Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study.

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness.

BACKGROUND: In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations. METHODS: We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome. RESULTS: Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]). CONCLUSIONS: Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Detection of subclinical epileptiform discharges in Alzheimer's disease using long-term outpatient EEG monitoring.

BACKGROUND: In patients with Alzheimer's disease (AD) without clinical seizures, up to half have epileptiform discharges on long-term in-patient electroencephalography (EEG) recordings. Long-term in-patient monitoring is obtrusive, and expensive as compared to outpatient monitoring. No studies have so far investigated if long-term outpatient EEG monitoring is able to identify epileptiform discharges in AD. Our aim is to investigate if epileptiform discharges as measured with ear-EEG are more common in patients with AD compared to healthy elderly controls (HC). METHODS: In this longitudinal observational study, 24 patients with mild to moderate AD and 15 age-matched HC were included in the analysis. Patients with AD underwent up to three ear-EEG recordings, each lasting up to two days, within 6 months. RESULTS: The first recording was defined as the baseline recording. At baseline, epileptiform discharges were detected in 75.0% of patients with AD and in 46.7% of HC (p-value = 0.073). The spike frequency (spikes or sharp waves/24 h) was significantly higher in patients with AD as compared to HC with a risk ratio of 2.90 (CI: 1.77-5.01, p < 0.001). Most patients with AD (91.7%) showed epileptiform discharges when combining all ear-EEG recordings. CONCLUSIONS: Long-term ear-EEG monitoring detects epileptiform discharges in most patients with AD with a three-fold increased spike frequency compared to HC, which most likely originates from the temporal lobes. Since most patients showed epileptiform discharges with multiple recordings, elevated spike frequency should be considered a marker of hyperexcitability in AD.

Subclinical Epileptiform Activity in Dementia with Lewy Bodies.

BACKGROUND: Patients with dementia with Lewy bodies (DLB) have a higher probability of seizures than in normal aging and in other types of neurodegenerative disorders. Depositions of α-synuclein, a pathological hallmark of DLB, can induce network excitability, which can escalate into seizure activity. Indicator of seizures are epileptiform discharges as observed using electroencephalography (EEG). However, no studies have so far investigated the occurrence of interictal epileptiform discharges (IED) in patients with DLB. OBJECTIVES: To investigate if IED as measured with ear-EEG occurs with a higher frequency in patients with DLB compared to healthy controls (HC). METHODS: In this longitudinal observational exploratory study, 10 patients with DLB and 15 HC were included in the analysis. Patients with DLB underwent up to three ear-EEG recordings, each lasting up to 2 days, over a period of 6 months. RESULTS: At baseline, IED were detected in 80% of patients with DLB and in 46.7% of HC. The spike frequency (spikes or sharp waves/24 hours) was significantly higher in patients with DLB as compared to HC with a risk ratio of 2.52 (CI, 1.42-4.61; P-value = 0.001). Most IED occurred at night. CONCLUSIONS: Long-term outpatient ear-EEG monitoring detects IED in most patients with DLB with an increased spike frequency compared to HC. This study extends the spectrum of neurodegenerative disorders in which epileptiform discharges occurs at an elevated frequency. It is possible that epileptiform discharges are, therefore, a consequence of neurodegeneration. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Progressive slowing of clonic phase predicts postictal generalized EEG suppression.

OBJECTIVE: Postictal generalized electroencephalography (EEG) suppression (PGES) is a surrogate marker of sudden unexpected death in epilepsy (SUDEP). It is still unclear which ictal phenomena lead to prolonged PGES and increased risk of SUDEP. Semiology features of generalized convulsive seizure (GCS type 1) have been reported as a predictor of prolonged PGES. Progressive slowing of clonic phase (PSCP) has been observed in GCSs, with gradually increasing inhibitory periods interrupting the tonic contractions. We hypothesized that PSCP is associated with prolonged PGES. METHODS: We analyzed 90 bilateral convulsive seizures in 50 consecutive patients (21 female; age: 11-62 years, median: 31 years) recruited to video-EEG monitoring. Five raters, blinded to all other data, independently assessed the presence of PSCP. PGES and seizure semiology were evaluated independently. We determined inter-rater agreement (IRA) for the presence of PSCP, and we evaluated its association, as well as that of other ictal features, with the occurrence of PGES, prolonged PGES (≥20 s) and very prolonged PGES (≥50 s) using multivariate logistic regression analysis. RESULTS: We found substantial IRA for the presence of PSCP (percent agreement: 80%; beyond-chance agreement coefficient: .655). PSCP was an independent predictor of the occurrence of PGES and prolonged PGES (p < .001). All seizures with very prolonged PGES had PSCP. GCS type 1 was an independent predictor of occurrence of PGES (p = .02) and prolonged PGES (p = .03) but not of very prolonged PGES. Only half of the seizures with very prolonged PGES were GCS type 1. SIGNIFICANCE: PSCP predicts prolonged PGES, emphasizing the importance of gradually increasing inhibitory phenomena at the end of the seizures. Our findings shed more light on the ictal phenomena leading to increased risk of SUDEP. These phenomena may provide basis for algorithms implemented into wearable devices for identifying GCS with increased risk of SUDEP.

Does continuous electroencephalography influence therapeutic decisions in neurocritical care?

OBJECTIVES: In the neurocritical care unit (neuro-ICU), the impact of continuous EEG (cEEG) on therapeutic decisions and prognostication, including outcome prediction using the Status Epilepticus Severity Score (STESS), is poorly investigated. We studied to what extent cEEG contributes to treatment decisions, and how this relates to clinical outcome and the use of STESS in neurocritical care. METHODS: We included patients admitted to the neuro-ICU or neurological step-down unit of a tertiary referral hospital between 05/2013 and 06/2015. Inclusion criteria were ≥20 h of cEEG monitoring and age ≥15 years. Exclusion criteria were primary epileptic and post-cardiac arrest encephalopathies. RESULTS: Ninety-eight patients met inclusion criteria, 80 of which had status epilepticus, including 14 with super-refractory status. Median length of cEEG monitoring was 50 h (range 21-374 h). Mean STESS was lower in patients with favorable outcome 1 year after discharge (modified Rankin Scale [mRS] 0-2) compared to patients with unfavorable outcome (mRS 3-6), albeit not statistically significant (mean STESS 2.3 ± 2.1 vs 3.6 ± 1.7, p = 0.09). STESS had a sensitivity of 80%, a specificity of 42%, and a negative predictive value of 93% for outcome. cEEG results changed treatment decisions in 76 patients, including escalation of antiepileptic treatment in 65 and reduction in 11 patients. CONCLUSION: Status Epilepticus Severity Score had a high negative predictive value but low sensitivity, suggesting that STESS should be used cautiously. Of note, cEEG results altered clinical decision-making in three of four patients, irrespective of the presence or absence of status epilepticus, confirming the clinical value of cEEG in neurocritical care.

What Should a Clinician Do When Spreading Depolarizations are Observed in a Patient?

The International Conference on Spreading Depolarizations (iCSD) held in Boca Raton, Florida, in the September of 2018 devoted a section to address the question, "What should a clinician do when spreading depolarizations are observed in a patient?" Discussants represented a wide range of expertise, including neurologists, neurointensivists, neuroradiologists, neurosurgeons, and pre-clinical neuroscientists, to provide both clinical and basic pathophysiology perspectives. A draft summary of viewpoints offered was then written by a multidisciplinary writing group of iCSD members, based on a transcript of the session. Feedback of all discussants was formally collated, reviewed, and incorporated into the final document which was subsequently approved by all authors.

Which Spreading Depolarizations Are Deleterious To Brain Tissue?

Spreading depolarizations (SDs) are profound disruptions of cellular homeostasis that slowly propagate through gray matter and present an extraordinary metabolic challenge to brain tissue. Recent work has shown that SDs occur commonly in human patients in the neurointensive care setting and have established a compelling case for their importance in the pathophysiology of acute brain injury. The International Conference on Spreading Depolarizations (iCSD) held in Boca Raton, Florida, in September of 2018 included a discussion session focused on the question of "Which SDs are deleterious to brain tissue?" iCSD is attended by investigators studying various animal species including invertebrates, in vivo and in vitro preparations, diseases of acute brain injury and migraine, computational modeling, and clinical brain injury, among other topics. The discussion included general agreement on many key issues, but also revealed divergent views on some topics that are relevant to the design of clinical interventions targeting SDs. A draft summary of viewpoints offered was then written by a multidisciplinary writing group of iCSD members, based on a transcript of the session. Feedback of all discussants was then formally collated, reviewed and incorporated into the final document. It is hoped that this report will stimulate collection of data that are needed to develop a more nuanced understanding of SD in different pathophysiological states, as the field continues to move toward effective clinical interventions.

Neurostereologic Lesion Volumes and Spreading Depolarizations in Severe Traumatic Brain Injury Patients: A Pilot Study.

BACKGROUND: Spreading depolarizations (SDs) occur in 50-60% of patients after surgical treatment of severe traumatic brain injury (TBI) and are independently associated with unfavorable outcomes. Here we performed a pilot study to examine the relationship between SDs and various types of intracranial lesions, progression of parenchymal damage, and outcomes. METHODS: In a multicenter study, fifty patients (76% male; median age 40) were monitored for SD by continuous electrocorticography (ECoG; median duration 79 h) following surgical treatment of severe TBI. Volumes of hemorrhage and parenchymal damage were estimated using unbiased stereologic assessment of preoperative, postoperative, and post-ECoG serial computed tomography (CT) studies. Neurologic outcomes were assessed at 6 months by the Glasgow Outcome Scale-Extended. RESULTS: Preoperative volumes of subdural and subarachnoid hemorrhage, but not parenchymal damage, were significantly associated with the occurrence of SDs (P's < 0.05). Parenchymal damage increased significantly (median 34 ml [Interquartile range (IQR) - 2, 74]) over 7 (5, 8) days from preoperative to post-ECoG CT studies. Patients with and without SDs did not differ in extent of parenchymal damage increase [47 ml (3, 101) vs. 30 ml (- 2, 50), P = 0.27], but those exhibiting the isoelectric subtype of SDs had greater initial parenchymal damage and greater increases than other patients (P's < 0.05). Patients with temporal clusters of SDs (≥ 3 in 2 h; n = 10 patients), which included those with isoelectric SDs, had worse outcomes than those without clusters (P = 0.03), and parenchymal damage expansion also correlated with worse outcomes (P = 0.01). In multivariate regression with imputation, both clusters and lesion expansion were significant outcome predictors. CONCLUSIONS: These results suggest that subarachnoid and subdural blood are important primary injury factors in provoking SDs and that clustered SDs and parenchymal lesion expansion contribute independently to worse patient outcomes. These results warrant future prospective studies using detailed quantification of TBI lesion types to better understand the relationship between anatomic and physiologic measures of secondary injury.

Functional MRI for Assessment of the Default Mode Network in Acute Brain Injury.

BACKGROUND: Assessment of the default mode network (DMN) using resting-state functional magnetic resonance imaging (fMRI) may improve assessment of the level of consciousness in chronic brain injury, and therefore, fMRI may also have prognostic value in acute brain injury. However, fMRI is much more challenging in critically ill patients because of cardiovascular vulnerability, intravenous sedation, and artificial ventilation. METHODS: Using resting-state fMRI, we investigated the DMN in a convenience sample of patients with acute brain injury admitted to the intensive care unit. The DMN was classified dichotomously into "normal" and "grossly abnormal." Clinical outcome was assessed at 3 months. RESULTS: Seven patients with acute brain injury (4 females; median age 37 years [range 14-71 years]; 1 traumatic brain injury [TBI]; 6 non-TBI) were investigated by fMRI a median of 15 days after injury (range 5-25 days). Neurological presentation included 2 coma, 1 vegetative state/unresponsive wakefulness syndrome (VS/UWS), 3 minimal conscious state (MCS) minus, and 1 MCS plus. Clinical outcomes at 3 months included 1 death, 1 VS/UWS, 1 MCS plus, and 4 conscious states (CS; 1 modified Rankin Scale 0; 2 mRS 4; 1 mRS 5). Normal DMNs were seen in 4 out of 7 patients (1 MCS plus, 3 CS at follow-up). CONCLUSIONS: It is feasible to assess the DMN by resting-state fMRI in patients with acute brain injury already in the very early period of intensive care unit admission. Although preliminary data, all patients with a preserved DMN regained consciousness levels at follow-up compatible with MCS+ or better.

Automated differentiation between epileptic and nonepileptic convulsive seizures.

Our objective was the clinical validation of an automated algorithm based on surface electromyography (EMG) for differentiation between convulsive epileptic and psychogenic nonepileptic seizures (PNESs). Forty-four consecutive episodes with convulsive events were automatically analyzed with the algorithm: 25 generalized tonic-clonic seizures (GTCSs) from 11 patients, and 19 episodes of convulsive PNES from 13 patients. The gold standard was the interpretation of the video-electroencephalographic recordings by experts blinded to the EMG results. The algorithm correctly classified 24 GTCSs (96%) and 18 PNESs (95%). The overall diagnostic accuracy was 95%. This algorithm is useful for distinguishing between epileptic and psychogenic convulsive seizures.

Preserved consciousness in vegetative and minimal conscious states: systematic review and meta-analysis.

Active, passive and resting state paradigms using functional MRI (fMRI) or EEG may reveal consciousness in the vegetative (VS) and the minimal conscious state (MCS). A meta-analysis was performed to assess the prevalence of preserved consciousness in VS and MCS as revealed by fMRI and EEG, including command following (active paradigms), cortical functional connectivity elicited by external stimuli (passive paradigms) and default mode networks (resting state). Studies were selected from multiple indexing databases until February 2015 and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 37 studies were identified, including 1041 patients (mean age 43 years, range 16-89; male/female 2.1:1; 39.5% traumatic brain injuries). MCS patients were more likely than VS patients to follow commands during active paradigms (32% vs 14%; OR 2.85 (95% CI 1.90 to 4.27; p<0.0001)) and to show preserved functional cortical connectivity during passive paradigms (55% vs 26%; OR 3.53 (95% CI 2.49 to 4.99; p<0.0001)). Passive paradigms suggested preserved consciousness more often than active paradigms (38% vs 24%; OR 1.98 (95% CI 1.54 to 2.54; p<0.0001)). Data on resting state paradigms were insufficient for statistical evaluation. In conclusion, active paradigms may underestimate the degree of consciousness as compared to passive paradigms. While MCS patients show signs of preserved consciousness more frequently in both paradigms, roughly 15% of patients with a clinical diagnosis of VS are able to follow commands by modifying their brain activity. However, there remain important limitations at the single-subject level; for example, patients from both categories may show command following despite negative passive paradigms.

Continuous EEG monitoring in aneurysmal subarachnoid hemorrhage: a systematic review.

BACKGROUND: Continuous EEG (cEEG) may allow monitoring of patients with aneurysmal subarachnoid hemorrhage (SAH) for delayed cerebral ischemia (DCI) and seizures, including non-convulsive seizures (NCSz), and non-convulsive status epilepticus (NCSE). We aimed to evaluate: (a) the diagnostic accuracy of cEEG as a confirmatory test, (b) the prognostic value of EEG patterns suggestive of seizures and DCI, and (c) the effectiveness of intensified neuromonitoring using cEEG in terms of improved clinical outcome following SAH. METHODS: A systematic review was performed with eligible studies selected from multiple indexing databases through June 2014. The methodological quality of these studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. RESULTS: Eighteen studies were identified, including cEEG data from 481 patients with aneurysmal SAH. NCSz were diagnosed in 7-18 % of patients; NCSE in 3-13 %. NCSE was associated with increased age (mean age 68 years) and mortality (82-100 %) compared to the entire patient population (53.9 years; mortality 13 %; p values <0.05). DCI was diagnosed in 20-46 % of patients. Quantitative EEG patterns suggestive of DCI included decreased alpha/delta ratio, relative alpha variability, and total power. All studies were subject to a high risk of bias concerning patient selection and cEEG methodology. CONCLUSIONS: cEEG monitoring following SAH detects an increased number of subclinical seizures and may predict DCI many hours in advance. NCSE is associated with high mortality and morbidity, whereas for DCI identified by cEEG this association is less clear. Prospective randomized controlled multicenter trials are needed to evaluate the benefits (or risks) of intensified treatment of seizures and DCI following SAH.

Quantitative analysis of surface electromyography during epileptic and nonepileptic convulsive seizures.

OBJECTIVE: To investigate the characteristics of sustained muscle activation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), as compared to voluntary muscle activation. The main goal was to find surface electromyography (EMG) features that can distinguish between convulsive epileptic seizures and convulsive PNES. METHODS: In this case-control study, surface EMG was recorded from the deltoid muscles during long-term video-electroencephalography (EEG) monitoring in 25 patients and in 21 healthy controls. A total of 46 clinical episodes were recorded: 28 generalized tonic-clonic seizures (GTCS) from 14 patients with epilepsy, and 18 convulsive PNES from 12 patients (one patient had both GTCS and PNES). The healthy controls were simulating GTCS. To quantitatively characterize the signals we calculated the following parameters: root mean square (RMS) of the amplitude, median frequency (MF), coherence, and duration of the seizures, of the clonic EMG discharges, and of the silent periods between the cloni. Based on wavelet analysis, we distinguished between a low-frequency component (LF 2-8 Hz) and a high-frequency component (HF 64-256 Hz). RESULTS: Duration of the seizure, and separation between the tonic and the clonic phases distinguished at group-level but not at individual level between convulsive PNES and GTCS. RMS, temporal dynamics of the HF/LF ratio, and the evolution of the silent periods differentiated between epileptic and nonepileptic convulsive seizures at the individual level. A combination between HF/LF ratio and RMS separated all PNES from the GTCS. A blinded review of the EMG features distinguished correctly between GTCS and convulsive PNES in all cases. The HF/LF ratio and the RMS of the PNES were smaller compared to the simulated seizures. SIGNIFICANCE: In addition to providing insight into the mechanism of muscle activation during convulsive PNES, these results have diagnostic significance, at the individual level. Surface EMG features can accurately distinguish convulsive epileptic from nonepileptic psychogenic seizures, even in PNES cases without rhythmic clonic movements.

Detection of spreading depolarization with intraparenchymal electrodes in the injured human brain.

BACKGROUND: Spreading depolarization events following ischemic and traumatic brain injury are associated with poor patient outcome. Currently, monitoring these events is limited to patients in whom subdural electrodes can be placed at open craniotomy. This study examined whether these events can be detected using intra-cortical electrodes, opening the way for electrode insertion via burr hole. METHODS: Animal work was carried out on adult Sprague-Dawley rats in a laboratory setting to investigate the feasibility of recording depolarization events. Subsequently, 8 human patients requiring craniotomy for traumatic brain injury or aneurysmal subarachnoid hemorrhage were monitored for depolarization events in an intensive care setting with concurrent strip (subdural) and depth (intra-parenchymal) electrode recordings. RESULTS: (1) Depolarization events can be reliably detected from intra-cortically placed electrodes. (2) A reproducible slow potential change (SPC) waveform morphology was identified from intra-cortical electrodes on the depth array. (3) The depression of cortical activity known to follow depolarization events was identified consistently from both intra-cortical and sub-cortical electrodes on the depth array. CONCLUSIONS: Intra-parenchymally sited electrodes can be used to consistently identify depolarization events in humans. This technique greatly extends the capability of monitoring for spreading depolarization events in injured patients, as electrodes can be sited without the need for craniotomy. The method provides a new investigative tool for the evaluation of the contribution of these events to secondary brain injury in human patients.

Prognostic value of the 5-SENSE Score to predict focality of the seizure-onset zone as assessed by stereoelectroencephalography: a prospective international multicentre validation study.

Introduction: Epilepsy surgery is the only curative treatment for patients with drug-resistant focal epilepsy. Stereoelectroencephalography (SEEG) is the gold standard to delineate the seizure-onset zone (SOZ). However, up to 40% of patients are subsequently not operated as no focal non-eloquent SOZ can be identified. The 5-SENSE Score is a 5-point score to predict whether a focal SOZ is likely to be identified by SEEG. This study aims to validate the 5-SENSE Score, improve score performance by incorporating auxiliary diagnostic methods and evaluate its concordance with expert decisions. Methods and analysis: Non-interventional, observational, multicentre, prospective study including 200 patients with drug-resistant epilepsy aged ≥15 years undergoing SEEG for identification of a focal SOZ and 200 controls at 22 epilepsy surgery centres worldwide. The primary objective is to assess the diagnostic accuracy and generalisability of the 5-SENSE in predicting focality in SEEG in a prospective cohort. Secondary objectives are to optimise score performance by incorporating auxiliary diagnostic methods and to analyse concordance of the 5-SENSE Score with the expert decisions made in the multidisciplinary team discussion. Ethics and dissemination: Prospective multicentre validation of the 5-SENSE score may lead to its implementation into clinical practice to assist clinicians in the difficult decision of whether to proceed with implantation. This study will be conducted in accordance with the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (2014). We plan to publish the study results in a peer-reviewed full-length original article and present its findings at scientific conferences. Trial registration number: NCT06138808.

Standardized computer-based organized reporting of EEG: SCORE.

The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan-European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, "episodes" (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make possible the build-up of a multinational database, and it will help in training young neurophysiologists.

Spreading convulsions, spreading depolarization and epileptogenesis in human cerebral cortex.

Spreading depolarization of cells in cerebral grey matter is characterized by massive ion translocation, neuronal swelling and large changes in direct current-coupled voltage recording. The near-complete sustained depolarization above the inactivation threshold for action potential generating channels initiates spreading depression of brain activity. In contrast, epileptic seizures show modest ion translocation and sustained depolarization below the inactivation threshold for action potential generating channels. Such modest sustained depolarization allows synchronous, highly frequent neuronal firing; ictal epileptic field potentials being its electrocorticographic and epileptic seizure its clinical correlate. Nevertheless, Leão in 1944 and Van Harreveld and Stamm in 1953 described in animals that silencing of brain activity induced by spreading depolarization changed during minimal electrical stimulations. Eventually, epileptic field potentials were recorded during the period that had originally seen spreading depression of activity. Such spreading convulsions are characterized by epileptic field potentials on the final shoulder of the large slow potential change of spreading depolarization. We here report on such spreading convulsions in monopolar subdural recordings in 2 of 25 consecutive aneurismal subarachnoid haemorrhage patients in vivo and neocortical slices from 12 patients with intractable temporal lobe epilepsy in vitro. The in vitro results suggest that γ-aminobutyric acid-mediated inhibition protects from spreading convulsions. Moreover, we describe arterial pulse artefacts mimicking epileptic field potentials in three patients with subarachnoid haemorrhage that ride on the slow potential peak. Twenty-one of the 25 subarachnoid haemorrhage patients (84%) had 656 spreading depolarizations in contrast to only three patients (12%) with 55 ictal epileptic events isolated from spreading depolarizations. Spreading depolarization frequency and depression periods per 24 h recording episodes showed an early and a delayed peak on Day 7. Patients surviving subarachnoid haemorrhage with poor outcome at 6 months showed significantly higher total and peak numbers of spreading depolarizations and significantly longer total and peak depression periods during the electrocorticographic monitoring than patients with good outcome. In a semi-structured telephone interview 3 years after the initial haemorrhage, 44% of the subarachnoid haemorrhage survivors had developed late post-haemorrhagic seizures requiring anti-convulsant medication. In those patients, peak spreading depolarization number had been significantly higher [15.1 (11.4-30.8) versus 7.0 (0.8-11.2) events per day, P = 0.045]. In summary, monopolar recordings here provided unequivocal evidence of spreading convulsions in patients. Hence, practically all major pathological cortical network events in animals have now been observed in people. Early spreading depolarizations may indicate a risk for late post-haemorrhagic seizures.

Spreading depolarizations have prolonged direct current shifts and are associated with poor outcome in brain trauma.

Cortical spreading depolarizations occur spontaneously after ischaemic, haemorrhagic and traumatic brain injury. Their effects vary spatially and temporally as graded phenomena, from infarction to complete recovery, and are reflected in the duration of depolarization measured by the negative direct current shift of electrocorticographic recordings. In the focal ischaemic penumbra, peri-infarct depolarizations have prolonged direct current shifts and cause progressive recruitment of the penumbra into the core infarct. In traumatic brain injury, the effects of spreading depolarizations are unknown, although prolonged events have not been observed in animal models. To determine whether detrimental penumbral-type depolarizations occur in human brain trauma, we analysed electrocorticographic recordings obtained by subdural electrode-strip monitoring during intensive care. Of 53 patients studied, 10 exhibited spreading depolarizations in an electrophysiologic penumbra (i.e. isoelectric cortex with no spontaneous activity). All 10 patients (100%) with isoelectric spreading depolarizations had poor outcomes, defined as death, vegetative state, or severe disability at 6 months. In contrast, poor outcomes were observed in 60% of patients (12/20) who had spreading depolarizations with depression of spontaneous activity and only 26% of patients (6/23) who had no depolarizations (χ2, P<0.001). Spontaneous electrocorticographic activity and direct current shifts of depolarizations were further examined in nine patients. Direct current shift durations (n=295) were distributed with a significant positive skew (range 0:51-16:19 min:s), evidencing a normally distributed group of short events and a sub-group of prolonged events. Prolonged direct current shifts were more commonly associated with isoelectric depolarizations (median 2 min 36 s), whereas shorter depolarizations occurred with depression of spontaneous activity (median 2 min 10 s; P<0.001). In the latter group, direct current shift durations correlated with electrocorticographic depression periods, and were longer when preceded by periodic epileptiform discharges than by continuous delta (0.5-4.0 Hz) or higher frequency activity. Prolonged direct current shifts (>3 min) also occurred mainly within temporal clusters of events. Our results show for the first time that spreading depolarizations are associated with worse clinical outcome after traumatic brain injury. Furthermore, based on animal models of brain injury, the prolonged durations of depolarizations raise the possibility that these events may contribute to maturation of cortical lesions. Prolonged depolarizations, measured by negative direct current shifts, were associated with (i) isoelectricity or periodic epileptiform discharges; (ii) prolonged depression of spontaneous activity and (iii) occurrence in temporal clusters. Depolarizations with these characteristics are likely to reflect a worse prognosis.

Learn to interpret voltage maps: an atlas of topographies.

Describing the location of EEG abnormalities, such as interictal epileptiform discharges, is an important step in the interpretation of EEG recordings and has clinical relevance, as it is expected to point out the region of the brain generating these abnormal signals. Traditionally, the location is reported by specifying the area on the scalp where maximum negativity is located. However, this only reflects the correct localization in the brain when the cortical generator is located on the convexity (radial orientation). When the cortical generator is in the wall of a sulcus (tangential orientation), due to current flow (volume conduction), the maximum negativity is not over the generator, but at a distance from it. Voltage maps are widely available in most EEG reader software programs. Simple rules for reading voltage maps help to estimate the orientation and location of the source in the brain, avoiding false lateralization and false localization. In this seminar in epileptology, using a didactic approach, we explain how to read voltage maps and provide an atlas of voltage maps.