Jurák, Pavel

Abstract Objective The upper frequency limit of human brain activity remains unknown. Using ultrahigh sampling rate (≥20 kHz) intracranial microelectroencephalography, this study aimed to systematically explore and quantitatively characterize brain field oscillations beyond the established high‐frequency oscillation range (>2 kHz), and to determine their relationship to epileptogenic tissue. Methods We analyzed intracranial electroencephalographic recordings from 15 patients with drug‐resistant epilepsy, comprising 466 microcontacts and 434 macrocontacts implanted in mesiotemporal structures across four international epilepsy centers. A custom spectrogram‐based detector optimized for ultrahigh frequencies was developed to identify ultrafast oscillations (UFOs; >2 kHz). UFO rates were assessed in epileptic (successfully resected) and nonepileptic hippocampi and compared with established electrophysiological biomarkers, including interictal epileptiform discharges, ripples, and fast ripples. Statistical comparisons were performed using mixed‐effects models to account for intersubject and interelectrode variability. Results We identified a previously undescribed class of short‐duration oscillatory events spanning 2–8 kHz. UFOs occurred at significantly higher rates in epileptic compared with nonepileptic mesiotemporal regions, with the strongest differentiation observed in the 2–3‐kHz band. Two distinct UFO phenotypes were consistently observed: (1) spindlelike, narrow‐band oscillations and (2) sharp‐onset, rapidly decaying bursts. Both forms were prevalent within epileptic hippocampi but were exceedingly rare in nonepileptic structures. UFOs exhibited pronounced temporal intermittency and spatial focality and frequently arose independently of ripples, fast ripples, and very high‐frequency oscillations, indicating that they represent a distinct electrophysiological phenomenon. Macrocontacts detected UFOs only exceptionally, highlighting the necessity of microelectrode recordings to capture activity in this ultrahigh‐frequency regime. Significance These findings substantially extend the known frequency range of human brain field activity and identify ultrafast oscillations as a novel biomarker of neuronal hyperexcitability. UFOs likely reflect pathological microcircuit dynamics inaccessible to conventional clinical recordings and provide new insights into the organization and pathophysiology of epileptogenic networks.

Abstract Precise localization of the epileptogenic zone is pivotal for planning minimally invasive surgeries in drug-resistant epilepsy. Here, we present a graph neural network (GNN) framework that integrates interictal intracranial EEG features, electrode topology, and MRI features to automate epilepsy surgery planning. We retrospectively evaluated the model using leave-one-patient-out cross-validation on a dataset of 80 drug-resistant epilepsy patients treated at St. Anne's University Hospital (Brno, Czech Republic), comprising 31 patients with good postsurgical outcomes (Engel I) and 49 with poor outcomes (Engel II–IV). The GNN predictions demonstrated a significantly better (P < 0.05, Mann–Whitney-U test) area under the precision-recall curve in patients with good outcomes (area under the precision-recall curve: 0.69) compared with those with poor outcomes (area under the precision-recall curve: 0.33), indicating that the model captures clinically relevant targets in successful cases. In patients with poor outcomes, the graph neural network proposed alternative intervention sites that diverged from the original clinical plans, highlighting its potential to identify alternative therapeutic targets. We show that topology-aware GNNs significantly outperformed (P < 0.05, Wilcoxon signed-rank test) traditional neural networks while using the same intracranial EEG features, emphasizing the importance of incorporating implantation topology into predictive models. These findings uncover the potential of GNNs to automatically suggest targets for epilepsy surgery, which can assist the clinical team during the planning process.

AbstractObjectiveHigh‐frequency oscillations are considered among the most promising interictal biomarkers of the epileptogenic zone in patients suffering from pharmacoresistant focal epilepsy. However, there is no clear definition of pathological high‐frequency oscillations, and the existing detectors vary in methodology, performance, and computational costs. This study proposes relative entropy as an easy‐to‐use novel interictal biomarker of the epileptic tissue.MethodsWe evaluated relative entropy and high‐frequency oscillation biomarkers on intracranial electroencephalographic data from 39 patients with seizure‐free postoperative outcome (Engel Ia) from three institutions. We tested their capability to localize the epileptogenic zone, defined as resected contacts located in the seizure onset zone. The performance was compared using areas under the receiver operating curves (AUROCs) and precision‐recall curves. Then we tested whether a universal threshold can be used to delineate the epileptogenic zone across patients from different institutions.ResultsRelative entropy in the ripple band (80–250 Hz) achieved an average AUROC of .85. The normalized high‐frequency oscillation rate in the ripple band showed an identical AUROC of .85. In contrast to high‐frequency oscillations, relative entropy did not require any patient‐level normalization and was easy and fast to calculate due to its clear and straightforward definition. One threshold could be set across different patients and institutions, because relative entropy is independent of signal amplitude and sampling frequency.SignificanceAlthough both relative entropy and high‐frequency oscillations have a similar performance, relative entropy has significant advantages such as straightforward definition, computational speed, and universal interpatient threshold, making it an easy‐to‐use promising biomarker of the epileptogenic zone.

Abstract Objective This study investigates high‐frequency oscillations ( HFO s; 65–600 Hz) as a biomarker of epileptogenic brain and explores three barriers to their clinical translation: (1) Distinguishing pathological HFO s (path HFO ) from physiological HFO s (phys HFO ). (2) Classifying tissue under individual electrodes as epileptogenic (3) Reproducing results across laboratories. Methods We recorded HFO s using intracranial EEG ( iEEG ) in 90 patients with focal epilepsy and 11 patients without epilepsy. In nine patients with epilepsy putative phys HFO s were induced by cognitive or motor tasks. HFO s were identified using validated detectors. A support vector machine ( SVM ) using HFO features was developed to classify tissue under individual electrodes as normal or epileptogenic. Results There was significant overlap in the amplitude, frequency, and duration distributions for spontaneous phys HFO , task induced phys HFO , and path HFO , but the amplitudes of the path HFO were higher ( P < 0.0001). High gamma path HFO had the strongest association with seizure onset zone ( SOZ ), and were elevated on SOZ electrodes in 70% of epilepsy patients ( P < 0.0001). Failure to resect tissue generating high gamma path HFO was associated with poor outcomes ( P < 0.0001). A SVM classified individual electrodes as epileptogenic with 63.9% sensitivity and 73.7% specificity using SOZ as the target. Interpretation A broader range of interictal path HFO (65–600 Hz) than previously recognized are biomarkers of epileptogenic brain, and are associated with SOZ and surgical outcome. Classification of HFO s into physiological or pathological remains challenging. Classification of tissue under individual electrodes was demonstrated to be feasible. The open source data and algorithms provide a resource for future studies.

ObjectiveIn the present study, we aimed to investigate depth electroencephalographic (EEG) recordings in a large cohort of patients with drug‐resistant epilepsy and to focus on interictal very high‐frequency oscillations (VHFOs) between 500Hz and 2kHz. We hypothesized that interictal VHFOs are more specific biomarkers for epileptogenic zone compared to traditional HFOs.MethodsForty patients with focal epilepsy who underwent presurgical stereo‐EEG (SEEG) were included in the study. SEEG data were recorded with a sampling rate of 25kHz, and a 30‐minute resting period was analyzed for each patient. Ten patients met selected criteria for analyses of correlations with surgical outcome: detection of interictal ripples (Rs), fast ripples (FRs), and VHFOs; resective surgery; and at least 1 year of postoperative follow‐up. Using power envelope computation and visual inspection of power distribution matrixes, electrode contacts with HFOs and VHFOs were detected and analyzed.ResultsInterictal very fast ripples (VFRs; 500–1,000Hz) were detected in 23 of 40 patients and ultrafast ripples (UFRs; 1,000–2,000Hz) in almost half of investigated subjects (n = 19). VFRs and UFRs were observed only in patients with temporal lobe epilepsy and were recorded exclusively from mesiotemporal structures. The UFRs were more spatially restricted in the brain than lower‐frequency HFOs. When compared to R oscillations, significantly better outcomes were observed in patients with a higher percentage of removed contacts containing FRs, VFRs, and UFRs.InterpretationInterictal VHFOs are relatively frequent abnormal phenomena in patients with epilepsy, and appear to be more specific biomarkers for epileptogenic zone when compared to traditional HFOs. Ann Neurol 2017;82:299–310

The function and connectivity of human brain is disrupted in epilepsy. We previously reported that the region of epileptic brain generating focal seizures, i.e., the seizure onset zone (SOZ), is functionally isolated from surrounding brain regions in focal neocortical epilepsy. The modulatory effect of behavioral state on the spatial and spectral scales over which the reduced functional connectivity occurs, however, is unclear. Here we use simultaneous sleep staging from scalp EEG with intracranial EEG recordings from medial temporal lobe to investigate how behavioral state modulates the spatial and spectral scales of local field potential synchrony in focal epileptic hippocampus. The local field spectral power and linear correlation between adjacent electrodes provide measures of neuronal population synchrony at different spatial scales, ∼1 and 10 mm, respectively. Our results show increased connectivity inside the SOZ and low connectivity between electrodes in SOZ and outside the SOZ. During slow-wave sleep, we observed decreased connectivity for ripple and fast ripple frequency bands within the SOZ at the 10 mm spatial scale, while the local synchrony remained high at the 1 mm spatial scale. Further study of these phenomena may prove useful for SOZ localization and help understand seizure generation, and the functional deficits seen in epileptic eloquent cortex.

Background— We postulated that the variability of the phase shift between blood pressure and heart rate fluctuation near the frequency of 0.10 Hz might be useful in assessing autonomic circulatory control. Methods and Results— We tested this hypothesis in 4 groups of subjects: 28 young, healthy individuals; 13 elderly healthy individuals; 25 patients with coronary heart disease; and 19 patients with a planned or implanted cardioverter-defibrillator (ICD recipients). Data from 5 minutes of free breathing and at 2 different, controlled breathing frequencies (0.10 and 0.33 Hz) were used. Clear differences ( P <0.001) in variability of phase were evident between the ICD recipients and all other groups. Furthermore, at a breathing frequency of 0.10 Hz, differences in baroreflex sensitivity ( P <0.01) also became evident, even though these differences were not apparent at the 0.33-Hz breathing frequency. Conclusions— The frequency of 0.10 Hz represents a useful and potentially important one for controlled breathing, at which differences in blood pressure–RR interactions become evident. These interactions, whether computed as a variability of phase to define stability of the blood pressure–heart rate interaction or defined as the baroreflex sensitivity to define the gain in heart rate response to blood pressure changes, are significantly different in patients at risk for sudden arrhythmic death. In young versus older healthy individuals, only baroreflex gain is different, with the variability of phase being similar in both groups. These measurements of short-term circulatory control might help in risk stratification for sudden cardiac death.

Background— We examined the effects of the various sleep stages on RR and QT intervals in healthy subjects and tested the hypothesis that there is a differential effect of sleep stage on QT interval in women compared with men. Methods and Results— Eighteen healthy subjects (9 women, age 22 to 45 years) underwent polysomnography and simultaneous recording of ECG, blood pressure, and respiration. RR interval, RR variability, and QT values were measured in stable conditions (no abrupt changes of heart rate or blood pressure, stable breathing pattern) during inactive wakefulness during stages 2 and 3 to 4 of non-REM sleep and during REM sleep. The absolute QT interval was normalized for variations of RR (QTc). In men, RR interval and RR variability increased through all sleep stages. The QTc remained stable from wakefulness through all sleep stages. In women, however, RR interval increased only during non-REM and was virtually identical in wakefulness and in REM. RR variability remained very stable from wakefulness through all stages of sleep. Also, during REM in women, both absolute QT interval and QTc, regardless of the correction maneuver used, increased compared with wakefulness. Conclusions— The influence of sleep on RR, RR variability, and QTc is sex-dependent. We speculate that these differential sex effects on cardiac rate and repolarization may have important implications for sleep-selected cardiac arrhythmias in women.