Electrical cortical stimulation can impair production of the alphabet without impairing counting

Neurosurgery has the potential to cure patients with drug-resistant focal epilepsy, but carries the risk of permanent language impairment when surgery involves the dominant hemisphere of the brain. This risk can be estimated and minimized using electrical stimulation mapping (ESM), which uses cognitive and linguistic tasks during cortical ESM to differentiate "eloquent" and "resectable" areas in the brain. One such task, counting, is often used to screen and characterize language during ESM in patients whose language abilities are limited. Here we report a patient with drug-resistant epilepsy arising from the language-dominant hemisphere using fMRI. Our patient experienced loss of the ability to recite or write the alphabet, but not to count, during ESM of the dominant left posterior superior temporal gyrus. This selective impairment extended to both spoken and written production. We suggest the need for caution when using counting as a sole means to screen language function and as a method of testing low functioning patients using ESM

Substantial and sustained seizure reduction with ketogenic diet in a patient with Ohtahara syndrome

Ketogenic diet has been shown to be efficacious in some epileptic encephalopathies but rarely reported as being useful in children with Ohtahara syndrome. This could possibly be attributed to the rarity of the disease and associated short survival period. We report on a 5-year-old child with Ohtahara syndrome, whose seizures failed to improve with all known medications, continued to show persistent suppression-burst pattern on the electroencephalography (EEG) and had substantial reduction in seizure frequency for one year post-initiation of ketogenic diet. He has not had a single visit to the emergency room because of seizures in the last one year, and more importantly, there has been a clear improvement noted in his level of interaction and temperament. Patients with Ohtahara syndrome invariably have medically intractable seizures and catastrophic neurodevelopmental outcome. Ketogenic diet is a treatment modality that might be worth considering even in this group of patients

Electro-clinical characteristics and prognostic significance of post anoxic myoclonus

Objective: To systematically examine the electro-clinical characteristics of post anoxic myoclonus (PAM) and their prognostic implications in comatose cardiac arrest (CA) survivors. Methods: Fifty-nine CA survivors who developed myoclonus within 72 h of arrest and underwent continuous EEG monitoring were included in the study. Retrospective chart review was performed for all relevant clinical variables including time of PAM onset (“early onset” when within 24 h) and semiology (multi-focal, facial/ocular, whole body and limbs only). EEG findings including background, reactivity, epileptiform patterns and EEG correlate to myoclonus were reviewed at 6, 12, 24, 48 and 72 h after the return of spontaneous circulation (ROSC). Outcome was categorized as either with recovery of consciousness (Cerebral Performance Category (CPC) 1–3) or without recovery of consciousness (CPC 4–5) at the time of discharge. Results: Seven of the 59 patients (11.9%) regained consciousness, including 6/51 (11.8%) with early onset PAM. Patients with recovery of consciousness had shorter time to ROSC, and were more likely to have preserved brainstem reflexes and normal voltage background at all times. No patient with suppression burst or low voltage background (N = 52) at any point regained consciousness. In the subset where precise electro-clinical correlation was possible, all (5/5) those with recovery of consciousness had multi-focal myoclonus and most (4/5) had midline-maximal spikes over a continuous background. No patient with any other semiology (N = 21) regained consciousness. Conclusions: Early onset PAM is not always associated with lack of recovery of consciousness. EEG can help discriminate between patients who may or may not regain consciousness by the time of hospital discharge.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Cyclic seizures in critically ill patients: Clinical correlates, DC recordings and outcomes

Objective To describe EEG and clinical correlates, DC recordings and prognostic significance of cyclic seizures (CS). Methods We reviewed our prospective continuous EEG database to identify patients with CS, controls with non-cyclic status epilepticus (SE) and controls without seizure matched for age and etiology. EEG was reviewed with DC settings. Results 39/260 (15%) patients with electrographic seizures presented with CS. These patients were older (62 vs. 54 years; p = 0.04) and more often had acute or progressive brain injury (77% vs. 52%; p = 0.03) than patients with non-cyclic SE and had a lower level of consciousness, were more severely ill, than matched controls. CS almost always had focal onset, often from posterior regions. Patients with CS trended towards worse prognosis. When available (12 patients), DC recordings showed an infraslow cyclic oscillation of EEG baseline synchronized to the seizures in all cases. Conclusions CS occur mostly in older patients with acute or progressive brain injury, are more likely to be associated with poor outcome than patients with other forms of nonconvulsive SE, and are accompanied by synchronous oscillations of the EEG baseline on DC recordings. Significance CS are a common form of non-convulsive status epilepticus in critically ill patients and provide further insights into the relationship between infraslow activity and seizures; further study on this relationship may shed light on the mechanisms of seizure initiation and termination.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Factors Predicting Outcome after Intracranial EEG Evaluation in Patients with Medically Refractory Epilepsy

Background and ObjectivesThe aim of this study was to identify predictors of a resective surgery and subsequent seizure freedom following intracranial EEG (ICEEG) for seizure-onset localization.MethodsThis is a retrospective chart review of 178 consecutive patients with medically refractory epilepsy who underwent ICEEG monitoring from 2002 to 2015. Univariable and multivariable regression analysis identified independent predictors of resection vs other options. Stepwise Akaike information criteria with the aid of clinical consideration were used to select the best multivariable model for predicting resection and outcome. Discrete time survival analysis was used to analyze the factors predicting seizure-free outcome. Cumulative probability of seizure freedom was analyzed using Kaplan-Meier curves and compared between resection and nonresection groups. Additional univariate analysis was performed on 8 select clinical scenarios commonly encountered during epilepsy surgical evaluations.ResultsMultivariable analysis identified the presence of a lesional MRI, presurgical hypothesis suggesting temporal lobe onset, and a nondominant hemisphere implant as independent predictors of resection (p < 0.0001, area under the receiver operating characteristic curve 0.80, 95% CI 0.73-0.87). Focal ICEEG onset and undergoing a resective surgery predicted absolute seizure freedom at the 5-year follow-up. Patients who underwent resective surgery were more likely to be seizure-free at 5 years compared with continued medical treatment or neuromodulation (60% vs 7%; p < 0.0001, hazard ratio 0.16, 95% CI 0.09-0.28). Even patients thought to have unfavorable predictors (nonlesional MRI or extratemporal lobe hypothesis or dominant hemisphere implant) had ≥50% chance of seizure freedom at 5 years if they underwent resection.DiscussionUnfavorable predictors, including having nonlesional extratemporal epilepsy, should not deter a thorough presurgical evaluation, including with invasive recordings in many cases. Resective surgery without functional impairment offers the best chance for sustained seizure freedom and should always be considered first.Classification of EvidenceThis study provides Class II evidence that the presence of a lesional MRI, presurgical hypothesis suggesting temporal lobe onset, and a nondominant hemisphere implant are independent predictors of resection. Focal ICEEG onset and undergoing resection are independent predictors of 5-year seizure freedom.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Prognostication of post-cardiac arrest coma: early clinical and electroencephalographic predictors of outcome

Purpose: To determine the temporal evolution, clinical correlates, and prognostic significance of electroencephalographic (EEG) patterns in post-cardiac arrest comatose patients treated with hypothermia. Methods: Prospective cohort study of consecutive post-anoxic patients receiving hypothermia and continuous EEG monitoring between May 2011 and June 2014 (n = 100). In addition to clinical variables, 5-min EEG clips at 6, 12, 24, 48, and 72 h after return of spontaneous circulation (ROSC) were reviewed. EEG background was classified according to the American Clinical Neurophysiological Society critical care EEG terminology. Clinical outcome at discharge was dichotomized as good [Glasgow outcome scale (GOS) 4–5, low to moderate disability] vs. poor (GOS 1–3, severe disability to death). Results: Non-ventricular fibrillation/tachycardia arrest, longer time to ROSC, absence of brainstem reflexes, extensor or no motor response, lower pH, higher lactate, hypotension requiring >2 vasopressors, and absence of reactivity on EEG were all associated with poor outcome (all p values ≤0.01). Suppression-burst at any time indicated a poor prognosis, with a 0 % false positive rate (FPR) [95 % confidence interval (CI) 0–10 %]. All patients (54/54) with suppression-burst or a low voltage (70 % for good outcome. Conclusions: Suppression-burst or a low voltage at 24 h after ROSC was not compatible with good outcome in this series. Normal background voltage without epileptiform discharges predicted a good outcome.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

MRI–EEG correlation for outcome prediction in postanoxic myoclonus : A multicenter study

To examine the prognostic ability of the combination of EEG and MRI in identifying patients with good outcome in postanoxic myoclonus (PAM) after cardiac arrest (CA). Adults with PAM who had an MRI within 20 days after CA were identified in 4 prospective CA registries. The primary outcome measure was coma recovery to command following by hospital discharge. Clinical examination included brainstem reflexes and motor activity. EEG was assessed for best background continuity, reactivity, presence of epileptiform activity, and burst suppression with identical bursts (BSIB). MRI was examined for presence of diffusion restriction or fluid-attenuated inversion recovery changes consistent with anoxic brain injury. A prediction model was developed using optimal combination of variables. Among 78 patients, 11 (14.1%) recovered at discharge and 6 (7.7%) had good outcome (Cerebral Performance Category &lt; 3) at 3 months. Patients who followed commands were more likely to have pupillary and corneal reflexes, flexion or better motor response, EEG continuity and reactivity, no BSIB, and no anoxic injury on MRI. The combined EEG/MRI variable of continuous background and no anoxic changes on MRI was associated with coma recovery at hospital discharge with sensitivity 91% (95% confidence interval [CI], 0.59-1.00), specificity 99% (95% CI, 0.92-1.00), positive predictive value 91% (95% CI, 0.59-1.00), and negative predictive value 99% (95% CI, 0.92-1.00). EEG and MRI are complementary and identify both good and poor outcome in patients with PAM with high accuracy. An MRI should be considered in patients with myoclonus showing continuous or reactive EEGs

Predicting Neurological Outcome from Electroencephalogram Dynamics in Comatose Patients after Cardiac Arrest with Deep Learning

Objective: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. Methods: We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. Results: The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). Conclusions and Significance: These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest

Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy

OBJECTIVES: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions. DESIGN: Retrospective. SETTING: ICUs at four academic medical centers in the United States. PATIENTS: Comatose patients with acute hypoxic-ischemic encephalopathy.None. MEASUREMENTS AND MAIN RESULTS: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p \u3c 0.05) and a random forest approach (0.74 ± 0.13; p \u3c 0.05). The time-sensitive model was also the best-calibrated. CONCLUSIONS: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance

Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy*

OBJECTIVES: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions. DESIGN: Retrospective. SETTING: ICUs at four academic medical centers in the United States. PATIENTS: Comatose patients with acute hypoxic-ischemic encephalopathy.None. MEASUREMENTS AND MAIN RESULTS: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated. CONCLUSIONS: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance