOs were semi-automatically detected from scalp sleep EEG data recorded from 23 children (15 boys, 8 girls; initial data obtained at <10 years of age) with TSC-associated epilepsy.

RESULTS

The number of FOs per patient that were associated with spikes was significantly greater than that of FOs unassociated with spikes (median 145 and 5, respectively; p = 0.0001 by the Wilcoxon signed-rank test). In the eight patients who had West syndrome (WS) in infancy, FOs associated with spikes were abundant during the WS period prior to adrenocorticotropic hormone therapy, with significantly greater numbers of FOs compared to the post-WS period (median 242 and 0, respectively; p = 0.0078). As there was no such time-dependent difference regarding FOs unassociated with spikes, FOs associated with spikes were identified as epileptic. The detected FOs included both gamma and ripple oscillations with no consistent age-dependent shifts in dominant frequency. There were no apparent age-related changes in FO duration.

CONCLUSIONS

Epileptic scalp FOs are confirmed to correspond to severity of epileptic encephalopathy, particularly in WS, even during the long-term evolutional courses of TSC-associated epilepsy.

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

OBJECTIVE

To investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms.

METHODS

We retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000 Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500 Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4 Hz and 0.5-1 Hz as quantified with modulation indices (MIs).

RESULTS

Twenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p < 0.001 by HFO rate; p < 0.01 by MIs at 3-4 Hz; p = 0.02 by MIs at 0.5-1 Hz). The presence of active spasms was predicted by the logistic regression models incorporating HFO-related metrics (AUC: 0.80-0.98) better than that incorporating hypsarrhythmia (AUC: 0.61). The predictive performance of the best model remained favorable (87.5% accuracy) after a cross-validation procedure.

CONCLUSIONS

Increased rate of HFOs and coupling between HFOs and SWA are associated with active epileptic spasms.

SIGNIFICANCE

Scalp-recorded HFOs may serve as an objective EEG biomarker for active epileptic spasms.

Pitfalls in Scalp High-Frequency Oscillation Detection From Long-Term EEG Monitoring

Aims: Intracranially recorded high-frequency oscillations (>80 Hz) are considered a candidate epilepsy biomarker. Recent studies claimed their detectability on the scalp surface. We aimed to investigate the applicability of high-frequency oscillation analysis to routine surface EEG obtained at an epilepsy monitoring unit. Methods: We retrospectively analyzed surface EEGs of 18 patients with focal epilepsy and six controls, recorded during sleep under maximal medication withdrawal. As a proof of principle, the occurrence of motor task-related events during wakefulness was analyzed in a subsample of six patients with seizure- or syncope-related motor symptoms. Ripples (80-250 Hz) and fast ripples (>250 Hz) were identified by semi-automatic detection. Using semi-parametric statistics, differences in spontaneous and task-related occurrence rates were examined within subjects and between diagnostic groups considering the factors diagnosis, brain region, ripple type, and task condition. Results: We detected high-frequency oscillations in 17 out of 18 patients and in four out of six controls. Results did not show statistically significant differences in the mean rates of event occurrences, neither regarding the laterality of the epileptic focus, nor with respect to active and inactive task conditions, or the moving hand laterality. Significant differences in general spontaneous incidence [WTS(1) = 9.594; p = 0.005] that indicated higher rates of fast ripples compared to ripples, notably in patients with epilepsy compared to the control group, may be explained by variations in data quality. Conclusion: The current analysis methods are prone to biases. A common agreement on a standard operating procedure is needed to ensure reliable and economic detection of high-frequency oscillations.

Impacting development in infants with tuberous sclerosis complex: Multidisciplinary research collaboration

The Tuberous Sclerosis Complex Autism Center of Excellence Network (TACERN) is a 6-site collaborative conducting longitudinal research on infants with tuberous sclerosis complex (TSC), focused on identifying early biomarkers for autism spectrum disorder (ASD). A multidisciplinary research team that includes the specialties of psychology, neurology, pediatrics, medical genetics, and speech-language pathology, its members work together to conduct studies on neurological status, brain structure and function, neurodevelopmental phenotype, and behavioral challenges in this population. This article provides insights into the roles of the multidisciplinary multisite team and lessons learned from the collaboration, in terms of research as well as training of future researchers and clinicians. In addition, the authors detail the major findings to date, including those related to the identification and measurement of early symptoms of ASD, relationship between seizures and early development, and early biomarkers for epilepsy and developmental delay in infants and young children with TSC. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Interictal scalp fast ripple occurrence and high frequency oscillation slow wave coupling in epileptic spasms

OBJECTIVE

Intracranial high frequency oscillation (HFO) occurrence rate (OR) and slow wave activity (SWA) coupling are potential markers of epileptogenicity in epileptic spasms (ES). Scalp ripple (R) detection and SWA coupling have been described in ES; however, the feasibility of scalp fast ripple (FR) detection and measurement of scalp FR coupling to SWA is not known. We evaluated interictal scalp R and FR OR and SWA coupling in pre-treatment EEG in children with short-term treatment-refractory ES compared to short-term treatment non-refractory ES.

METHODS

We retrospectively identified children with ES and identified HFOs using a semi-automated HFO detector on pre-treatment scalp EEG during sleep. We evaluated HFO OR and event-triggered modulation index (MI) to quantify R (100-250 Hz) and FR (250-600 Hz) coupling strength with different SWA passbands (0.5-1, 1-2, 2-3, 3-4, and 4-8 Hz). We used HFO phasor transform and circular statistics to evaluate phase coupling angle distributions.

RESULTS

We identified 15 children with ES with pre-treatment EEG recorded at 2000 Hz. Thirteen out of 15 patients had HFOs and were included for analysis. There were six treatment responders and seven nonresponders three months after treatment initiation. Responders and nonresponders were similar in age (6.1 vs 7.2 mo), ES diagnosis duration (0.7 vs 2.6 mo), and HFO OR (R: 1.07 vs 2.30/min, FR: 0.43 vs 1.96/min). No differences between responders and nonresponders were seen in HFO MI at different SWA. Coupling of R and FR to 2-3 Hz SWA demonstrated increased incidence rate ratio in nonresponders relative to responders at distinct phase coupling angle distributions.

CONCLUSIONS

This study demonstrates the feasibility of interictal scalp R and FR detection and quantification of scalp R and FR coupling to SWA in ES.

SIGNIFICANCE

HFO phase coupling with SWA may be useful as a marker of potential treatment refractoriness in patients with ES.

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic–ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia–ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80–120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms.

Modifying genetic epilepsies - Results from studies on tuberous sclerosis complex

Tuberous sclerosis complex (TSC) is an autosomal dominant neurocutaneous disorder affecting approximately 1 in 6,000 in general population and represents one of the most common genetic causes of epilepsy. Epilepsy affects 90% of the patients and appears in the first 2 years of life in the majority of them. Early onset of epilepsy in the first year of life is associated with high risk of cognitive decline and neuropsychiatric problems including autism. Recently TSC has been recognized as a model of genetic epilepsies. TSC is a genetic condition with known dysregulated mTOR pathway and is increasingly viewed as a model for human epileptogenesis. Moreover, TSC is characterized by a hyperactivation of mTOR (mammalian target of rapamycin) pathway, and mTOR activation was showed to be implicated in epileptogenesis in many animal models and human epilepsies. Recently published studies documented positive effect of preventive or disease modifying treatment of epilepsy in infants with high risk of epilepsy with significantly lower incidence of epilepsy and better cognitive outcome. Further studies on preventive treatment of epilepsy in other genetic epilepsies of early childhood are considered. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy

High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R² = 0.80, p < 0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p = 0.0066, PPV = 86%, NPV = 80%, accuracy = 84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.

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

Pathological high-frequency oscillations (HFOs), specifically fast ripples (FRs, >250 Hz), are pathognomonic of an active epileptogenic zone. However, the origin of FRs remains unknown. Here we explored the correlation between FRs recorded with intraoperative pre-resection electrocorticography (ECoG) and spontaneous synaptic activity recorded ex vivo from cortical tissue samples resected for the treatment of pharmacoresistant epilepsy. The cohort included 47 children (ages 0.22-9.99 yr) with focal cortical dysplasias (CD types I and II), tuberous sclerosis complex (TSC) and non-CD pathologies. Whole-cell patch clamp recordings were obtained from pyramidal neurons and interneurons in cortical regions that were positive or negative for pathological HFOs, defined as FR band oscillations (250-500 Hz) at ECoG. The frequency of spontaneous excitatory and inhibitory postsynaptic currents (sEPSCs and IPSCs, respectively) was compared between HFO+ and HFO- regions. Regardless of pathological substrate, regions positive for FRs displayed significantly increased frequencies of sIPSCs compared with regions negative for FRs. In contrast, the frequency of sEPSCs was similar in both regions. In about one third of cases (n = 17), pacemaker GABA synaptic activity (PGA) was observed. In the vast majority (n = 15), PGA occurred in HFO+ areas. Further, fast-spiking interneurons displayed signs of hyperexcitability exclusively in HFO+ areas. These results indicate that, in pediatric epilepsy patients, increased GABA synaptic activity is associated with interictal FRs in the epileptogenic zone and suggest an active role of GABAergic interneurons in the generation of pathological HFOs. Increased GABA synaptic activity could serve to dampen excessive excitability of cortical pyramidal neurons in the epileptogenic zone, but it could also promote neuronal network synchrony.

Resting-State fMRI Networks in Children with Tuberous Sclerosis Complex

BACKGROUND AND PURPOSE

There are no published studies examining resting state networks (RSNs) and their relationship with neurodevelopmental metrics in tuberous sclerosis complex (TSC). We aimed to identify major resting-state functional magnetic resonance imaging (rs-fMRI) networks in infants with TSC and correlate network analyses with neurodevelopmental assessments, autism diagnosis, and seizure history.

METHODS

Rs-fMRI data from 34 infants with TSC, sedated with propofol during the scan, were analyzed to identify auditory, motor, and visual RSNs. We examined the correlations between auditory, motor, and visual RSNs at approximately 11.5 months, neurodevelopmental outcome at approximately 18.5 months, and diagnosis of autism spectrum disorders at approximately 36 months of age.

RESULTS

RSNs were obtained in 76.5% (26/34) of infants. We observed significant negative correlations between auditory RSN and auditory comprehension test scores (p = .038; r = -.435), as well as significant positive correlations between motor RSN and gross motor skills test scores (p = .023; r = .564). Significant positive correlations between motor RSNs and gross motor skills (p = .012; r = .754) were observed in TSC infants without autism, but not in TSC infants with autism, which could suggest altered motor processing. There were no significant differences in RSNs according to seizure history.

CONCLUSIONS

Negative correlation between auditory RSN, as well as positive correlation between motor RSN and developmental outcome measures might reflect different brain mechanisms and, when identified, may be helpful in predicting later function. A larger study of TSC patients with a healthy control group is needed before auditory and motor RSNs could be considered as neurodevelopmental outcome biomarkers.

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.

Simultaneously recorded intracranial and scalp high frequency oscillations help identify patients with poor postsurgical seizure outcome

OBJECTIVE

High frequency oscillations (HFO) between 80-500 Hz are markers of epileptic areas in intracranial and maybe also scalp EEG. We investigate simultaneous recordings of scalp and intracranial EEG and hypothesize that scalp HFOs provide important additional clinical information in the presurgical setting.

METHODS

Spikes and HFOs were visually identified in all intracranial scalp EEG channels. Analysis of correlation of event location between intracranial and scalp EEG as well as relationship between events and the SOZ and zone of surgical removal was performed.

RESULTS

24 patients could be included, 23 showed spikes and 19 HFOs on scalp recordings. In 15/19 patients highest scalp HFO rate was located over the implantation side, with 13 patients having the highest scalp and intracranial HFO rate over the same region. 17 patients underwent surgery, 7 became seizure free. Patients with poor post-operative outcome showed significantly more regions with HFO than those with seizure free outcome.

CONCLUSIONS

Scalp HFOs are mostly located over the SOZ. Widespread scalp HFOs are indicative of a larger epileptic network and associated with poor postsurgical outcome.

SIGNIFICANCE

Analysis of scalp HFO add clinically important information about the extent of epileptic areas during presurgical simultaneous scalp and intracranial EEG recordings.

Interrater reliability in visual identification of interictal high-frequency oscillations on electrocorticography and scalp EEG

High-frequency oscillations (HFOs), including ripples (Rs) and fast ripples (FRs), are promising biomarkers of epileptogenesis, but their clinical utility is limited by the lack of a standardized approach to identification. We set out to determine whether electroencephalographers experienced in HFO analysis can reliably identify and quantify interictal HFOs. Two blinded raters independently reviewed 10 intraoperative electrocorticography (ECoG) samples from epilepsy surgery cases, and 10 scalp EEG samples from epilepsy monitoring unit evaluations. HFOs were visually marked using bandpass filters (R, 80-250 Hz; FR, 250-500 Hz) with a sampling frequency of 2,000 Hz. There was agreement as to the presence or absence of epileptiform discharges (EDs), Rs, and FRs, in 17, 18, and 18 cases, respectively. Interrater reliability (IRR) was favorable with κ = 0.70, 0.80, and 0.80, respectively, and similar for ECoG and scalp electroencephalography (EEG). Furthermore, interclass correlation for rates of Rs (0.99, 95% confidence interval [CI] 0.96-0.99) and FRs (0.77, 95% CI 0.41-0.91) were superior in comparison to EDs (0.37, 95% CI -0.60 to 0.75). Our data suggest that HFO identification and quantification are reliable among experienced electroencephalographers. Our findings support the reliability of utilizing HFO data in both research and clinical arenas.