Significance of High-frequency Electrical Brain Activity

　Electroencephalogram (EEG) data include broadband electrical brain activity ranging from infra-slow bands (200 / 250 Hz, respectively) are particularly of note due to their very close relationship to epileptogenicity, with the possibility that they could function as a surrogate biomarker of epileptogenicity. In contrast, physiological high-frequency activity plays an important role in higher brain functions, and the differentiation between pathological / epileptic and physiological HFOs is a critical issue, especially in epilepsy surgery. HFOs were initially recorded with intracranial electrodes in patients with intractable epilepsy as part of a long-term invasive seizure monitoring study. However, fast oscillations (FOs) in the ripple and gamma bands (40-80 Hz) are now noninvasively detected by scalp EEG and magnetoencephalography, and thus the scope of studies on HFOs /FOs is rapidly expanding

Scalp HFO rates are higher for larger lesions

High frequency oscillations (HFO) in scalp EEG are a new and promising non-invasive 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=0.01) that were detectable in a higher number of channels (p=0.05). 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

Sleep-related epileptic behaviors and non-REM-related parasomnias: Insights from stereo-EEG

During the last decade, many clinical and pathophysiological aspects of sleep-related epileptic and non-epileptic paroxysmal behaviors have been clarified. Advances have been achieved in part through the use of intracerebral recording methods such as stereo-electroencephalography (S-EEG), which has allowed a unique "in vivo" neurophysiological insight into focal epilepsy. Using S-EEG, the local features of physiological and pathological EEG activity in different cortical and subcortical structures have been better defined during the entire sleep-wake spectrum. For example, S-EEG has contributed to clarify the semiology of sleep-related seizures as well as highlight the specific epileptogenic networks involved during ictal activity. Moreover, intracerebral EEG recordings derived from patients with epilepsy have been valuable to study sleep physiology and specific sleep disorders. The occasional co-occurrence of NREM-related parasomnias in epileptic patients undergoing S-EEG investigation has permitted the recordings of such events, highlighting the presence of local electrophysiological dissociated states and clarifying the underlying pathophysiological substrate of such NREM sleep disorders. Based on these recent advances, the authors review and summarize the current and relevant S-EEG literature on sleep-related hypermotor epilepsies and NREM-related parasomnias. Finally, novel data and future research hypothesis will be discussed

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.The presented research was supported by the Austrian Science Fund (FWF): T 798-B27 and KLI657-B31 and by the Research Fund of the Paracelsus Medical University (PMU-FFF): A16/02/021-HÖL and A-18/01/029-HÖL.Peer Reviewe

Electrical source imaging of interictal spikes using multiple sparse volumetric priors for presurgical epileptogenic focus localization

ABSTRACT: Electrical source imaging of interictal spikes observed in EEG recordings of patients with refractory epilepsy provides useful information to localize the epileptogenic focus during the presurgical evaluation. However, the selection of the time points or time epochs of the spikes in order to estimate the origin of the activity remains a challenge. In this study, we consider a Bayesian EEG source imaging technique for distributed sources, i.e. the multiple volumetric sparse priors (MSVP) approach. The approach allows to estimate the time courses of the intensity of the sources corresponding with a specific time epoch of the spike. Based on presurgical averaged interictal spikes in six patients who were successfully treated with surgery, we estimated the time courses of the source intensities for three different time epochs: (i) an epoch starting 50 ms before the spike peak and ending at 50% of the spike peak during the rising phase of the spike, (ii) an epoch starting 50 ms before the spike peak and ending at the spike peak and (iii) an epoch containing the full spike time period starting 50 ms before the spike peak and ending 230 ms after the spike peak. To identify the primary source of the spike activity, the source with the maximum energy from 50 ms before the spike peak till 50% of the spike peak was subsequently selected for each of the time windows. For comparison, the activity at the spike peaks and at 50% of the peaks was localized using the LORETA inversion technique and an ECD approach. Both patient-specific spherical forward models and patient-specific 5-layered finite difference models were considered to evaluate the influence of the forward model. Based on the resected zones in each of the patients, extracted from post-operative MR images, we compared the distances to the resection border of the estimated activity. Using the spherical models, the distances to the resection border for the MSVP approach and each of the different time epochs were in the same range as the LORETA and ECD techniques. We found distances smaller than 23 mm, with robust results for all the patients. For the finite difference models, we found that the distances to the resection border for the MSVP inversions of the full spike time epochs were generally smaller compared to the MSVP inversions of the time epochs before the spike peak. The results also suggest that the inversions using the finite difference models resulted in slightly smaller distances to the resection border compared to the spherical models. The results we obtained are promising because the MSVP approach allows to study the network of the estimated source-intensities and allows to characterize the spatial extent of the underlying sources

High-frequency oscillations in scalp EEG: A systematic review of methodological choices and clinical findings

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

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

Influence of deep structures on the EEG and their invasive and non-invasive assessment

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Medicina, Departamento de Fisiología, leída el 22-11-2019El EEG es la prueba diagnóstica de mayor utilidad en el diagnóstico de la epilepsia. Consiste esencialmente en la representación gráfica de los potenciales postsinápticos generados en las neuronas piramidales de la corteza. Los campos eléctricos registrados en la superficie tienen principalmente dos mecanismos de origen: conducción de volumen desde regiones adyacentes y propagación interneuronal sináptica. Las neuronal piramidales se agrupan formando microcircuitos locales siendo estos circuitos los responsables de la generación delos ritmos registrados en el EEG. Uno de los principales retos de la electroencefalografía consiste en descifrar la relación entre la actividad registrada y la actividad subyacente en las redes neuronales. Para encontrar la fuente de dichas actividades, es necesario tener en cuenta complejos mecanismos tanto no lineales como lineales, así como el efecto de la conducción de volumen y la influencia de la morfología y las propiedades eléctricas del cerebro y el cráneo. Además, las regiones cerebrales se encuentran profusamente interconectadas a menudo produciendo una modulación recíproca que añade un mayor grado complejidad...The EEG is the most valuable diagnostic test in epilepsy. In essence, it mainly consists in agraphical representation of the summated postsynaptic potentials generated in the pyramidal neurons from the cortex. The electrical fields can be generated on the scalp by two mechanisms: volume conduction from nearby regions and synaptic inter‐neuronal propagation. Pyramidal cells align conforming local microcircuit configurations which activation lead to the generation of EEG rhythms. One of the main challenges of EEG is to decipher the relation between the recorded EEG activity and the activity in the neuronal networks. To find the source of EEG activity, complex non‐linear and linear mechanisms as well as volume conduction effect and influence of the shape and electrical properties of the brain and skull need to be taken in consideration. In addition, brain regions are profusely interconnected and functionally connected regions often produce mutual modulation that adds additional complexity...Depto. de FisiologíaFac. de MedicinaTRUEunpu

Automatic detection and visualisation of MEG ripple oscillations in epilepsy

High frequency oscillations (HFOs, 80–500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting