Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease.

Alzheimer's Disease (AD) is the leading cause of dementia. It results in cortical thickness changes and is associated with a decline in cognition and behaviour. Such decline affects multiple important day-to-day functions, including memory, language, orientation, judgment and problem-solving. Recent research has made important progress in identifying brain regions associated with single outcomes, such as individual AD status and general cognitive decline. The complex projection from multiple brain areas to multiple AD outcomes, however, remains poorly understood. This makes the assessment and especially the prediction of multiple AD outcomes - each of which may unveil an integral yet different aspect of the disease - challenging, particularly when some are not strongly correlated. Here, uniting residual learning, partial least squares (PLS), and predictive modelling, we develop an explainable, generalisable, and reproducible method called the Residual Partial Least Squares Learning (the re-PLS Learning) to (1) chart the pathways between large-scale multivariate brain cortical thickness data (inputs) and multivariate disease and behaviour data (outcomes); (2) simultaneously predict multiple, non-pairwise-correlated outcomes; (3) control for confounding variables (e.g., age and gender) affecting both inputs and outcomes and the pathways in-between; (4) perform longitudinal AD disease status classification and disease severity prediction. We evaluate the performance of the proposed method against a variety of alternatives on data from AD patients, subjects with mild cognitive impairment (MCI), and cognitively normal individuals (n=1,196) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results unveil pockets of brain areas in the temporal, frontal, sensorimotor, and cingulate areas whose cortical thickness may be respectively associated with declines in different cognitive and behavioural subdomains in AD. Finally, we characterise re-PLS' geometric interpretation and mathematical support for delivering meaningful neurobiological insights and provide an open software package (re-PLS) available at https://github.com/thanhvd18/rePLS.

Ambulatory seizure detection.

PURPOSE OF REVIEW: To review recent advances in the field of seizure detection in ambulatory patients with epilepsy. RECENT FINDINGS: Recent studies have shown that wrist or arm wearable sensors, using 3D-accelerometry, electrodermal activity or photoplethysmography, in isolation or in combination, can reliably detect focal-to-bilateral and generalized tonic-clonic seizures (GTCS), with a sensitivity over 90%, and false alarm rates varying from 0.1 to 1.2 per day. A headband EEG has also demonstrated a high sensitivity for detecting and help monitoring generalized absence seizures. In contrast, no appropriate solution is yet available to detect focal seizures, though some promising findings were reported using ECG-based heart rate variability biomarkers and subcutaneous EEG. SUMMARY: Several FDA and/or EU-certified solutions are available to detect GTCS and trigger an alarm with acceptable rates of false alarms. However, data are still missing regarding the impact of such intervention on patients' safety. Noninvasive solutions to reliably detect focal seizures in ambulatory patients, based on either EEG or non-EEG biosignals, remain to be developed. To this end, a number of challenges need to be addressed, including the performance, but also the transparency and interpretability of machine learning algorithms.

Combining general and personal models for epilepsy detection with hyperdimensional computing.

Epilepsy is a highly prevalent chronic neurological disorder with great negative impact on patients' daily lives. Despite this there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday life. Hyperdimensional (HD) computing is a promising method for epilepsy detection via wearable devices, characterized by a simpler learning process and lower memory requirements compared to other methods. In this work, we demonstrate additional avenues in which HD computing and the manner in which its models are built and stored can be used to better understand, compare and create more advanced machine learning models for epilepsy detection. These possibilities are not feasible with other state-of-the-art models, such as random forests or neural networks. We compare inter-subject model similarity of different classes (seizure and non-seizure), study the process of creating general models from personal ones, and finally posit a method of combining personal and general models to create hybrid models. This results in an improved epilepsy detection performance. We also tested knowledge transfer between models trained on two different datasets. The attained insights are highly interesting not only from an engineering perspective, to create better models for wearables, but also from a neurological perspective, to better understand individual epilepsy patterns.

Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers.

Electroencephalography (EEG) is widely used to monitor epileptic seizures, and standard clinical practice consists of monitoring patients in dedicated epilepsy monitoring units via video surveillance and cumbersome EEG caps. Such a setting is not compatible with long-term tracking under typical living conditions, thereby motivating the development of unobtrusive wearable solutions. However, wearable EEG devices present the challenges of fewer channels, restricted computational capabilities, and lower signal-to-noise ratio. Moreover, artifacts presenting morphological similarities to seizures act as major noise sources and can be misinterpreted as seizures. This paper presents a combined seizure and artifacts detection framework targeting wearable EEG devices based on Gradient Boosted Trees. The seizure detector achieves nearly zero false alarms with average sensitivity values of [Formula: see text] for 182 seizures from the CHB-MIT dataset and [Formula: see text] for 25 seizures from the private dataset with no preliminary artifact detection or removal. The artifact detector achieves a state-of-the-art accuracy of [Formula: see text] (on the TUH-EEG Artifact Corpus dataset). Integrating artifact and seizure detection significantly reduces false alarms-up to [Formula: see text] compared to standalone seizure detection. Optimized for a Parallel Ultra-Low Power platform, these algorithms enable extended monitoring with a battery lifespan reaching 300 h. These findings highlight the benefits of integrating artifact detection in wearable epilepsy monitoring devices to limit the number of false positives.

Grading system for assessing the confidence in the epileptogenic zone reported in published studies: A Delphi consensus study.

OBJECTIVE: This study was undertaken to develop a standardized grading system based on expert consensus for evaluating the level of confidence in the localization of the epileptogenic zone (EZ) as reported in published studies, to harmonize and facilitate systematic reviews in the field of epilepsy surgery. METHODS: We conducted a Delphi study involving 22 experts from 18 countries, who were asked to rate their level of confidence in the localization of the EZ for various theoretical clinical scenarios, using different scales. Information provided in these scenarios included one or several of the following data: magnetic resonance imaging (MRI) findings, invasive electroencephalography summary, and postoperative seizure outcome. RESULTS: The first explorative phase showed an overall interrater agreement of .347, pointing to large heterogeneity among experts' assessments, with only 17% of the 42 proposed scenarios associated with a substantial level of agreement. A majority showed preferences for the simpler scale and single-item scenarios. The successive Delphi voting phases resulted in a majority consensus across experts, with more than two thirds of respondents agreeing on the rating of each of the tested single-item scenarios. High or very high levels of confidence were ascribed to patients with either an Engel class I or class IA postoperative seizure outcome, a well-delineated EZ according to all available invasive EEG (iEEG) data, or a well-delineated focal epileptogenic lesion on MRI. MRI signs of hippocampal sclerosis or atrophy were associated with a moderate level of confidence, whereas a low level was ascribed to other MRI findings, a poorly delineated EZ according to iEEG data, or an Engel class II-IV postoperative seizure outcome. SIGNIFICANCE: The proposed grading system, based on an expert consensus, provides a simple framework to rate the level of confidence in the EZ reported in published studies in a structured and harmonized way, offering an opportunity to facilitate and increase the quality of systematic reviews and guidelines in the field of epilepsy surgery.

Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs.

The long-term, continuous analysis of electroencephalography (EEG) signals on wearable devices to automatically detect seizures in epileptic patients is a high-potential application field for deep neural networks, and specifically for transformers, which are highly suited for end-to-end time series processing without handcrafted feature extraction. In this work, we propose a small-scale transformer detector, the EEGformer, compatible with unobtrusive acquisition setups that use only the temporal channels. EEGformer is the result of a hardware-oriented design exploration, aiming for efficient execution on tiny low-power micro-controller units (MCUs) and low latency and false alarm rate to increase patient and caregiver acceptance.Tests conducted on the CHB-MIT dataset show a 20% reduction of the onset detection latency with respect to the state-of-the-art model for temporal acquisition, with a competitive 73% seizure detection probability and 0.15 false-positive-per-hour (FP/h). Further investigations on a novel and challenging scalp EEG dataset result in the successful detection of 88% of the annotated seizure events, with 0.45 FP/h.We evaluate the deployment of the EEGformer on three commercial low-power computing platforms: the single-core Apollo4 MCU and the GAP8 and GAP9 parallel MCUs. The most efficient implementation (on GAP9) results in as low as 13.7 ms and 0.31 mJ per inference, demonstrating the feasibility of deploying the EEGformer on wearable seizure detection systems with reduced channel count and multi-day battery duration.

GABAergic inhibition shapes behavior and neural dynamics in human visual working memory.

Neuronal inhibition, primarily mediated by GABAergic neurotransmission, is crucial for brain development and healthy cognition. Gamma-aminobutyric acid concentration levels in sensory areas have been shown to correlate with hemodynamic and oscillatory neuronal responses. How these measures relate to one another during working memory, a higher-order cognitive process, is still poorly understood. We address this gap by collecting magnetoencephalography, functional magnetic resonance imaging, and Flumazenil positron emission tomography data within the same subject cohort using an n-back working-memory paradigm. By probing the relationship between GABAA receptor distribution, neural oscillations, and Blood Oxygen Level Dependent (BOLD) modulations, we found that GABAA receptor density in higher-order cortical areas predicted the reaction times on the working-memory task and correlated positively with the peak frequency of gamma power modulations and negatively with BOLD amplitude. These findings support and extend theories linking gamma oscillations and hemodynamic responses to gamma-aminobutyric acid neurotransmission and to the excitation-inhibition balance and cognitive performance in humans. Considering the small sample size of the study, future studies should test whether these findings also hold for other, larger cohorts as well as to examine in detail how the GABAergic system and neural fluctuations jointly support working-memory task performance.

Focal electroclinical features in generalized tonic-clonic seizures: Decision flowchart for a diagnostic challenge.

OBJECTIVE: Bilateral tonic-clonic seizures with focal semiology or focal interictal electroencephalography (EEG) can occur in both focal and generalized epilepsy types, leading to diagnostic errors and inappropriate therapy. We investigated the prevalence and prognostic values of focal features in patients with idiopathic generalized epilepsy (IGE), and we propose a decision flowchart to distinguish between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal EEG or semiology. METHODS: We retrospectively analyzed video-EEG recordings of 101 bilateral tonic-clonic seizures from 60 patients (18 with IGE, 42 with focal epilepsy). Diagnosis and therapeutic response were extracted after ≥1-year follow-up. The decision flowchart was based on previous observations and assessed concordance between interictal and ictal EEG. RESULTS: Focal semiology in IGE was observed in 75% of seizures and 77.8% of patients, most often corresponding to forced head version (66.7%). In patients with multiple seizures, direction of head version was consistent across seizures. Focal interictal epileptiform discharges (IEDs) were observed in 61.1% of patients with IGE, whereas focal ictal EEG onset only occurred in 13% of seizures and 16.7% of patients. However, later during the seizures, a reproducible pattern of 7-Hz lateralized ictal rhythm was observed in 56% of seizures, associated with contralateral head version. We did not find correlation between presence of focal features and therapeutic response in IGE patients. Our decision flowchart distinguished between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal features with an accuracy of 96.6%. SIGNIFICANCE: Focal semiology associated with bilateral tonic-clonic seizures and focal IEDs are common features in patients with IGE, but focal ictal EEG onset is rare. None of these focal findings appears to influence therapeutic response. By assessing the concordance between interictal and ictal EEG findings, one can accurately distinguish between focal and generalized epilepsies.

[Neurology: what's new in 2023]. / Neurologie: ce qui a changé en 2023.

The year 2023 is marked by the arrival on the market of lecanemab for the treatment of Alzheimer's disease. New biomarkers have demonstrated their usefulness in monitoring peripheral neuropathies and diagnosing synucleinopathies. A genetic study has highlighted the role of nervous system cells in the risk of progression of multiple sclerosis (MS). The adverse effects of anticonvulsant treatments after prenatal exposure and on lipid metabolism have been clarified. New anti-CGRP treatments have demonstrated their efficacy in migraine attacks and chronic migraines. The criteria for thrombectomy have been further broadened. And finally, rehabilitation is refining the management of cerebrovascular patients and those with secondary progressive MS.

L'année 2023 est marquée par l'arrivée sur le marché du lécanémab pour le traitement de la maladie d'Alzheimer. De nouveaux biomarqueurs ont démontré leur utilité dans le suivi des neuropathies périphériques ou dans le diagnostic des synucléinopathies. Une étude génétique a mis en évidence le rôle des cellules du système nerveux dans le risque de progression de la sclérose en plaques (SEP). Les effets indésirables des traitements anticonvulsivants lors d'exposition prénatale ou sur le métabolisme des lipides ont été précisés. De nouveaux traitements anti-CGRP ont démontré leur efficacité dans les crises migraineuses et les migraines chroniques. Les critères de thrombectomie se sont encore élargis. Et enfin, la réhabilitation affine la prise en charge des patients cérébrovasculaires et de ceux atteints d'une SEP secondaire progressive.

Report of the ILAE SUDEP Task Force on national recommendations and practices around the world regarding the use of wearable seizure detection devices: A global survey.

Wearable seizure detection devices have the potential to address unmet needs of people with epilepsy. A recently published evidence-based international guideline recommends using such devices for safety indications in patients with tonic-clonic seizures (TCS). Our objective was to map existing guidelines and clinical practices at national level. We conducted a survey of the International League Against Epilepsy (ILAE) chapters regarding national recommendations and practical circumstances for prescribing seizure detection devices, and another survey of physicians in the ILAE constituency anywhere in the world, concerning their views and practices regarding recommendations for and prescription of such devices. Fifty-eight ILAE chapters (response rate 48%) and 157 physicians completed the surveys. More than two-thirds of responding countries do not have standards on wearables for seizure detection, although they indicated availability of such devices. The most often recognized indications were safety and objective seizure quantification. In nearly half of countries, devices are purchased by patients or caregivers, and either lack a uniform reimbursement scheme (41%) or patients pay the full cost for the device (48%). Tonic-clonic seizure frequency, nocturnal seizures, and previous injuries were the main factors that influenced the surveyed physicians to recommend wearable seizure detection devices. Our results document the need to implement international clinical practice guidelines at national level and to consider these when deciding upon reimbursement of seizure detection devices.

Structural connectivity in recovery after coma: Connectome atlas approach.

AIM: Pathological states of recovery after coma as a result of a severe brain injury are marked with changes in structural connectivity of the brain. This study aimed to identify a topological correlation between white matter integrity and the level of functional and cognitive impairment in patients recovering after coma. METHODS: Structural connectomes were computed based on fractional anisotropy maps from 40 patients using a probabilistic human connectome atlas. We used a network based statistics approach to identify potential brain networks associated with a more favorable outcome, assessed with clinical neurobehavioral scores at the patient's discharge from the acute neurorehabilitation unit. RESULTS: We identified a subnetwork whose strength of connectivity correlated with a more favorable outcome as measured with the Disability Rating Scale (network based statistics: t >3.5, P =.010). The subnetwork predominated in the left hemisphere and included the thalamic nuclei, putamen, precentral and postcentral gyri, and medial parietal regions. Spearman correlation between the mean fractional anisotropy value of the subnetwork and the score was ρ = -0.60 (P <.0001). A less extensive overlapping subnetwork correlated with the Coma Recovery Scale Revised score, consisting mostly of the left hemisphere connectivity between the thalamic nuclei and pre- and post-central gyri (network based statistics: t >3.5, P =.033; Spearman's ρ = 0.58, P <.0001). CONCLUSION: The present findings suggest an important role of structural connectivity between the thalamus, putamen and somatomotor cortex in the recovery from coma as evaluated with neurobehavioral scores. These structures are part of the motor circuit involved in the generation and modulation of voluntary movement, as well as the forebrain mesocircuit supposedly underlying the maintenance of consciousness. As behavioural assessment of consciousness depends heavily on the signs of voluntary motor behaviour, further work will elucidate whether the identified subnetwork reflects the structural architecture underlying the recovery of consciousness or rather the ability to communicate its content.

Deep Brain Stimulation of the Anterior Nucleus of the Thalamus in Drug-Resistant Epilepsy in the MORE Multicenter Patient Registry.

BACKGROUND AND OBJECTIVES: The efficacy of deep brain stimulation of the anterior nucleus of the thalamus (ANT DBS) in patients with drug-resistant epilepsy (DRE) was demonstrated in the double-blind Stimulation of the Anterior Nucleus of the Thalamus for Epilepsy randomized controlled trial. The Medtronic Registry for Epilepsy (MORE) aims to understand the safety and longer-term effectiveness of ANT DBS therapy in routine clinical practice. METHODS: MORE is an observational registry collecting prospective and retrospective clinical data. Participants were at least 18 years old, with focal DRE recruited across 25 centers from 13 countries. They were followed for at least 2 years in terms of seizure frequency (SF), responder rate (RR), health-related quality of life (Quality of Life in Epilepsy Inventory 31), depression, and safety outcomes. RESULTS: Of the 191 patients recruited, 170 (mean [SD] age of 35.6 [10.7] years, 43% female) were implanted with DBS therapy and met all eligibility criteria. At baseline, 38% of patients reported cognitive impairment. The median monthly SF decreased by 33.1% from 15.8 at baseline to 8.8 at 2 years (p < 0.0001) with 32.3% RR. In the subgroup of 47 patients who completed 5 years of follow-up, the median monthly SF decreased by 55.1% from 16 at baseline to 7.9 at 5 years (p < 0.0001) with 53.2% RR. High-volume centers (>10 implantations) had 42.8% reduction in median monthly SF by 2 years in comparison with 25.8% in low-volume center. In patients with cognitive impairment, the reduction in median monthly SF was 26.0% by 2 years compared with 36.1% in patients without cognitive impairment. The most frequently reported adverse events were changes (e.g., increased frequency/severity) in seizure (16%), memory impairment (patient-reported complaint, 15%), depressive mood (patient-reported complaint, 13%), and epilepsy (12%). One definite sudden unexpected death in epilepsy case was reported. DISCUSSION: The MORE registry supports the effectiveness and safety of ANT DBS therapy in a real-world setting in the 2 years following implantation. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that ANT DBS reduces the frequency of seizures in patients with drug-resistant focal epilepsy. TRIAL REGISTRATION INFORMATION: MORE ClinicalTrials.gov Identifier: NCT01521754, first posted on January 31, 2012.

A Focus on Subtle Signs and Motor Behavior to Unveil Awareness in Unresponsive Brain-Impaired Patients: The Importance of Being Clinical.

Brain-injured patients in a state of cognitive motor dissociation (CMD) exhibit a lack of command following using conventional neurobehavioral examination tools but a high level of awareness and language processing when assessed using advanced imaging and electrophysiology techniques. Because of their behavioral unresponsiveness, patients with CMD may seem clinically indistinguishable from those with a true disorder of consciousness that affects awareness on a substantial level (coma, vegetative state/unresponsive wakefulness state, or minimally conscious state minus). Yet, by expanding the range of motor testing across limb, facial, and ocular motricity, we may detect subtle, purposeful movements even in the subset of patients classified as vegetative state/unresponsive wakefulness state. We propose the term of clinical CMD to describe patients showing these slight but determined motor responses and exhibiting a characteristic akinetic motor behavior as opposed to a pyramidal motor system behavior. These patients may harbor hidden cognitive capabilities and significant potential for a good long-term outcome. Indeed, we envision CMD as ranging from complete (no motor response) to partial (subtle clinical motor response) forms, falling within a spectrum of progressively better motor output in patients with considerable cognitive capabilities. In addition to providing a decisional flowchart, we present this novel approach to classification as a graphical model that illustrates the range of clinical manifestations and recovery trajectories fundamentally differentiating true disorders of consciousness from the spectrum of CMD.

Subject-Specific Activation of Central Respiratory Centers during Breath-Holding Functional Magnetic Resonance Imaging.

BACKGROUND: Voluntary breath-holding (BH) triggers responses from central neural control and respiratory centers in order to restore breathing. Such responses can be observed using functional MRI (fMRI). OBJECTIVES: We used this paradigm in healthy volunteers with the view to develop a biomarker that could be used to investigate disorders of the central control of breathing at the individual patient level. METHOD: In 21 healthy human subjects (mean age±SD, 32.8 ± 9.9 years old), fMRI was used to determine, at both the individual and group levels, the physiological neural response to expiratory and inspiratory voluntary apneas, within respiratory control centers in the brain and brainstem. RESULTS: Group analysis showed that expiratory BH, but not inspiratory BH, triggered activation of the pontine respiratory group and raphe nuclei at the group level, with a significant relationship between the levels of activation and drop in SpO2. Using predefined ROIs, expiratory BH, and to a lesser extent, inspiratory BH were associated with activation of most respiratory centers. The right ventrolateral nucleus of the thalamus, right pre-Bötzinger complex, right VRG, right nucleus ambiguus, and left Kölliker-Fuse-parabrachial complex were only activated during inspiratory BH. Individual analysis identified activations of cortical/subcortical and brainstem structures related to respiratory control in 19 out of 21 subjects. CONCLUSION: Our study shows that BH paradigm allows to reliably trigger fMRI response from brainstem and cortical areas involved in respiratory control at the individual level, suggesting that it might serve as a clinically relevant biomarker to investigate conditions associated with an altered central control of respiration.

Prevalence and Burden of Migraine in Switzerland: Cross-Sectional Study in ten Specialised Headache Centres from the BECOME Study.

INTRODUCTION: There is limited real-world evidence on the burden of migraine among patients with prior preventive treatment failure (PPTF). In the BECOME Swiss subanalysis, we aimed to assess current prevalence of PPTF in patients with migraine seen at specialised headache centres in Switzerland and burden of migraine in these patients. Furthermore, we assessed this burden in subgroups stratified by monthly migraine days (MMDs) and number of PPTFs. METHODS: BECOME was a prospective, multicentre, non-interventional two-part study conducted in 17 countries across Europe and Israel. This subanalysis includes patients visiting ten headache specialist centres in Switzerland. In part 1, patients visiting the centres over 3 months were screened by physicians for frequency of PPTF, MMD and other migraine characteristics. Patients with ≥ 1 PPTF and ≥ 4 MMDs were invited to take part in part 2. The primary endpoint was the proportion of patients with ≥ 1 PPTF (part 1). Other endpoints included proportion of patients specified by number of PPTF and MMD (part 1, part 2), and impact of migraine on patient-reported outcomes (PROs; part 2). RESULTS: Patients (1677) from ten Swiss centres were included in part 1, of which 855 (51.0%) reported ≥ 1 PPTF. One hundred fifty-five patients were included in part 2: 6.5% reported ≥ 4 PPTFs and 43.2% reported ≥ 15 MMDs. Mean EuroQoL 5 and EuroQoL visual analogue scale (EQ-VAS) were 0.8 ± 0.2 and 69.6 ± 20.2, respectively, suggesting a mild level of impairment in the daily functioning and self-reported health of the patients. Mean six-item Headache Impact Test (HIT-6) and modified Migraine Disability Assessment (mMIDAS) scores were 63.3 ± 6.5 and 22.7 ± 21.8, respectively, corresponding to severe migraine burden. Patients also reported impairment in work-related productivity and general activities (48.6 ± 22.8) but no associations of anxiety (7.2 ± 4.4) or depression (6.0 ± 4.4) with migraine were noted. Burden of migraine increased with increasing frequency of PPTF and MMD. CONCLUSIONS: Migraine-related quality of life, as well as work productivity are significantly affected in Swiss patients with migraine. Increasing migraine burden is associated with increasing migraine frequency and prior treatment failures.

[Neurology: what's new in 2022]. / Neurologie : ce qui a changé en 2022.

The year 2022 was marked by the development of numerous new treatments for refractory myasthenia gravis. The link between epilepsy and cerebrovascular disorder was studied and lamotrigine discovered to be the optimal treatment choice for epilepsy secondary to stroke to prevent mortality on patient of 45 years and older. New randomized study finally demonstrated the utility of thrombectomy in selected patients with basilar artery occlusion. The causal relationship between Epstein-Barr infection and multiple sclerosis has been proved thanks to a large cohort study. A new possibility of subcutaneous continuous levodopa administration gave promising result. Finally, numerous studies confirmed the efficacy and excellent tolerability of anti-CGRP antibodies.

L'année 2022 a été marquée par l'arrivée de nombreux traitements pour la myasthénie réfractaire. Le lien entre l'épilepsie et le risque cérébro-vasculaire a été bien étudié, démontrant que la lamotrigine semble être le meilleur traitement pour prévenir la mortalité chez les patients de 45 ans et plus. De nouvelles études ont enfin pu établir l'utilité de la thrombectomie dans les occlusions basilaires. Le lien entre le virus d'Epstein-Barr et la sclérose en plaques a pu être prouvé à la suite d'une importante étude de cohorte. Une nouvelle technique d'administration sous-cutanée de la lévodopa semble prometteuse. Enfin, de nombreuses études confirment l'efficacité et l'excellente tolérance des anticorps anti-CGRP (Calcitonine Gene Related Protein).

Shaping the future of European epilepsy research: Final meeting report from EPICLUSTER.

Collaboration is essential to the conduct of basic, applied and clinical research and its translation into the technologies and treatments urgently needed to improve the lives of people living with brain diseases and the health professionals who care for them. EPICLUSTER was formed in 2019 by the European Brain Research Area (EBRA) to support the coordination of epilepsy research in Europe. A key objective was to provide a platform to discuss shared research priorities by bringing together scientists and clinicians with multiple stakeholders including patient organisations and industry and the networks and infrastructures that provide healthcare and support research. Additional objectives were to facilitate access and sharing of data and biosamples, working together to ensure epilepsy is a priority for research funding, and embedding a culture of public and patient involvement (PPI) among epilepsy researchers. In this meeting report, we summarise the shared research priorities discussed by the leadership of EPICLUSTER at the recent final meeting. We also briefly review the discussion on patient and industry priorities, guidance on starting PPI for epilepsy researchers, and the sustainability of funding and infrastructures needed to ensure a comprehensive stakeholder-embedded community for epilepsy research.

Stratifying sudden death risk in adults with drug-resistant focal epilepsy: The SUDEP-CARE score.

BACKGROUND AND PURPOSE: A clinical risk score for sudden unexpected death in epilepsy (SUDEP) in patients with drug-resistant focal epilepsy could help improve prevention. METHODS: A case-control study was conducted including (i) definite or probable SUDEP cases collected by the French National Sentinel Mortality Epilepsy Network and (ii) control patients from the French national research database of epilepsy monitoring units. Patients with drug-resistant focal epilepsy were eligible. Multiple logistic regressions were performed. After sensitivity analysis and internal validation, a simplified risk score was developed from the selected variables. RESULTS: Sixty-two SUDEP cases and 620 controls were included. Of 21 potential predictors explored, seven were ultimately selected, including generalized seizure frequency (>1/month vs. <1/year: adjusted odds ratio [AOR] 2.6, 95% confidence interval [CI] 1.25-5.41), nocturnal or sleep-related seizures (AOR 4.49, 95% CI 2.68-7.53), current or past depression (AOR 2.0, 95% CI 1.19-3.34) or the ability to alert someone of an oncoming seizure (AOR 0.57, 95% CI 0.33-0.98). After internal validation, a clinically usable score ranging from -1 to 8 was developed, with high discrimination capabilities (area under the receiver operating curve 0.85, 95% CI 0.80-0.90). The threshold of 3 has good sensitivity (82.3%, 95% CI 72.7-91.8), whilst keeping a good specificity (82.7%, 95% CI 79.8-85.7). CONCLUSIONS: These results outline the importance of generalized and nocturnal seizures on the occurrence of SUDEP, and show a protective role in the ability to alert someone of an oncoming seizure. The SUDEP-CARE score is promising and will need external validation. Further work, including paraclinical explorations, could improve this risk score.