Dysregulation of the Suprachiasmatic Nucleus Disturbs the Circadian Rhythm and Aggravates Epileptic Seizures by Inducing Hippocampal GABAergic Dysfunction in C57BL/6 Mice.

The interplay between circadian rhythms and epilepsy has gained increasing attention. The suprachiasmatic nucleus (SCN), which acts as the master circadian pacemaker, regulates physiological and behavioral rhythms through its complex neural networks. However, the exact role of the SCN and its Bmal1 gene in the development of epilepsy remains unclear. In this study, we utilized a lithium-pilocarpine model to induce epilepsy in mice and simulated circadian disturbances by creating lesions in the SCN and specifically knocking out the Bmal1 gene in the SCN neurons. We observed that the pilocarpine-induced epileptic mice experienced increased daytime seizure frequency, irregular oscillations in core body temperature, and circadian gene alterations in both the SCN and the hippocampus. Additionally, there was enhanced activation of GABAergic projections from the SCN to the hippocampus. Notably, SCN lesions intensified seizure activity, concomitant with hippocampal neuronal damage and GABAergic signaling impairment. Further analyses using the Gene Expression Omnibus database and gene set enrichment analysis indicated reduced Bmal1 expression in patients with medial temporal lobe epilepsy, potentially affecting GABA receptor pathways. Targeted deletion of Bmal1 in SCN neurons exacerbated seizures and pathology in epilepsy, as well as diminished hippocampal GABAergic efficacy. These results underscore the crucial role of the SCN in modulating circadian rhythms and GABAergic function in the hippocampus, aggravating the severity of seizures. This study provides significant insights into how circadian rhythm disturbances can influence neuronal dysfunction and epilepsy, highlighting the therapeutic potential of targeting SCN and the Bmal1 gene within it in epilepsy management.

Dissociation Between the Epileptogenic Lesion and Primary Seizure Onset Zone in the Tetanus Toxin Model of Temporal Lobe Epilepsy.

Despite extensive temporal lobe epilepsy (TLE) research, understanding the specific limbic structures' roles in seizures remains limited. This weakness can be attributed to the complex nature of TLE and the existence of various TLE subsyndromes, including non-lesional TLE. Conventional TLE models like kainate and pilocarpine hinder precise assessment of the role of individual limbic structures in TLE ictogenesis due to widespread limbic damage induced by the initial status epilepticus. In this study, we used a non-lesional TLE model characterized by the absence of initial status and cell damage to determine the spatiotemporal profile of seizure initiation and limbic structure recruitment in TLE. Epilepsy was induced by injecting a minute dose of tetanus toxin into the right dorsal hippocampus in seven animals. Following injection, animals were implanted with bipolar recording electrodes in the amygdala, dorsal hippocampus, ventral hippocampus, piriform, perirhinal, and entorhinal cortices of both hemispheres. The animals were video-EEG monitored for four weeks. In total, 140 seizures (20 seizures per animal) were analyzed. The average duration of each seizure was 53.2+/-3.9 s. Seizure could initiate in any limbic structure. Most seizures initiated in the ipsilateral (41 %) and contralateral (18 %) ventral hippocampi. These two structures displayed a significantly higher probability of seizure initiation than by chance. The involvement of limbic structures in seizure initiation varied between individual animals. Surprisingly, only 7 % of seizures initiated in the injected dorsal hippocampus. The limbic structure recruitment into the seizure activity wasn't random and displayed consistent patterns of early recruitment of hippocampi and entorhinal cortices. Although ventral hippocampus represented the primary seizure onset zone, the study demonstrated the involvement of multiple limbic structures in seizure initiation in a non-lesional TLE model. The study also revealed the dichotomy between the primary epileptogenic lesion and main seizure onset zones and points to the central role of ventral hippocampi in temporal lobe ictogenesis.

Factors Affecting the Activation and Appearances of Epileptiform Abnormalities in Routine Electroencephalography by Different Provocation Methods.

OBJECTIVE: The objective of this study was to observe the effects of various clinical factors on the activation and appearance of epileptiform abnormalities (EAs) in routine electroencephalography (rEEG) by different provocation methods. METHODS: This observational study involved a review of 136 patients presented for EEG recording due to various indications and their EEG showing EAs during various provocation methods. RESULTS: Generalized spike-wave discharges (GSWDs) were the most frequent activated epileptiform pattern observed in, 81 (59.1%) recordings. This pattern was seen mainly in females 49 (P = 0.00), in patients with generalized seizures 48 (P = 0.00), in prolonged EEG records 3 (P = 0.03), and in both genetic 35 (P = 0.00) and lesional epilepsies 21 (P = 0.00). Focal sharp waves with bilateral synchrony (FSWSBS) were the most activated ictal pattern (P = 0.00). Ictal EAs after hyperventilation (HV) (P = 0.03) and intermittent photic stimulation (IPS) (P = 0.01) were mainly observed in patients with uncontrolled seizures (P = 0.00), and immune-mediated epilepsy (P = 0.02). Females sex (odds ratio [OR]: 1.33, confidence interval [CI]: 0.6-2.6; P = 0.25), bilateral tonic-clonic seizures (OR: 1.17, CI: 0.5-2.4; P = 0.31) and lesional epilepsies (OR: 1.45, CI: 0.7-2.9; P = 0.20) had risk of activation of EAs by provocation methods; however this risk was not statistically significant. While sleep deprivation (SD) (OR: 6.33, CI: 2.2-18.2; P = 0.00), nonrapid eye movement sleep (NREM) (OR: 2.41, CI: 1.0-5.4; P = 0.00), and prolong EEG recording (OR: 1.91, CI: 0.9-3.9; P = 0.04) were leading to a statistically significant risk of activation and appearances of EAs due to provocation. CONCLUSION: Different provocation methods can activate and augment the variety of EEG patterns of diverse clinical significance. Detection of activated ictal EAs is dependent on various patient factors, including seizure control, and the provocation method applied. Further larger prospective cohort studies with adequate sample sizes are warranted.

Résumé Objectif:L'objectif de cette étude était d'observer les effets de différents facteurs cliniques sur l'activation et l'apparition d'épileptiformes. anomalies (EA) dans l'électroencéphalographie (EEG) de routine par différentes méthodes de provocation.Méthodes:Cette étude observationnelle impliquait une examen de 136 patients présentés pour un enregistrement EEG en raison de diverses indications et de leurs EEG montrant des EA au cours de diverses méthodes de provocation.Résultats:Les décharges épileptiformes généralisées étaient le schéma épileptiforme activé le plus fréquemment observé dans 81 enregistrements (59,1 %). Cele schéma a été observé principalement chez les femmes 49 (P = 0,00), chez les patients présentant des crises généralisées 48 (P = 0,00), dans les enregistrements EEG prolongés 3 (P = 0,03), et dans les épilepsies génétiques 35 (P = 0,00) et lésionnelles 21 (P = 0,00). Les ondes aiguës focales avec synchronie bilatérale étaient les ondes critiques les plus activées motif (P = 0,00). Les AE ictales après hyperventilation (P = 0,03) et stimulation photique intermittente (P = 0,01) ont été principalement observées chez les patients avec crises incontrôlées (P = 0,00) et épilepsie à médiation immunitaire (P = 0,02). Sexe féminin (oddsratio [OR] : 1,33, intervalle de confiance [IC] :0,6­2,6; P = 0,25), crises tonico-cloniques bilatérales (OR : 1,17, IC : 0,5­2,4; P = 0,31) et épilepsies lésionnelles (OR : 1,45, IC : 0,7­2,9; P = 0,20) avait un risque d'activation des EA par des méthodes de provocation; cependant, ce risque n'était pas statistiquement significatif. Alors que la privation de sommeil (OR : 6,33, IC : 2,2­18,2; P = 0,00), sommeil à mouvements oculaires non rapides (OR : 2,41, IC : 1,0­5,4; P = 0,00) et prolonger l'enregistrement EEG (OR : 1,91, IC : 0,9­3,9; P = 0,04) entraînaient un risque statistiquement significatif d'activation et d'apparition d'AE par provocation.Conclusion:Différent les méthodes de provocation peuvent activer et augmenter la variété des schémas EEG de signification clinique diverse. La détection des EA ictaux activés est dépend de divers facteurs liés au patient, y compris le contrôle des crises et la méthode de provocation appliquée. D'autres études de cohorte prospectives plus importantes avec des tailles d'échantillons adéquates sont justifiées.

EEG-fMRI in awake rat and whole-brain simulations show decreased brain responsiveness to sensory stimulations during absence seizures.

In patients suffering absence epilepsy, recurring seizures can significantly decrease their quality of life and lead to yet untreatable comorbidities. Absence seizures are characterized by spike-and-wave discharges on the electroencephalogram associated with a transient alteration of consciousness. However, it is still unknown how the brain responds to external stimuli during and outside of seizures. This study aimed to investigate responsiveness to visual and somatosensory stimulation in Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a well-established rat model for absence epilepsy. Animals were imaged under non-curarized awake state using a quiet, zero echo time, functional magnetic resonance imaging (fMRI) sequence. Sensory stimulations were applied during interictal and ictal periods. Whole-brain hemodynamic responses were compared between these two states. Additionally, a mean-field simulation model was used to explain the changes of neural responsiveness to visual stimulation between states. During a seizure, whole-brain responses to both sensory stimulations were suppressed and spatially hindered. In the cortex, hemodynamic responses were negatively polarized during seizures, despite the application of a stimulus. The mean-field simulation revealed restricted propagation of activity due to stimulation and agreed well with fMRI findings. Results suggest that sensory processing is hindered or even suppressed by the occurrence of an absence seizure, potentially contributing to decreased responsiveness during this absence epileptic process.

A pH-sensitive closed-loop nanomachine to control hyperexcitability at the single neuron level.

Epilepsy affects 1% of the general population and 30% of patients are resistant to antiepileptic drugs. Although optogenetics is an efficient antiepileptic strategy, the difficulty of illuminating deep brain areas poses translational challenges. Thus, the search of alternative light sources is strongly needed. Here, we develop pH-sensitive inhibitory luminopsin (pHIL), a closed-loop chemo-optogenetic nanomachine composed of a luciferase-based light generator, a fluorescent sensor of intracellular pH (E2GFP), and an optogenetic actuator (halorhodopsin) for silencing neuronal activity. Stimulated by coelenterazine, pHIL experiences bioluminescence resonance energy transfer between luciferase and E2GFP which, under conditions of acidic pH, activates halorhodopsin. In primary neurons, pHIL senses the intracellular pH drop associated with hyperactivity and optogenetically aborts paroxysmal activity elicited by the administration of convulsants. The expression of pHIL in hippocampal pyramidal neurons is effective in decreasing duration and increasing latency of pilocarpine-induced tonic-clonic seizures upon in vivo coelenterazine administration, without affecting higher brain functions. The same treatment is effective in markedly decreasing seizure manifestations in a murine model of genetic epilepsy. The results indicate that pHIL represents a potentially promising closed-loop chemo-optogenetic strategy to treat drug-refractory epilepsy.

Utility and rationale for continuous EEG monitoring: a primer for the general intensivist.

This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.

High precision in epileptic seizure self-reporting with an app diary.

People with epilepsy frequently under- or inaccurately report their seizures, which poses a challenge for evaluating their treatment. The introduction of epilepsy health apps provides a novel approach that could improve seizure documentation. This study assessed the documentation performance of an app-based seizure diary and a conventional paper seizure diary. At two tertiary epilepsy centers patients were asked to use one of two offered methods to report their seizures (paper or app diary) during their stay in the epilepsy monitoring unit. The performances of both methods were assessed based on the gold standard of video-EEG annotations. In total 89 adults (54 paper and 35 app users) with focal epilepsy were included in the analysis, of which 58 (33 paper and 25 app users) experienced at least one seizure and made at least one seizure diary entry. We observed a high precision of 85.7% for the app group, whereas the paper group's precision was lower due to overreporting (66.9%). Sensitivity was similar for both methods. Our findings imply that performance of seizure self-reporting is patient-dependent but is more precise for patients who are willing to use digital apps. This may be relevant for treatment decisions and future clinical trial design.

Frequency-dependent seizure-suppressing effects of optogenetic activation of septal inhibitory cells in mesial temporal lobe epilepsy.

Mesial temporal lobe epilepsy (MTLE) is characterized by recurring focal seizures that arise from limbic areas and are often refractory to pharmacological interventions. We have reported that optogenetic stimulation of PV-positive cells in the medial septum at 0.5 Hz exerts seizure-suppressive effects. Therefore, we compared here these results with those obtained by optogenetic stimulation of medial septum PV-positive neurons at 8 Hz in male PV-ChR2 mice (P60-P100) undergoing an initial, pilocarpine-induced status epilepticus (SE). Optogenetic stimulation (5 min ON, 10 min OFF) was performed from day 8 to day 12 after SE at a frequency of 8 Hz (n = 6 animals) or 0.5 Hz (n = 8 animals). Surprisingly, in both groups, no effects were observed on the occurrence of interictal spikes and interictal high frequency oscillations (HFOs). However, 0.5 Hz stimulation induced a significant decrease of seizure occurrence (p < 0.05). Such anti-ictogenic effect was not observed in the 8 Hz protocol that instead triggered seizures (p < 0.05); these seizures were significantly longer under optogenetic stimulation compared to when optogenetic stimulation was not implemented (p < 0.05). Analysis of ictal HFOs revealed that in the 0.5 Hz group, but not in the 8 Hz group, seizures occurring under optogenetic stimulation were associated with significantly lower rates of fast ripples compared to when optogenetic stimulation was not performed (p < 0.05). Our results indicate that activation of GABAergic PV-positive neurons in the medial septum exerts seizure-suppressing effects that are frequency-dependent and associated with low rates of fast ripples. Optogenetic activation of medial septum PV-positive neurons at 0.5 Hz is efficient in blocking seizures in the pilocarpine model of MTLE, an effect that did not occur with 8 Hz stimulation.

Multiscale distribution entropy analysis of short epileptic EEG signals.

This paper proposes an information-theoretic measure for discriminating epileptic patterns in short-term electroencephalogram (EEG) recordings. Considering nonlinearity and nonstationarity in EEG signals, quantifying complexity has been preferred. To decipher abnormal epileptic EEGs, i.e., ictal and interictal EEGs, via short-term EEG recordings, a distribution entropy (DE) is used, motivated by its robustness on the signal length. In addition, to reflect the dynamic complexity inherent in EEGs, a multiscale entropy analysis is incorporated. Here, two multiscale distribution entropy (MDE) methods using the coarse-graining and moving-average procedures are presented. Using two popular epileptic EEG datasets, i.e., the Bonn and the Bern-Barcelona datasets, the performance of the proposed MDEs is verified. Experimental results show that the proposed MDEs are robust to the length of EEGs, thus reflecting complexity over multiple time scales. In addition, the proposed MDEs are consistent irrespective of the selection of short-term EEGs from the entire EEG recording. By evaluating the Man-Whitney U test and classification performance, the proposed MDEs can better discriminate epileptic EEGs than the existing methods. Moreover, the proposed MDE with the moving-average procedure performs marginally better than one with the coarse-graining. The experimental results suggest that the proposed MDEs are applicable to practical seizure detection applications.

Neurological Impact of Slower Rewarming during Bypass Surgery in Infants.

BACKGROUND: Hypothermia is a neuroprotective strategy during cardiopulmonary bypass. Rewarming entailing a rapid rise in cerebral metabolism might lead to secondary neurological sequelae. In this pilot study, we aimed to validate the hypothesis that a slower rewarming rate would lower the risk of cerebral hypoxia and seizures in infants. METHODS: This is a prospective, clinical, single-center study. Infants undergoing cardiac surgery in hypothermia were rewarmed either according to the standard (+1°C in < 5 minutes) or a slow (+1°C in > 5-8 minutes) rewarming strategy. We monitored electrocortical activity via amplitude-integrated electroencephalography (aEEG) and cerebral oxygenation by near-infrared spectroscopy during and after surgery. RESULTS: Fifteen children in the standard rewarming group (age: 13 days [5-251]) were cooled down to 26.6°C (17.2-29.8) and compared with 17 children in the slow-rewarming group (age: 9 days [4-365]) with a minimal temperature of 25.7°C (20.1-31.4). All neonates in both groups (n = 19) exhibited suppressed patterns compared with 28% of the infants > 28 days (p < 0.05). During rewarming, only 26% of the children in the slow-rewarming group revealed suppressed aEEG traces (vs. 41%; p = 0.28). Cerebral oxygenation increased by a median of 3.5% in the slow-rewarming group versus 1.5% in the standard group (p = 0.9). Our slow-rewarming group revealed no aEEG evidence of any postoperative seizures (0 vs. 20%). CONCLUSION: These results might indicate that a slower rewarming rate after hypothermia causes less suppression of electrocortical activity and higher cerebral oxygenation during rewarming, which may imply a reduced risk of postoperative seizures.

Investigating psychobiological causes and mechanisms in functional seizures and functional motor symptoms: Study protocol.

INTRODUCTION: Advances have been made in understanding the aetiology of functional neurological disorder (FND); however, its pathophysiological mechanisms have not been definitively demonstrated. Evidence suggests interacting roles for altered emotional processing and interoception, elevated autonomic arousal, and dissociation, but there is limited evidence demonstrating their causal influence on specific FND symptoms. Our superordinate aim is to elucidate potentially shared and distinct aetiological factors and mechanisms in two common FND subtypes, functional seizures (FS) and functional motor symptoms (FMS). METHODS: This study has a multimodal, mixed between- and within-groups design. The target sample is 50 individuals with FS, 50 with FMS, 50 clinical controls (anxiety/depression), and 50 healthy controls. Potential aetiological factors (e.g., adverse life events, physical/mental health symptoms, dissociative tendencies, interoceptive insight/sensibility) will be assessed with a detailed medical history interview and self-report questionnaires. A laboratory session will include a neurocognitive battery, psychophysiological testing, cardiac interoception and time estimation tasks and an isometric handgrip task. A subsample will undergo magnetic resonance imaging, including structural, resting-state and task-based scans combined with psychophysiological recording. Remote monitoring with ecological momentary assessment and wearables will measure variability in FND symptoms and their potential predictors/correlates for ≥2 weeks in patients' daily lives. Longitudinal follow-ups at 3, 6, and 12-months will monitor longer-term outcomes in the clinical groups. DISCUSSION: This study employs multimodal research methods to rigorously examine several putative mechanisms in FND, at subjective/experiential, behavioural, and physiological levels. The study will test causal hypotheses about the role of altered emotional processing, autonomic arousal, dissociation and interoception in the initiation or exacerbation of FND symptoms, directly comparing these processes in FS and FMS to healthy and clinical controls. This is the first study of its kind, with potential to reveal important targets for prevention and treatment of FND in future.

A spatial perturbation framework to validate implantation of the epileptogenic zone.

Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.

Addressing data limitations in seizure prediction through transfer learning.

According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research.

Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks.

Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features in the input of the network, the differential features of the signals, the amplitude spectrum and the phase spectrum in the frequency domain are extracted to form a two-dimensional feature vector. In order to solve the problem of recognizing multimodal features, a neural network model based on a multimodal dual-stream network is proposed, which uses a mixture of one-dimensional convolution, two-dimensional convolution and LSTM neural networks to extract the spatial features of the EEG two-dimensional vectors and the temporal features of the signals, respectively, and combines the advantages of the two networks, using the hybrid neural network to extract both the temporal and spatial features of the signals at the same time. In addition, a channel attention module was used to focus the model on features related to seizures. Finally, multiple sets of experiments were conducted on the Bonn and New Delhi data sets, and the highest accuracy rates of 99.69% and 97.5% were obtained on the test set, respectively, verifying the superiority of the proposed model in the task of epileptic seizure detection.

Seizure Assessment and Forecasting With Efficient Rapid-EEG: A Retrospective Multicenter Comparative Effectiveness Study.

BACKGROUND AND OBJECTIVES: Approximately 30% of critically ill patients have seizures, and more than half of these seizures do not have an overt clinical correlate. EEG is needed to avoid missing seizures and prevent overtreatment with antiseizure medications. Conventional-EEG (cEEG) resources are logistically constrained and unable to meet their growing demand for seizure detection even in highly developed centers. Brief EEG screening with the validated 2HELPS2B algorithm was proposed as a method to triage cEEG resources, but it is hampered by cEEG requirements, primarily EEG technologists. Seizure risk-stratification using reduced time-to-application rapid response-EEG (rrEEG) systems (∼5 minutes) could be a solution. We assessed the noninferiority of the 2HELPS2B score on a 1-hour rrEEG compared to cEEG. METHODS: A multicenter retrospective EEG diagnostic accuracy study was conducted from October 1, 2021, to July 31, 2022. Chart and EEG review performed with consecutive sampling at 4 tertiary care centers, included records of patients ≥18 years old, from January 1, 2018, to June 20, 2022. Monte Carlo simulation power analysis yielded n = 500 rrEEG; for secondary outcomes n = 500 cEEG and propensity-score covariate matching was planned. Primary outcome, noninferiority of rrEEG for seizure risk prediction, was assessed per area under the receiver operator characteristic curve (AUC). Noninferiority margin (0.05) was based on the 2HELPS2B validation study. RESULTS: A total of 240 rrEEG with follow-on cEEG were obtained. Median age was 64 (interquartile range 22); 42% were female. 2HELPS2B on a 1-hour rrEEG met noninferiority to cEEG (AUC 0.85, 95% CI 0.78-0.90, p = 0.001). Secondary endpoints of comparison with a matched contemporaneous cEEG showed no significant difference in AUC (0.89, 95% CI 0.83-0.94, p = 0.31); in false negative rate for the 2HELPS2B = 0 group (p = 1.0) rrEEG (0.021, 95% CI 0-0.062), cEEG (0.016, 95% CI 0-0.048); nor in survival analyses. DISCUSSION: 2HELPS2B on 1-hour rrEEG is noninferior to cEEG for seizure prediction. Patients with low-risk (2HELPS2B = 0) may be able to forgo prolonged cEEG, allowing for increased monitoring of at-risk patients. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that rrEEG is noninferior to cEEG in calculating the 2HELPS2B score to predict seizure risk.

AI in ECG: Validating an ambulatory semiology labeller and predictor.

OBJECTIVES: Early prediction of epileptic seizures can help reduce morbidity and mortality. In this work, we explore using electrocardiographic (ECG) signal as input to a seizure prediction system and note that the performance can be improved by using selected signal processing techniques. METHODS: We used frequency domain analysis with a deep neural network backend for all our experiments in this work. We further analysed the effect of the proposed system for different seizure semiologies and prediction horizons. We explored refining the signal using signal processing to enhance the system's performance. RESULTS: Our final system using the Temple University Hospital's Seizure (TUHSZ) corpus gave an overall prediction accuracy of 84.02 %, sensitivity of 87.59 %, specificity of 81.9 %, and an area under the receiver operating characteristic curve (AUROC) of 0.9112. Notably, these results surpassed the state-of-the-art outcomes reported using the TUHSZ database; all findings are statistically significant. We also validated our study using the Siena scalp EEG database. Using the frequency domain data, our baseline system gave a performance of 75.17 %, 79.17 %, 70.04 % and 0.82 for prediction accuracy, sensitivity, specificity and AUROC, respectively. After selecting the optimal frequency band of 0.8-15 Hz, we obtained a performance of 80.49 %, 89.51 %, 75.23 % and 0.89 for prediction accuracy, sensitivity, specificity and AUROC, respectively which is an improvement of 5.32 %, 10.34 %, 5.19 % and 0.08 for prediction accuracy, sensitivity, specificity and AUROC, respectively. CONCLUSIONS: The seizure information in ECG is concentrated in a narrow frequency band. Identifying and selecting that band can help improve the performance of seizure detection and prediction. SIGNIFICANCE: EEG is susceptible to artefacts and is not preferred in a low-cost ambulatory device. ECG can be used in wearable devices (like chest bands) and is feasible for developing a low-cost ambulatory device for seizure prediction. Early seizure prediction can provide patients and clinicians with the required alert to take necessary precautions and prevent a fatality, significantly improving the patient's quality of life.

Rat strain differences in seizure frequency and hilar neuron loss after systemic treatment with pilocarpine.

At least 3 months after systemic treatment with pilocarpine to induce status epilepticus, Long-Evans and Sprague-Dawley rats were video-EEG monitored for seizures continuously for 1 month. Rats were then perfused, hippocampi were processed for Nissl staining, and hilar neurons were quantified. Seizure frequency in Long-Evans rats was 1/10th of that in Sprague-Dawley rats, and more variable. Hilar neuron loss was also less severe in Long-Evans rats. However, there was no correlation between hilar neuron loss and seizure frequency in either strain. The low and variable seizure frequency suggests limited usefulness of pilocarpine-treated Long-Evans rats for some epilepsy experiments.

A generalized seizure type: Myoclonic-to-tonic seizure.

BACKGROUND AND PURPOSE: To test the hypothesis that myoclonic seizures can evolve to tonic seizures, we documented the electroclinical features of this under-recognized seizure type. METHODS: We observed a distinct seizure pattern starting with myoclonus without returning to an interictal state, which subsequently evolved into generalized tonic seizures. The detailed symptomatic and electroencephalographic characteristics of this seizure were extracted, and the clinical manifestations, drug curative responses in patients with this seizure were reviewed and analyzed. RESULTS: The onset of all seizures was characterized by a preceding period of myoclonus and bursts of generalized spike or poly-spike slow wave discharges with high amplitude. This was closely followed by the occurrence of tonic seizures, which were distinguished by bursts of generalized fast activity at 10 Hz or higher frequency. This under-recognized seizure type has been designated as myoclonic-to-tonic (MT) seizure. The number of patients identified with MT seizures in this study was 34. The prevalence rate of MT seizures was found to be higher in males. While MT seizures typically included a tonic component, it should be noted that some patients experiencing this seizure type never presented with isolated tonic seizures. Generalized Epilepsy not further defined (GE) accounted for approximately one-third of the diagnosed cases, followed by Lennox-Gastaut syndrome and Epilepsy with Myoclonic-Atonic seizures. In comparison to other types of epilepsy, GE with MT seizures demonstrated a more favorable prognosis. CONCLUSIONS: The classification of myoclonic-to-tonic seizure represents a novel approach in comprehending the ictogenesis of generalized seizures and can provide valuable assistance to clinicians in epilepsy diagnosis.