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Objective: Ultra long-term monitoring with subcutaneous EEG (sqEEG) offers objective outpatient recording of electrographic seizures as an alternative to self-reported epileptic seizure diaries. This methodology requires an algorithm-based automatic seizure detection to indicate periods of potential seizure activity to reduce the time spent on visual review. The objective of this study was to evaluate the performance of a sqEEG-based automatic seizure detection algorithm. Methods: A multicenter cohort of subjects using sqEEG were analyzed, including nine people with epilepsy (PWE) and 12 healthy subjects, recording a total of 965 days. The automatic seizure detections of a deep-neural-network algorithm were compared to annotations from three human experts. Results: Data reduction ratios were 99.6% in PWE and 99.9% in the control group. The cross-PWE sensitivity was 86% (median 80%, range 69–100% when PWE were evaluated individually), and the corresponding median false detection rate was 2.4 detections per 24 hours (range: 2.0–13.0). Conclusions: Our findings demonstrated that step one in a sqEEG-based semi-automatic seizure detection/review process can be performed with high sensitivity and clinically applicable specificity. Significance: Ultra long-term sqEEG bears the potential of improving objective seizure quantification.

Data de publicació electrònica: 18 de febrer de 2023 A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures—ICTALS 2022—convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being. Epilepsy Foundation, Epilepsy Innovation Institute My Seizure Gauge; Medical Research Council Centre for Neurodevelopmental Disorders, Grant/ Award Number: Ref MR/N026063/1; National Health and Medical Research Council, Grant/Award Number: GNT1160815 and GNT1183119; National Institutes of Health, Grant/ Award Number: NIH NS094399 and NIH NS109062; Spanish Ministry of Science and Innovation and the State Research Agency, Grant/Award Number: PID2020-118196GBI00/ MICIN/AEI/10.13039/50110001103; Swebilius Foundation.; University of Bern, the Inselspital, University Hospital Bern, the Alliance for Epilepsy Research, the Swiss National Science Foundation, UCB, FHC, the Wyss Center for bio- and neuro-engineering, the American Epilepsy Society (AES), the CURE epilepsy Foundation, Ripple neuro, Sintetica, DIXI medical, UNEEG medical and NeuroPace.

People with focal epilepsies are known to under-document their seizures, but there is no data on self-documentation in adults with genetic (idiopathic) generalized epilepsy (GGE/IGE). We assessed the accuracy of self-evaluation of typical absences (TA) or myoclonic seizures (MS) in adults with IGE based on home video-EEG telemetry (HVET). Patients' own estimates were compared to the objective count of definite TA and MS, performed visually. We considered definite TA as generalized spike-wave discharges (GSWD) that met any of the following criteria: 1) coinciding with clear behavioural arrest on video, 2) followed after a few seconds by positive indication that an absence occurred, or 3) in the absence of video, consistently coinciding with clear motor arrest, as evidenced by interruption of continuous muscle activity. For each patient, we also classified probable TA as GSWDs that were longer than those corresponding to the shortest definite TA on HVET or based on the most recent sleep-deprived EEG (SDEEG). From the first 300 consecutive adults who had HVET, 24 had IGE with TA and / or MS; 23 were women. Only one patient had newly diagnosed IGE. Erroneous self-assessment of TA and MS was noted in 16/24 patients (66.7%). Seizures were overestimated in nine (37.5%) and underestimated in seven (29.2%). Only one patient (4.2%) documented all her TA and MS without false-positive estimates. Overestimation (but not underestimation) of TA on HVET could be predicted when patients reported daily or multiple weekly TA and a recent SDEEG was either normal or contained only subclinical GSWD (p=0.0095). Under- and over-self-documentation of TA and MS occurred in two thirds of adults with GGE/IGE, with substantial impact on their outpatient management and treatment. Diagnostic HVET is a useful tool for the detection of erroneous self-evaluation in these patients.

The Wearables for Epilepsy And Research (WEAR) International Study Group identified a set of methodology standards to guide research on wearable devices for seizure detection. We formed an international consortium of experts from clinical research, engineering, computer science, and data analytics at the beginning of 2020. The study protocols and practical experience acquired during the development of wearable research studies were discussed and analyzed during bi-weekly virtual meetings to highlight commonalities, strengths, and weaknesses, and to formulate recommendations. Seven major essential components of the experimental design were identified, and recommendations were formulated about: (1) description of study aims, (2) policies and agreements, (3) study population, (4) data collection and technical infrastructure, (5) devices, (6) reporting results, and (7) data sharing. Introducing a framework of methodology standards promotes optimal, accurate, and consistent data collection. It also guarantees that studies are generalizable and comparable, and that results can be replicated, validated, and shared.

This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo-prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave-one-patient-out cross-validation method, and the area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave-one-signal-out method with repeated training and testing for each classifier. Cross-patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution (p .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross-patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two-channel ultra-long-term sqEEG.

Objective Recent neuroimaging studies have revealed differences in cortical and white matter brain structure in children with self‐limiting rolandic epilepsy (RE). Despite this, reproducibility of the findings has been difficult, and there is no consensus about where and when structural differences are most apparent. We performed a systematic review of quantitative neuroimaging studies in children with RE to explore these questions. Methods Using PRISMA guidelines, we used a multilayered search strategy to identify neuroimaging studies in RE. Publications were included if they were quantitative and derived from controlled group studies and passed a quality assessment. Findings of the studies were presented and stratified by duration of epilepsy and age of participants. Results We identified six gray matter studies and five white matter studies. Consistent findings were found inside and outside the central sulcus, predominantly within the bilateral frontal and parietal lobes, striatal structures, such as the putamen and white matter, mainly involving the left superior longitudinal fasciculus and connections between the left pre‐ and postcentral gyrus. Stratification of the T1 studies by age found that cortical thickness differences varied between the under and over 10 year olds. Furthermore, the longer the duration of epilepsy, the less likely differences were detected. In white matter studies, there was a reduction in differences with increased age and duration of epilepsy. Significance These findings would suggest that the development of regions of the cortex in children with RE is abnormal. These regions are more widespread than the suspected seizure onset zone. Moreover, the findings would suggest that these differences are evidence of neurodevelopmental delay rather than apparent “damage” from the epilepsy.

OBJECTIVE: Video-electroencephalography (vEEG) is an important component of epilepsy diagnosis and management. Nevertheless, inpatient vEEG monitoring fails to capture seizures in up to one third of patients. We hypothesized that personalized seizure forecasts could be used to optimize the timing of vEEG. METHODS: We used a database of ambulatory vEEG studies to select a cohort with linked electronic seizure diaries of more than 20 reported seizures over at least 8 weeks. The total cohort included 48 participants. Diary seizure times were used to detect individuals' multiday seizure cycles and estimate times of high seizure risk. We compared whether estimated seizure risk was significantly different between conclusive and inconclusive vEEGs, and between vEEG with and without recorded epileptic activity. vEEGs were conducted prior to self-reported seizures; hence, the study aimed to provide a retrospective proof of concept that cycles of seizure risk were correlated with vEEG outcomes. RESULTS: Estimated seizure risk was significantly higher for conclusive vEEGs and vEEGs with epileptic activity. Across all cycle strengths, the average time in high risk during vEEG was 29.1% compared with 14% for the conclusive/inconclusive groups and 32% compared to 18% for the epileptic activity/no epileptic activity groups. On average, 62.5% of the cohort showed increased time in high risk during their previous vEEG when epileptic activity was recorded (compared to 28% of the cohort where epileptic activity was not recorded). For conclusive vEEGs, 50% of the cohort had increased time in high risk, compared to 21.5% for inconclusive vEEGs. SIGNIFICANCE: Although retrospective, this study provides a proof of principle that scheduling monitoring times based on personalized seizure risk forecasts can improve the yield of vEEG. Forecasts can be developed at low cost from mobile seizure diaries. A simple scheduling tool to improve diagnostic outcomes may reduce cost and risks associated with delayed or missed diagnosis in epilepsy.

Objective: The study aim was to compare interictal encephalographic (EEG) functional network topology between people with well-controlled idiopathic generalized epilepsy (WC-IGE) and drug-resistant IGE (DR-IGE). Methods: Nineteen participants with WC-IGE, 18 with DR-IGE, and 20 controls underwent a resting state, 64-channel EEG. An artifact-free epoch was bandpass filtered into the frequency range of high and low extended alpha. Weighted functional connectivity matrices were calculated. Mean degree, degree distribution variance, characteristic path length (L), clustering coefficient, small world index (SWI), and betweenness centrality were measured. A Kruskal–Wallis H-test assessed effects across groups. Where significant differences were found, Bonferroni-corrected Mann–Whitney pairwise comparisons were calculated. Results: In the low alpha band (6–9 Hz), there was a significant difference in L at the three-group level (p

© 2022 International League Against Epilepsy, Objective: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. Methods: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. Significance: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG., This work was supported by the Epilepsy Foundation’s Epilepsy Innovation Institute My Seizure Gauge Project. M.P.R. is supported by the NIHR Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust, the MRC Centre for Neurodevelopmental Disorders (MR/N026063/1), and the RADAR-CNS project funded by the European Commission (www.radar-cns.org, grant agreement 115902). B.H.B. is supported by the Mayo Neurology AI Program and by the NIH (NS UG3 123066).

There is increasing evidence that patients with Coronavirus disease 19 (COVID-19) present with neurological and psychiatric symptoms. Anosmia, hypogeusia, headache, nausea and altered consciousness are commonly described, although there are emerging clinical reports of more serious and specific conditions such as acute cerebrovascular accident, encephalitis and demyelinating disease. Whether these presentations are directly due to viral invasion of the central nervous system (CNS) or caused by indirect mechanisms has yet to be established. Neuropathological examination of brain tissue at autopsy will be essential to establish the neuro-invasive potential of the SARS-CoV-2 virus but, to date, there have been few detailed studies. The pathological changes in the brain probably represent a combination of direct cytopathic effects mediated by SARS-CoV-2 replication or indirect effects due to respiratory failure, injurious cytokine reaction, reduced immune response and cerebrovascular accidents induced by viral infection. Further large-scale molecular and cellular investigations are warranted to clarify the neuropathological correlates of the neurological and psychiatric features seen clinically in COVID-19. In this review, we summarize the current reports of neuropathological examination in COVID-19 patients, in addition to our own experience, and discuss their contribution to the understanding of CNS involvement in this disease.

Despite representing the leading cause of epilepsy-related mortality, the pathophysiology of sudden unexpected death in epilepsy (SUDEP) remains elusive. In this context, the identification of clinical markers of SUDEP assumes a great importance and has been the target of many studies aimed at stratifying patients' individual risk. Among the potentially most hazardous post-ictal phenomena observed following convulsive seizures in monitored SUDEP cases, postictal generalized EEG suppression and postictal immobility have attracted attention as potential SUDEP risk factors. In this manuscript, we review the current knowledge on postictal generalized EEG suppression and postictal immobility, aiming to identify their pathophysiological mechanisms, reported frequencies and associated clinical factors, and critically evaluate the evidence on their potential relevance as SUDEP risk markers.

OBJECTIVE Movement-based wearable sensors are used for detection of convulsive seizures. The identification of the absence of motion following a seizure, known as post-ictal immobility (PI), may represent a potential additional application of wearables. PI has been associated with potentially life-threatening complications and with sudden unexpected death in epilepsy (SUDEP). We aimed to assess whether wearable accelerometers (ACCs) could be used as a digital marker of PI. METHOD Devices with embedded ACCs were worn by patients admitted to an epilepsy monitoring unit. Participants presenting with convulsive seizures were included in the study. PI presence and duration were assessed by experts reviewing video recordings. An algorithm for the automatic detection of post-ictal ACC silence and its duration was developed and the linear pairwise relationship between the automatically detected duration of post-ictal ACC silence and the duration of the expert-labeled PI was analyzed. RESULTS Twenty-two convulsive seizures were recorded from 18 study participants. Twenty were followed by PI and two by agitation. The automated estimation of post-ictal ACC silence identified all the 20 expert-labeled PI. The regression showed that the duration of the post-ictal ACC silence was correlated with the duration of PI (Pearson r = .92; P

Objective Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Methods Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms. Results The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low-risk state, with 14.8% of their time in a high-risk warning state. On average, 69.1% of seizures occurred during high-risk states and 10.5% of seizures occurred in low-risk states. Significance Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.

The ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously demonstrated using noninvasive wearable devices over long durations in an ambulatory setting. In this study we developed a seizure forecasting system with a long short-term memory (LSTM) recurrent neural network (RNN) algorithm, using a noninvasive wrist-worn research-grade physiological sensor device, and tested the system in patients with epilepsy in the field, with concurrent invasive EEG confirmation of seizures via an implanted recording device. The system achieved forecasting performance significantly better than a random predictor for 5 of 6 patients studied, with mean AUC-ROC of 0.80 (range 0.72–0.92). These results provide the first clear evidence that direct seizure forecasts are possible using wearable devices in the ambulatory setting for many patients with epilepsy.

BACKGROUND: Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link has previously been considered in epilepsy research, with potential implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy). METHODS: We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase. FINDINGS: Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles. INTERPRETATION: Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans. FUNDING: This research received funding from the Australian Government National Health and Medical Research Council (investigator grant 1178220), the Australian Government BioMedTech Horizons program, and the Epilepsy Foundation of America's 'My Seizure Gauge' grant. ispartof: EBIOMEDICINE vol:72 ispartof: location:Netherlands status: published

ObjectivesPeople with epilepsy (PWE) have a higher mortality rate than the general population. Epilepsy-related deaths have increased despite all-cause mortality decreasing in the general population pre-COVID-19. We hypothesised that clinical and lifestyle factors may identify people more at risk.DesignWe used a retrospective cohort study to explore cause of death and a nested case–control study to identify risk factors.SettingWe explored factors associated with mortality using primary care population data from 1 April 2004 to 31 March 2014. Data were obtained from the Clinical Practice Research Datalink which compiles anonymised patient data from primary care in the UK. Cause of death data was supplemented from the Office of National Statistics when available.ParticipantsThe analysis included 70 431 PWE, with 11 241 registered deaths.ResultsThe number of deaths within the database increased by 69% between the first and last year of the study. Epilepsy was considered as a contributing cause in approximately 45% of deaths of PWE under 35. Factors associated with increased risk of death included attendance at emergency departments and/or emergency admissions (OR 3.48, 95% CI 3.19 to 3.80), antiepileptic drug (AED) polytherapy (2 AEDs: OR 1.60, 95% CI 1.51 to 1.71; 3 AEDs: OR 2.06, 95% CI 1.86 to 2.29; 4+AEDs: OR 2.62, 95% CI 2.23 to 3.08), status epilepticus (OR 2.78, 95% CI 1.64 to 4.71), depression (OR 1.67, 95% CI 1.57 to 1.76) and injuries (OR 1.54, 95% CI 1.43 to 1.67). No seizures in the prior year (OR 0.52, 95% CI 0.41 to 0.65).ConclusionOur results add to existing evidence that deaths in epilepsy are increasing. Future studies could focus on identifying PWE at high risk and addressing them with clinical interventions or better self-management. Identifying specific risk factors for younger people should be a priority as epilepsy may be a factor in close to half of deaths of PWE under 35 years of age.

BACKGROUND AND PURPOSE With the increasing adoption of electronic records in the health system, machine learning-enabled techniques offer the opportunity for greater computer-assisted curation of these data for audit and research purposes. In this project, we evaluate the consistency of traditional curation methods used in routine clinical practice against a new machine learning-enabled tool, MedCAT, for the extraction of the stroke comorbidities recorded within the UK's Sentinel Stroke National Audit Programme (SSNAP) initiative. METHODS A total of 2327 stroke admission episodes from three different National Health Service (NHS) hospitals, between January 2019 and April 2020, were included in this evaluation. In addition, current clinical curation methods (SSNAP) and the machine learning-enabled method (MedCAT) were compared against a subsample of 200 admission episodes manually reviewed by our study team. Performance metrics of sensitivity, specificity, precision, negative predictive value, and F1 scores are reported. RESULTS The reporting of stroke comorbidities with current clinical curation methods is good for atrial fibrillation, hypertension, and diabetes mellitus, but poor for congestive cardiac failure. The machine learning-enabled method, MedCAT, achieved better performances across all four assessed comorbidities compared with current clinical methods, predominantly driven by higher sensitivity and F1 scores. CONCLUSIONS We have shown machine learning-enabled data collection can support existing clinical and service initiatives, with the potential to improve the quality and speed of data extraction from existing clinical repositories. The scalability and flexibility of these new machine-learning tools, therefore, present an opportunity to revolutionize audit and research methods.

Objective: Whilst stimulation of the anterior nucleus of the thalamus has shown efficacy for reducing seizure frequency in adults, alterations in thalamic connectivity have not been explored in children. We tested the hypotheses that (a) the anterior thalamus has increased functional connectivity in children with focal epilepsy, and (b) this alteration in the connectome is a persistent effect of the disease rather than due to transient epileptiform activity.Methods: Data from 35 children (7–18 years) with focal, drug-resistant epilepsy and 20 healthy children (7–17 years) were analyzed. All subjects underwent functional magnetic resonance imaging (fMRI) whilst resting and were simultaneously monitored with scalp electroencephalography (EEG). The fMRI timeseries were extracted for each Automated Anatomical Labeling brain region and thalamic subregion. Graph theory metrics [degree (DC) and eigenvector (EC) centrality] were used to summarize the connectivity profile of the ipsilateral thalamus, and its thalamic parcellations. The effect of interictal epileptiform discharges (IEDs) captured on EEG was used to determine their effect on DC and EC.Results: DC was significantly higher in the anterior nucleus (p = 0.04) of the thalamus ipsilateral to the epileptogenic zone in children with epilepsy compared to controls. On exploratory analyses, we similarly found a higher DC in the lateral dorsal nucleus (p = 0.02), but not any other thalamic subregion. No differences in EC measures were found between patients and controls. We did not find any significant difference in DC or EC in any thalamic subregion when comparing the results of children with epilepsy before, and after the removal of the effects of IEDs.Conclusions: Our data suggest that the anterior and lateral dorsal nuclei of the thalamus are more highly functionally connected in children with poorly controlled focal epilepsy. We did not detect a convincing change in thalamic connectivity caused by transient epileptiform activity, suggesting that it represents a persistent alteration to network dynamics.

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods.This feasibility study tracked participants’ (n = 11) heart rates, sleep, and step counts using wearable smartwatches and seizure occurrence using mobile seizure diaries for at least 6 months (mean = 14.6 months, SD = 3.8 months). Eligible participants had a diagnosis of refractory epilepsy and reported at least 20 seizures (mean = 135, SD = 123) during the recording period. An ensembled machine learning and neural network model estimated seizure risk either daily or hourly, with retraining occurring on a weekly basis as additional data was collected. Performance was evaluated retrospectively against a rate-matched random forecast using the area under the receiver operating curve. A pseudo-prospective evaluation was also conducted on a held-out dataset.Of the 11 participants, seizures were predicted above chance in all (100%) participants using an hourly forecast and in ten (91%) participants using a daily forecast. The average time spent in high risk (prediction time) before a seizure occurred was 37 minutes in the hourly forecast and 3 days in the daily forecast. Cyclic features added the most predictive value to the forecasts, particularly circadian and multiday heart rate cycles.Wearable devices can be used to produce patient-specific seizure forecasts, particularly when biomarkers of seizure and epileptic activity cycles are utilized.

OBJECTIVE Ultra long-term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long-term quality and consistency of the sqEEG signal, which is the objective of this study. METHODS The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long-term temporal trends in signal impedance and power spectral features were investigated with subject-specific linear regression models and group-level linear mixed-effects models. RESULTS sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8-13Hz) and nocturnal peaks in the sigma range (12-16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods. SIGNIFICANCE The spectral characteristics of minimally invasive, ultra long-term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.

Background Dissociative (non-epileptic) seizures are potentially treatable by psychotherapeutic interventions; however, the evidence for this is limited. Objectives To evaluate the clinical effectiveness and cost-effectiveness of dissociative seizure-specific cognitive–behavioural therapy for adults with dissociative seizures. Design This was a pragmatic, multicentre, parallel-arm, mixed-methods randomised controlled trial. Setting This took place in 27 UK-based neurology/epilepsy services, 17 liaison psychiatry/neuropsychiatry services and 18 cognitive–behavioural therapy services. Participants Adults with dissociative seizures in the previous 8 weeks and no epileptic seizures in the previous year and meeting other eligibility criteria were recruited to a screening phase from neurology/epilepsy services between October 2014 and February 2017. After psychiatric assessment around 3 months later, eligible and interested participants were randomised between January 2015 and May 2017. Interventions Standardised medical care consisted of input from neurologists and psychiatrists who were given guidance regarding diagnosis delivery and management; they provided patients with information booklets. The intervention consisted of 12 dissociative seizure-specific cognitive–behavioural therapy 1-hour sessions (plus one booster session) that were delivered by trained therapists, in addition to standardised medical care. Main outcome measures The primary outcome was monthly seizure frequency at 12 months post randomisation. The secondary outcomes were aspects of seizure occurrence, quality of life, mood, anxiety, distress, symptoms, psychosocial functioning, clinical global change, satisfaction with treatment, quality-adjusted life-years, costs and cost-effectiveness. Results In total, 698 patients were screened and 368 were randomised (standardised medical care alone, n = 182; and cognitive–behavioural therapy plus standardised medical care, n = 186). Primary outcome data were obtained for 85% of participants. An intention-to-treat analysis with multivariate imputation by chained equations revealed no significant between-group difference in dissociative seizure frequency at 12 months [standardised medical care: median of seven dissociative seizures (interquartile range 1–35 dissociative seizures); cognitive–behavioural therapy and standardised medical care: median of four dissociative seizures (interquartile range 0–20 dissociative seizures); incidence rate ratio 0.78, 95% confidence interval 0.56 to 1.09; p = 0.144]. Of the 16 secondary outcomes analysed, nine were significantly better in the arm receiving cognitive–behavioural therapy at a p-value p-value ≤ 0.001: the longest dissociative seizure-free period in months 7–12 inclusive post randomisation (incidence rate ratio 1.64, 95% confidence interval 1.22 to 2.20; p = 0.001); better psychosocial functioning (Work and Social Adjustment Scale, standardised treatment effect –0.39, 95% confidence interval –0.61 to –0.18; p p = 0.001; clinician rated: standardised treatment effect 0.37, 95% confidence interval 0.17 to 0.57; p p Limitations Unlike outcome assessors, participants and clinicians were not blinded to the interventions. Conclusions There was no significant additional benefit of dissociative seizure-specific cognitive–behavioural therapy in reducing dissociative seizure frequency, and cost-effectiveness over standardised medical care was low. However, this large, adequately powered, multicentre randomised controlled trial highlights benefits of adjunctive dissociative seizure-specific cognitive–behavioural therapy for several clinical outcomes, with no evidence of greater harm from dissociative seizure-specific cognitive–behavioural therapy. Future work Examination of moderators and mediators of outcome. Trial registration Current Controlled Trials ISRCTN05681227 and ClinicalTrials.gov NCT02325544. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 43. See the NIHR Journals Library website for further project information.

Wearables stosen bei Epilepsiepatienten und behandelnden Arzten auf ein groses Interesse zur Erfassung der Anfallsfrequenz und Warnung bei Anfallen, daruber hinaus auch zur Erkennung anfallsassoziierter Gefahrdungen von Patienten, zur differenzialdiagnostischen Einschatzung seltener Anfallsformen und zur Vorhersage von Perioden mit erhohter Anfallswahrscheinlichkeit. Akzelerometrie, Elektromyographie, Herzfrequenzmessungen und weitere Verfahren zur Messung autonomer Parameter werden hierbei zur Erfassung klinischer Anfallssymptome eingesetzt. Gegenwartig wird ein klinischer Einsatz zur Erfassung nachtlicher motorischer Anfalle moglich. In der Ubersicht werden verfugbare Gerate, Daten zur Leistungsfahigkeit bei der Dokumentation von Anfallen, aktuelle Einsatzmoglichkeiten sowie Entwicklungen in der Datenanalyse kritisch dargestellt und diskutiert.

Purpose To estimate the incidence of ictal hypoxemia (IH) and to identify clinical and study-related factors modulating the estimate. Methods We searched articles recording concurrent peri-ictal and ictal EEG and Sp02 in adults and children with epilepsy. Studies reporting the total number of seizures recorded and the number of seizures with IH were included in a random-effects meta-analysis. A random-effects meta-regression was used to identify variables affecting study heterogeneity. Results Twenty-one studies, including 917 participants and 1840 with SpO2 data available were included. The meta-analysis showed a pooled incidence of IH of 35/100 seizures (95% CI 27–44). Sp02 desaturation threshold was associated with the incidence of IH, with less severe desaturations resulting in higher IH frequencies. The incidence of IH was 41/100 seizures (95% CI 29–54) for adults and 47/100 seizures (95% CI 15–78) for tonic-clonic seizures. The meta-regression showed that SpO2 desaturation severity was the sole variable significantly correlated with the incidence of ictal hypoxemia (p = 0.00). Conclusion In a population with refractory epilepsy IH is a frequent phenomenon, especially in adults and in patients presenting with tonic-clonic seizures. The severity of IH appeared independent from the age group and from seizure type and is probably the major clinical concern for its correlation with potentially life-threatening cardiorespiratory alterations and sudden unexpected death in epilepsy (SUDEP).

Aims\ud The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis.\ud \ud Methods\ud Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy.\ud \ud Results\ud We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia.\ud \ud Conclusions\ud These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

Highlights • Machine learning and artificial intelligence have gained popularity for medical applications. • We applied support vector machine (SV) and deep learning (DL) in termporal lobe epilepsy (TLE) • Structural and diffusion-based models showed similar classification accuracies. • Diffusion-based models to diagnose TLE performed better or similar compared to models to lateralize TLE. • Models for patients with hippocampal sclerosis were more accurate than models that stratified non-lesional patients., Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Purpose: Focal seizures constitute the most common seizure type and are associated with poor control. One of the major difficulties in detecting focal onset with wearable devices seizures is related to their phenomenological complexity. We aimed at capturing focal onset seizures with motor manifestations with a multimodal wearable device to identify the digital semiology and the evolution pattern of ictal manifestations. Methods: Participants were asked to wear a multimodal wearable device (IMEC) aimed at seizure detection while admitted to an epilepsy monitoring unit. Seizures were labelled by a neurologist and start and offset time were noted. The signals captured by the device during the seizure window were plotted and a visual inspection was performed for focal motor seizures with impaired awareness and for focal motor aware seizures. Results: Fifty-three seizures from twelve patients with focal seizures with motor manifestations recorded with the device were visually inspected. Overall, a common pattern presented across focal motor seizures with impaired awareness and it was characterized by early cardiac manifestations followed by motor phenomena and final EDA response. Motor seizures with retained awareness appeared to be characterized by brief motor events not associated with major autonomic manifestations Conclusion: an overall common digital phenotype and time-evolution pattern was demonstrated for focal motor seizures with impaired awareness. The identification of the evolution pattern could more precisely inform the development of highly preforming algorithms opening the possibility to a more precise, and potentially customizable way to optimize focal seizure detection.

Copyright © 2021 Brinkmann, Karoly, Nurse, Dumanis, Nasseri, Viana, Schulze-Bonhage, Freestone, Worrell, Richardson and Cook. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms., It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic-clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.

Brain atrophy in human epilepsy syndromes is explainable by network architecture and strongest in hub regions., Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1021 adults with epilepsy compared to 1564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy colocalized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Negative effects of age on atrophy further revealed a strong influence of connectome architecture in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided deeper insights into the macroscale features that shape the pathophysiology of common epilepsies.

Video-electroencephalographic (EEG) monitoring is an essential tool in epileptology, conventionally carried out in a hospital epilepsy monitoring unit. Due to high costs and long waiting times for hospital admission, coupled with technological advances, several centers have developed and implemented video-EEG monitoring in the patient's home (home video-EEG telemetry [HVET]). Here, we review the history and current status of three general approaches to HVET: (1) supervised HVET, which entails setting up video-EEG in the patient's home with daily visiting technologist support; (2) mobile HVET (also termed ambulatory video-EEG), which entails attaching electrodes in a health care facility, supplying the patient and carers with the hardware and instructions, and then asking the patient and carer to set up recording at home without technologist support; and (3) cloud-based HVET, which adds to either of the previous models continuous streaming of video-EEG from the home to the health care provider, with the option to review data in near real time, troubleshoot hardware remotely, and interact remotely with the patient. Our experience shows that HVET can be highly cost-effective and is well received by patients. We note limitations related to long-term electrode attachment and correct camera placing while the patient is unsupervised at home, and concerns related to regulations regarding data privacy for cloud services. We believe that HVET opens significant new opportunities for research, especially in the field of understanding the many influences in seizure occurrence. We speculate that in the future HVET may merge into innovative new multisensor approaches to continuously monitoring people with epilepsy.

Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor-quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in-hospital or in-home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high-quality, marginal-quality, or poor-quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good-quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good-quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good-, marginal-, and poor-quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist-worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high-quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.

Purpose wearable devices aimed at detecting seizures are rapidly emerging. Continuous collection and optimal data quality are paramount to guarantee the acquisition of clinically meaningful data. It is still unknown how successfully patients can self-manage new technologies and which factors have an impact on this. We assessed the performance of patients managing a wrist-worn device. Method patients wearing a wrist-worn device received a single training session to perform 5 tasks: (1) fitting the device correctly; (2) switching the device on and off; (3) charging the device on a daily basis; (4) pairing the device with a phone or tablet; (5) seeking assistance. Participants were then reviewed every 24 h and, at the end of the study, a Wearable technology Self-management Score (WSS) was calculated according to their performance in the different tasks (0–12). The association between WSS, seizure capture, demographics and clinical characteristics was analysed. Results Thirty patients were included. The mean WSS score was 9.4 ± 2.1 points. The task more often performed inaccurately was pairing the device with a phone or tablet, followed by performing charging procedures. A strong association was found between WSS and seizure capture (p = 0.019), with higher scores strongly associated with more seizures captured. A WSS of ≥9 was the minimum value of WSS that allowed the device to record all the seizures. Number of AEDs and illness-perception related factors (perceived disease timeline and personal control) were significantly associated with WSS. Conclusions Overall, participants demonstrated good performances in self-managing a wrist-worn device. Digital inequalities may extend to variations in how different individuals feel about their own disease and, consequently, manage the technology. These aspects should be addressed when technological solutions are delivered to users.

In patients with epilepsy, the potential to prevent seizure-related injuries and to improve the unreliability of seizure self-report have fostered the development and marketing of numerous seizure detection devices for home use. Understanding the requirements of users (patients and caregivers) is essential to improve adherence and mitigate barriers to the long-term use of such devices. Here we reviewed the evidence on the needs and preferences of users and provided an overview of currently marketed devices for seizure detection (medically approved or with published evidence for their performance). We then compared devices with known needs. Seizure-detection devices are expected to improve safety and clinical and self-management, and to provide reassurance to users. Key factors affecting a device's usability relate to its design (attractive appearance, low visibility, low intrusiveness), comfort of use, confidentiality of recorded data, and timely support from both technical and clinical ends. High detection sensitivity and low false alarm rates are paramount. Currently marketed devices are focused primarily on the recording of non-electroencephalography (EEG) signals associated with tonic-clonic seizures, whereas the detection of focal seizures without major motor features remains a clear evidence gap. Moreover, there is paucity of evidence coming from real-life settings. A joint effort of clinical and nonclinical experts, patients, and caregivers is required to ensure an optimal level of acceptability and usability, which are key aspects for a successful continuous monitoring aimed at seizure detection at home.

Highlights • Computational modelling is combined with scalp EEG to assess epilepsy lateralization. • Our approach proved useful in informing lateralization in 12 out of 15 individuals studied. • The framework proposed may be used to aid deciding where to implant intracranial electrodes., Objective The effectiveness of intracranial electroencephalography (iEEG) to inform epilepsy surgery depends on where iEEG electrodes are implanted. This decision is informed by noninvasive recording modalities such as scalp EEG. Herein we propose a framework to interrogate scalp EEG and determine epilepsy lateralization to aid in electrode implantation. Methods We use eLORETA to map source activities from seizure epochs recorded from scalp EEG and consider 15 regions of interest (ROIs). Functional networks are then constructed using the phase-locking value and studied using a mathematical model. By removing different ROIs from the network and simulating their impact on the network’s ability to generate seizures in silico, the framework provides predictions of epilepsy lateralization. We consider 15 individuals from the EPILEPSIAE database and study a total of 62 seizures. Results were assessed by taking into account actual intracranial implantations and surgical outcome. Results The framework provided potentially useful information regarding epilepsy lateralization in 12 out of the 15 individuals (p=0.02, binomial test). Conclusions Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization. Significance The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.

Background: Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link is highly relevant for epilepsy, with implications for seizure prediction, therapy (i.e. vagal nerve stimulation) and mortality (i.e. sudden unexpected death in epilepsy). Methods: We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study quantified multiday cycles of heart rate and seizures in human participants (N=26) using wearable smartwatches and mobile seizure diaries over at least two months (M=6.4, SD=4.0). Participants had an epilepsy diagnosis and uncontrolled or partially controlled seizures. Significant cycles in heart rate were detected using a continuous wavelet transform. Significant co-modulation between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase (Omnibus test). Weekly trends were also investigated from a retrospective cohort of continuous video-EEG-ECG records of people (N=29) undergoing diagnostic epilepsy monitoring for at least eight days (M=9.7, SD=1.3). Findings: Heart rate cycles were found in all participants, with circadian (100%), about-weekly (88%) and about-monthly (92%) rhythms being the most prevalent. Seizures were significantly locked onto at least one heart rate cycle in 76% of eligible participants. Seizures occurred preferentially during the rising phase of the heart rate cycles. Significant weekly trends were also verified in 52% of the video-EEG-ECG cohort. Interpretation: The existence and prevalence of multiday heart rate cycles may have clinical implications for cardiology. Heart rate cycles showed striking similarities to multiday epileptic rhythms and were co-modulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans. Funding Statement: This research was funded by an NHMRC Investigator Grant 1178220. This research was supported by the Epilepsy Foundation of America's My Seizure Gauge grant. Declaration of Interests: Dr. Brinkmann reports grants from Epilepsy Foundation of America, My Seizure Gauge, during the conduct of the study; other from Cadence Neurosciences, outside the submitted work. Dr. Freestone reports grants from Epilepsy Foundation USA, personal fees and other from Seer Medical, during the conduct of the study; In addition, Dr. Freestone has a patent Methods and Systems of Seizure Forecasting issued. Dr. Nurse reports grants from Epilepsy Foundation of America, grants from MTPConnect, during the conduct of the study; personal fees from Seer Medical, outside the submitted work. Dr. Cook reports personal fees and other from Seer Medical Australia, personal fees and other from Epi Minder, outside the submitted work; In addition, Dr. Cook has a patent Methods and Systems of Seizure Forecasting issued. Dr. Karoly reports grants from National Health and Medical Research Council (NHMRC), during the conduct of the study; other from Seer Medical (Australia), outside the submitted work; In addition, Dr. Karoly has a patent Methods and Systems of Seizure Forecasting issued. The remaining authors have no competing interests to declare. Ethics Approval Statement: The study was approved by the St Vincent’s Hospital Human Research Ethics Committee (HREC 009.19) and first enrolment was in August 2019. All participants provided written informed consent.

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra–long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra–long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.

We studied slow (≤2.5Hz) non-evolving generalized periodic epileptiform discharges (GPED) in the EEG of comatose patients after cardiac arrest (CA) in search of evidence that could assist early diagnosis of possible hypoxic non-convulsive status epilepticus (NCSE) and its differentiation from terminal brain anoxia (BA) that can present with similar EEG pattern. We investigated the topography of the GPED in the first post-CA EEGs of 13 patients, using voltage-mapping and compared findings between 2 patients with NCSE and GPED >2.5Hz (group 1), and 11 with GPED ≤2Hz, of whom 6 had possible NCSE (group 2) and 5 terminal BA (group 3). Voltage mapping showed frontal maximum for the negative phase of the GPED in all patients of groups 1 and 2, but not in any of the patients of group 3, who invariably showed posterior maximum of the negative phase. Morphology, amplitude and duration of the GPED varied across the groups without distinctive features for possible NCSE. These findings provide evidence that, in hypoxic coma after CA with slow GPED, anterior topography of the maximum GPED negativity on voltage mapping may be a distinctive biomarker for possible NCSE contributing to the coma. This article is protected by copyright. All rights reserved.

Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy. Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we combine computational models with a genetic algorithm to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy. We show that they have the potential to aid epilepsy surgery by suggesting alternative resection sites as well as facilitating the avoidance of brain regions that should not be resected.

Purpose: To explore the retention rates and the efficacy and tolerability of perampanel (PER) by using monthly real life data for a period of 12 months. Methods: Longitudinal outcomes of (PER) usage were assessed using actuarial statistics in an observational nonrandomised multicentre study of 181 people with epilepsy (PWE) refractory to first and second line drugs. Graded seizure outcomes, toxicity and the dose of PER were recorded for each month. Results: PWE were followed for a mean of 15.1 months. The total cumulative probability for retention on PER at 12 months was 61.7% and for ≥50% improvement was 38.2%. Most improvements in seizure control occurred soon after initiation of PER, 17% by one month, 32% by six months and 38% by twelve months, and mostly at low doses 53% on 2 mg and 90% up to 6 mg. Improvements, when they occurred, were sustained. The most common side effects were neuropsychiatric, occurring in 28%. The emergence of side effects did not appear to be dose related. Although people with intellectual disability (ID) were more likely to remain on PER they did not show improved seizure control and also reported more side effects. Patients treated with VNS and PER had a worse outcome. Conclusion: Overall around a third of people showed a useful, response to PER therapy. The response to PER is noted usually early in the treatment and for the majority of the patients for doses up to 8 mg.

The propagation of epileptic seizure activity in the brain is a widespread pathophysiology that, in principle, should yield to intervention techniques guided by mathematical models of neuronal ensemble dynamics. During a seizure, neural activity will deviate from its current dynamical regime to one in which there are significant signal fluctuations. In silico treatments of neural activity are an important tool for the understanding of how the healthy brain can maintain stability, as well as of how pathology can lead to seizures. The hope is that, contained within the mathematical foundations of such treatments, there lie potential strategies for mitigating instabilities, e.g. via external stimulation. Here, we demonstrate that the dynamic causal modelling neuronal state equation generalises to a Fokker-Planck formalism if one extends the framework to model the ways in which activity propagates along the structural connections of neural systems. Using the Jacobian of this generalised state equation, we show that an initially unstable system can be rendered stable via a reduction in diffusivity–i.e., by lowering the rate at which neuronal fluctuations disperse to neighbouring regions. We show, for neural systems prone to epileptic seizures, that such a reduction in diffusivity can be achieved via external stimulation. Specifically, we show that this stimulation should be applied in such a way as to temporarily mirror the activity profile of a pathological region in its functionally connected areas. This counter-intuitive method is intended to be used pre-emptively–i.e., in order to mitigate the effects of the seizure, or ideally even prevent it from occurring in the first place. We offer proof of principle using simulations based on functional neuroimaging data collected from patients with idiopathic generalised epilepsy, in which we successfully suppress pathological activity in a distinct sub-network prior to seizure onset. Our hope is that this technique can form the basis for future real-time monitoring and intervention devices that are capable of treating epilepsy in a non-invasive manner., Author summary Epilepsy is a disease that affects over 50 million people worldwide. Current treatments include dangerous surgical procedures in which brain connections are severed, or even in which entire problem brain regions are removed. Pharmaceutical options are available, but only about one third of patients are responsive. However, even in these cases the drugs can cause such severe side effects that the patients sometimes choose to suffer seizures. We are proposing an innovative treatment of epilepsy that could be achieved by using non-invasive electrical stimulation. Specifically, we show that stimulation should be applied in such a way as to mirror the activity in a problem brain region, by targeting its neighbouring areas. This counterintuitive approach is based on a mathematical model in which this mirroring strategy is applied pre-emptively, i.e. long before the seizure has a chance to set in. The hope is that future clinical trials will be able to use this model to lessen the effect of seizures, or even prevent them from occurring in the first place.

Purpose Wearable devices are progressively becoming an available tool for continuous seizure detection. Motivation to use wearables is not only driven by the accuracy and reliability of the performance but also by the form factor, comfort, and stability on the body. We collected direct feedback and device placement-related issues experienced by a cohort of people with epilepsy (PWE) to investigate to what extent available devices are nonintrusive, comfortable, and stable on the body. Methods Four models of wearable devices (E4 wrist band, Everion upper arm band, IMEC upper arm band, and Epilog scalp patch electrodes) were worn by PWE who were admitted to two epilepsy monitoring units (EMUs) in London and Freiburg. Participants were periodically reviewed, and accidental displacements of the devices were annotated. Participants' experience was assessed using the Technology Acceptance Model Fast Form (TAM-FF) plus two additional questions on comfort. A thematic analysis was also performed on the free text of the questionnaire. Results One hundred and fifteen participants were enrolled. The devices had a good stability on the body including during seizures. Overall, all the devices were considered comfortable to be worn, including during sleep. However, devices containing wires and patches demonstrated a lesser degree of stability on the body and were judged less positively. Participants age was correlated with TAM-FF mean scores, and older participants judged the devices less favorably compared with younger participants. Discussion Removable but securely fitted, wireless, and comfortable designs were considered more appropriate for a continuous monitoring aimed at seizure detection. Some caution may be required when patch electrodes and electrodes glued to the skin or to the scalp are used, as those evaluated in the present study demonstrated a lower level of acceptability and a lower degree of stability to the body, especially at night. These factors could limit a continuous monitoring decreasing the device performance for nocturnal, unsupervised seizures which are at higher risk of lethality.

Summary Objective Epilepsy is one of the most common neurological conditions affecting about 1% of adults. Up to 40% of people with epilepsy (PWE) report recurring seizures while on medication. And optimal functioning requires good self‐management. Our objective was to evaluate a group self‐management education courses for people with epilepsy and drug‐resistant seizures by means of a multicenter, pragmatic, parallel group, randomized controlled trial. Methods We recruited adults with epilepsy, having ≥2 seizures in the prior 12 months, from specialist clinics. Consenting participants were randomized 1:1 to a group course or treatment as usual. The primary outcome measure was quality of life 12 months after randomization using Quality of Life 31‐P (QOLIE‐31‐P). Secondary outcome measures were seizure frequency and recency, psychological distress, impact and stigma of epilepsy, self‐mastery, medication adherence, and adverse effects. Analysis of outcomes followed the intention‐to‐treat principle using mixed‐effects regression models. Results We enrolled 404 participants (intervention: n = 205, control: n = 199) with 331 (82%) completing 12‐month follow‐up (intervention: n = 163, control: n = 168). Mean age was 41.7 years, ranging from 16 to 85, 54% were female and 75% were white. From the intervention arm, 73.7% attended all or some of the course. At 12‐month follow‐up, there were no statistically significant differences between trial arms in QOLIE‐31‐P (intervention mean: 67.4, standard deviation [SD]: 13.5; control mean: 69.5, SD 14.8) or in secondary outcome measures. Significance This is the first pragmatic trial of group education for people with poorly controlled epilepsy. Recruitment, course attendance, and follow‐up rates were higher than expected. The results show that the primary outcome and quality of life did not differ between the trial arms after 12 months. We found a high prevalence of felt‐stigma and psychological distress in this group of people with drug‐resistant seizures. To address this, social and psychological interventions require evaluation, and may be necessary before or alongside self‐management‐education courses.

Objective To investigate the clinical and surgical outcome correlates of preoperative hippocampal subfield volumes in patients with refractory temporal lobe epilepsy (TLE) using a new magnetic resonance imaging (MRI) multisequence segmentation technique. Methods We recruited 106 patients with TLE and hippocampal sclerosis (HS) who underwent conventional T1-weighted and T2 short TI inversion recovery MRI. An automated hippocampal segmentation algorithm was used to identify twelve subfields in each hippocampus. A total of 76 patients underwent amygdalohippocampectomy and postoperative seizure outcome assessment using the standardized ILAE classification. Semiquantitative hippocampal internal architecture (HIA) ratings were correlated with hippocampal subfield volumes. Results Patients with left TLE had smaller volumes of the contralateral presubiculum and hippocampus-amygdala transition area compared to those with right TLE. Patients with right TLE had reduced contralateral hippocampal tail volumes and improved outcomes. In all patients, there were no significant relationships between hippocampal subfield volumes and clinical variables such as duration and age at onset of epilepsy. There were no significant differences in any hippocampal subfield volumes between patients who were rendered seizure free and those with persistent postoperative seizure symptoms. Ipsilateral but not contralateral HIA ratings were significantly correlated with gross hippocampal and subfield volumes. Conclusions Our results suggest that ipsilateral hippocampal subfield volumes are not related to the chronicity/severity of TLE. We did not find any hippocampal subfield volume or HIA rating differences in patients with optimal and unfavorable outcomes. In patients with TLE and HS, sophisticated analysis of hippocampal architecture on MRI may have limited value for prediction of postoperative outcome.