Interictal epileptiform discharges (IEDs) often co-occur across spatially-separated cortical regions, forming IED networks. However, the factors prompting IED propagation remain unelucidated. We hypothesized that slow oscillations (SOs) might facilitate IED propagation. Here, the amplitude and phase synchronization of SOs preceding propagating and non-propagating IEDs were compared in 22 patients with focal epilepsy undergoing intracranial electroencephalography (EEG) evaluation. Intracranial channels were categorized into the irritative zone (IZ) and normal zone (NOZ) regarding the presence of IEDs. During wakefulness, we found that pre-IED SOs within the IZ exhibited higher amplitudes for propagating IEDs than non-propagating IEDs (delta band: p = 0.001, theta band: p < 0.001). This increase in SOs was also concurrently observed in the NOZ (delta band: p = 0.04). Similarly, the inter-channel phase synchronization of SOs prior to propagating IEDs was higher than those preceding non-propagating IEDs in the IZ (delta band: p = 0.04). Through sliding window analysis, we observed that SOs preceding propagating IEDs progressively increased in amplitude and phase synchronization, while those preceding non-propagating IEDs remained relatively stable. Significant differences in amplitude occurred approximately 1150 ms before IEDs. During non-rapid eye movement (NREM) sleep, SOs on scalp recordings also showed higher amplitudes before intracranial propagating IEDs than before non-propagating IEDs (delta band: p = 0.006). Furthermore, the analysis of IED density around sleep SOs revealed that only high-amplitude sleep SOs demonstrated correlation with IED propagation. Overall, our study highlights that transient but widely distributed SOs are associated with IED propagation as well as generation in focal epilepsy during sleep and wakefulness, providing new insight into the EEG substrate supporting IED networks.

Abstract Reward expectancy shapes behavioral performance by coordinating the allocation of cognitive resources, which incorporates the involvement of the anterior insular cortex (AIC). To investigate AIC’s electrophysiological mechanisms during reward expectancy, we collected intracranial electroencephalographic data from epilepsy patients implanted for clinical purposes. During recording, they navigated a virtual T-maze where they encountered rewards at three predetermined locations. We focused on the time window proximal to entering the reward zone, defined as the expectancy stage. During this stage, a robust phase-amplitude coupling (PAC) between theta oscillation and gamma activity was found within the AIC. This PAC exhibited a specific temporal structure, with peak gamma activity progressively coupling to an earlier theta phase before each reward onset. These phase shift phenomena mirrored the phase precession effect, termed phase-precession-like effects (PPLEs). Meanwhile, the reward-specific neural patterns were pre-activated during the expectancy stage of rewards, coinciding with peak gamma activities across trials. Additionally, subjects exhibiting PPLEs in the AIC presented reduced variability and a more pronounced enhancement in response latency across trials. These results revealed potential electrophysiological mechanisms of the AIC underlying reward expectancy.

AbstractObjectiveThe ictalHarmonicpattern (Hpattern), produced by the non-linear characteristics of EEG waveforms, may hold significant potential for localizing the epileptogenic zone (EZ) in focal epilepsy. However, further validation is needed to establish theHpattern’s effectiveness as a biomarker for measuring the EZ.MethodsWe retrospectively enrolled 131 patients diagnosed with drug-resistant focal epilepsy, all of whom had complete stereo-electroencephalographic (SEEG) data. From this cohort, we selected 85 patients for outcome analysis. We analyzed the morphological and time-frequency (TF) features of theHpattern using TF plots. A third quartile (Q3) threshold was applied to classify channels expressing either dominant (ChanneldHpattern) or non-dominantHpatterns (Channelnon-dHpattern). We then examined associations between the morphological features of theHpattern and patients’ clinical characteristics, as well as the correlations between the extent of channel removal and seizure outcomes.ResultsWe found no significant correlations between the morphological features of the ictalHpattern and clinical factors, including lesional MRI findings, epileptic onset patterns, epilepsy type, pathology, or surgical outcomes. The non-localizableHpattern appeared exclusively in patients with non-focal onset patterns. Notably, the proportion ofChanneldHpatternwas higher in the seizure-onset zone (SOZ) compared to the early propagation zone. The seizure-free group demonstrated significantly higher removal proportions ofChanneldHpattern, both within and outside the SOZ (p= 0.014;p= 0.036), with AUCs of 0.606 and 0.660, respectively, in a seizure freedom prediction model. Survival analysis confirmed that complete removal of these regions correlated with long-term seizure freedom (p= 0.008;p= 0.028). Further subgroup analysis showed a significant correlation in neocortical epilepsy (p= 0.0004;p= 0.011), but not in mesial temporal lobe epilepsy. Additionally, multivariate analysis identified the complete removal ofChanneldHpatternas the only independent predictor for seizure freedom (p= 0.022; OR 6.035, 95% CI 1.291-28.211).ConclusionsOur study supports the notion that the dominance of the ictalHpattern, regardless of its morphology, serves as a novel biomarker for the EZ in focal epilepsy. The non-linearity in EEG waveforms provides new insights into understanding ictal spreading propagation and offers potential improvements for surgical planning in neocortical epilepsy.

Reward expectancy contributes to behavioral performance by coordinating the allocation of cognitive resources, which incorporates the involvement of the anterior insular cortex (AIC). To investigate the electrophysiological mechanisms underlying reward expectancy in the AIC, we collected intracranial electroencephalography (iEEG) data from epilepsy patients implanted for clinical purposes. During the recording, they navigated a virtual T-maze where they encountered rewards at three predetermined locations. We focused on the time window proximal to entering the reward zone, which was defined as the expectancy stage. During this stage, a strong phase-amplitude coupling (PAC) effect was found between local theta oscillation and gamma activity within the AIC. Interestingly, the PAC effect exhibited a specific temporal structure in which the peak gamma power was progressively coupled to an earlier theta phase before each onset of the same reward. Meanwhile, the reward-specific brain patterns were pre-activated during the expectancy stage of rewards. This pre-activation was tended to take place simultaneously with the occurrence of peak gamma activities across individual trials. Furthermore, the pre-activation of reward-specific patterns also progressively shifted forward on theta phases as more trials were conducted. These phenomena of phase shift were reminiscent of the phase precession effect in which single neurons fire at progressively earlier phases of low-frequency oscillations, and thus were called phase-precession-like effects (PPLEs). Additionally, subjects exhibiting PPLEs in the AIC presented less variability and a more notable improvement in response latency across trials. These results revealed possible electrophysiological mechanisms of the AIC underlying reward expectancy.

The ictal EEG biomarkers of the epileptogenic zone (EZ) need to be better defined. The power and structure of ictal fast activity are important in EZ localization, but EEG onset patterns are heterogeneous and initial fast activity is absent in many patients. Here we defined a unique spectral structure of "harmonic pattern" (H pattern) on stereo-EEG (SEEG), characterized by multiple equidistant, high-density bands with varying frequency on time frequency map. H pattern was commonly observed among 57 (81.4%) out of 70 patients with focal onset pattern on SEEG. It was presented in seizures with various ictal onset patterns with or without fast activity, and during early or late stage of seizures. H pattern usually expressed at very close time point across the seizure onset zone (SOZ), primary propagation zone and sometimes other areas, with the same fundamental difference, reflecting an inter-regional synchronization within the ictal network during this time. Notably, SOZ showed the highest proportion of channels expressing H-pattern, and also highest band number of H-pattern. At patient level, the dominant H pattern was defined as those with high rank in band numbers (the third quartile, Q3). Resection of the region expressing dominant H pattern, but not SOZ, independently predicted seizure freedom after surgery, suggesting it is an ictal marker of EZ. How H pattern was produced was then investigated. It only embedded into two types of EEG segments: fast activity with a frequency >25Hz (FA-H pattern) at early seizure propagation (mean 13.3 sec after onset), and irregular polyspikes (> 5 Hz, PS-H pattern) during late propagation (mean 23.3 sec after onset). Nonlinear analysis was used to unravel the mechanism underlying H pattern generation. Our data showed it was produced by specific nonlinear phenomena rather than intermodulation of frequencies or purely methodological artefact. The nonlinearity was stronger for dominant compared to non-dominant H pattern. According to the spectral parameters, we postulate that FA-H pattern may be supported by a predominant and synchronized firing of GABAergic neurons, while excitatory neuron firing is more important for PS-H pattern. As a distinctive and common ictal spectral feature, H pattern conveys unique information of ictal neural dynamics and provides new insights into the EZ. Our study also provides evidence that there is an elongated time-window to measure EZ using quantitative EEG.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis work was supported by the National Natural Science Foundation of China (grant numbers: 82171437, 82001365 and 82272112).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This study was approved by the Medical Ethics Committee of the Second Affiliated Hospital, Zhejiang University School of Medicine (Study No. 2020-910).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThe data that support the findings of this study are available upon reasonable request from the corresponding author. The data are not publicly available to protect the privacy of research participants.

High-frequency oscillations (HFOs) encompass ripples (80 Hz–200 Hz) and fast ripples (200 Hz–600 Hz), serving as a promising biomarker for localizing the epileptogenic zone in epilepsy. Spontaneous fast ripples are always pathological, while ripples may be physiological or pathological. Distinguishing physiological from pathological ripples is important not only for designating epileptogenic brain regions, but also for investigations that study ripples in the context of memory encoding, consolidation, and recall in patients with epilepsy. Many studies have sought to identify distinguishing features between pathological and physiological ripples over the past two decades. Physiological and pathological ripples differ with respect to their spatial location, cellular mechanisms, morphology, and coupling with background electroencephalographic activity. Retrospective studies have demonstrated that differentiating between pathological and physiological ripples can improve surgical outcome prediction. In this review, we summarize the characteristics, differences, and applications of pathological and physiological HFOs and discuss strategies for their clinical translation.

Objective: We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state.Methods: We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC).Results: Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1-45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55-80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs.Conclusions: Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs. Significance: Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction.& COPY; 2023 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

OBJECTIVE:Sleep strongly activates interictal epileptic activity through an unclear mechanism. We investigated how scalp sleep slow waves (SSWs), whose positive and negative half-waves reflect the fluctuation of neuronal excitability between the up and down states, respectively, modulate interictal epileptic events in focal epilepsy. METHODS:Simultaneous polysomnography was performed in 45 patients with drug-resistant focal epilepsy during intracranial electroencephalographic recording. Scalp SSWs and intracranial spikes and ripples (80-250 Hz) were detected; ripples were classified as type I (co-occurring with spikes) or type II (occurring alone). The Hilbert transform was used to analyze the distributions of spikes and ripples in the phases of SSWs. RESULTS:Thirty patients with discrete seizure-onset zone (SOZ) and discernable sleep architecture were included. Intracranial spikes and ripples accumulated around the negative peaks of SSWs and increased with SSW amplitude. Phase analysis revealed that spikes and both ripple subtypes in SOZ were similarly facilitated by SSWs exclusively during down state. In exclusively irritative zones outside SOZ (EIZ), SSWs facilitated spikes and type I ripples across a wider range of phases and to a greater extent than those in SOZ. The type II and type I ripples in EIZ were modulated by SSWs in different patterns. Ripples in normal zones decreased specifically during the up-to-down transition and then increased after the negative peak of SSW, with a characteristically high post-/pre-negative peak ratio. SIGNIFICANCE:SSWs modulate interictal events in an amplitude-dependent and region-specific pattern. Pathological ripples and spikes were facilitated predominantly during the cortical down state. Coupling analysis of SSWs could improve the discrimination of pathological and physiological ripples and facilitate seizure localization.

Surface electromyography (sEMG) has been proven competent and reliable to recognize speech musculature movement patterns. In other words, we can understand what a person prepares to say by collecting sEMG signals around the mouth. Therefore, sEMG-based Mime Speech Recognition (MSR) is a potential technique for human-machine interaction within noisy surroundings as well as the application of helping dysarthric patients. In this paper, we introduce multi-layer Bidirectional Long Short-Term Memory (BLSTM) networks with attention mechanism as a classifier for MSR, and verify it in the data set collected by ourselves. Six-channel sEMG signals are firstly acquired from elaborately selected facial muscles. Short-time Fourier Transform (STFT) and Convolutional Neural Networks (CNN) are utilized to extract time-frequency domain feature maps, replacing the handcrafted features in classic methods. The second phase of recognition process lies in the designed classifier. This classification system achieves over 97% accuracy in the four-class MSR task, significantly surpassing simple CNN and LSTM methods. Such result also indicates that excellent MSR results can be achieved without relying on handcrafted signal features.