Jonathan J. Halford

Extrahippocampal gray matter loss and hippocampal deafferentation in patients with temporal lobe epilepsy

Purpose:  Medial temporal epilepsy (MTLE) is associated with extrahippocampal brain atrophy. The mechanisms underlying brain damage in MTLE are unknown. Seizures may lead to neuronal damage, but another possible explanation is deafferentation from loss of hippocampal connections. This study aimed to investigate the relationship between hippocampal deafferentation and brain atrophy in MTLE.

Asymmetrical extra-hippocampal grey matter loss related to hippocampal atrophy in patients with medial temporal lobe epilepsy

Background: Structural neuroimaging studies have consistently shown a pattern of extra-hippocampal atrophy in patients with left and right drug-refractory medial temporal lobe epilepsy (MTLE). However, it is not yet completely understood how extra-hippocampal atrophy is related to hippocampal atrophy. Moreover, patients with left MTLE often exhibit more intense cognitive impairment, and subtle brain asymmetries have been reported in patients with left MTLE versus right MTLE but have not been explored in a controlled study. Objectives: To investigate the association between extra-hippocampal and hippocampal atrophy in patients with MTLE, and the effect of side of hippocampal atrophy on extra-hippocampal atrophy. Methods: Voxel-based morphometry analyses of magnetic resonance images of the brain were performed to determine the correlation between regional extra-hippocampal grey matter volume and hippocampal grey matter volume. The results from 36 patients with right and left MTLE were compared, and results from the two groups were compared with those from 49 healthy controls. Results: Compared with controls, patients with MTLE showed a more intense correlation between hippocampal grey matter volume and regional grey matter volume in locations such as the contralateral hippocampus, bilateral parahippocampal gyri and frontal and parietal areas. Compared with right MTLE, patients with left MTLE exhibited a wider area of atrophy related to hippocampal grey matter loss, encompassing both the contralateral and ipsilateral hemispheres, particularly affecting the contralateral hippocampus. Conclusions: Our results suggest that left hippocampal atrophy is associated with a larger degree of extra-hippocampal atrophy. This may help to explain the more intense cognitive impairment usually observed in these patients.

Computerized epileptiform transient detection in the scalp electroencephalogram: Obstacles to progress and the example of computerized ECG interpretation

Computerized detection of epileptiform transients (ETs), also called spikes and sharp waves, in the electroencephalogram (EEG) has been a research goal for the last 40years. A reliable method for detecting ETs could improve efficiency in reviewing long EEG recordings and assist physicians in interpreting routine EEGs. Computer algorithms developed so far for detecting ETs are not as reliable as human expert interpreters, mostly due to the large number of false positive detections. Typical methods for ET detection include measuring waveform morphology, detecting signal non-stationarity, and power spectrum analysis. Some progress has been made by using more advanced algorithmic approaches including wavelet analysis, artificial neural networks, and dipole analysis. Comparing the performance of different algorithms is difficult since each study uses its own EEG test dataset. In order to overcome this problem, European researchers in the field of computerized electrocardiogram interpretation organized a large multi-center research workgroup to create a standardized dataset of ECG recordings which were interpreted by a large group of cardiologists. EEG researchers need to follow this as a model and seek funding for the creation of a standardized EEG research dataset to develop ET detection algorithms and certify commercial software.

ASSESSMENT OF A SCALP EEG-BASED AUTOMATED SEIZURE DETECTION SYSTEM

OBJECTIVE The purpose of this study was to evaluate and validate an offline, automated scalp EEG-based seizure detection system and to compare its performance to commercially available seizure detection software. METHODS The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ∼3653h with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (∼1200h with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persysts Reveal®, version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms. RESULTS The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24h, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24h, respectively. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p<0.05) smaller FDR. CONCLUSIONS The study validates the performance of the IdentEvent™ seizure detection system. SIGNIFICANCE With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.

Clinical Perspectives on Lacosamide

Despite the advent of new antiepileptic drugs (AEDs) over the past 15 years, the treatment of uncontrolled partial-onset seizures remains a dilemma for clinicians. The most recent AEDs offer new mechanisms of action and more favorable safety profiles than the first generation of AEDs. Lacosamide (LCM) is the latest AED awaiting approval by the FDA for adjunctive use in partial-onset seizures. It differs from all other approved AEDs in that it has two novel mechanisms of action and favorable pharmacokinetic and safety profiles. The purposes of this article are to present the significant parameters for its use in clinical practice, by summarizing the preliminary results of phase II and III clinical trials, and to compare its efficacy data with other second-generation AEDs.

Carisbamate as adjunctive treatment of partial onset seizures in adults in two randomized, placebo‐controlled trials

Purpose:  To assess the efficacy, safety, and tolerability of the investigational drug carisbamate as adjunctive treatment for partial‐onset seizures (POS).

Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis

The routine scalp electroencephalogram (rsEEG) is the most common clinical neurophysiology procedure. The most important role of rsEEG is to detect evidence of epilepsy, in the form of epileptiform transients (ETs), also known as spike or sharp wave discharges. Due to the wide variety of morphologies of ETs and their similarity to artifacts and waves that are part of the normal background activity, the task of ET detection is difficult and mistakes are frequently made. The development of reliable computerized detection of ETs in the EEG could assist physicians in interpreting rsEEGs. We report progress in developing a standardized database for testing and training ET detection algorithms. We describe a new version of our EEGnet software system for collecting expert opinion on EEG datasets, a completely web-browser based system. We report results of EEG scoring from a group of 11 board-certified academic clinical neurophysiologists who annotated 30-s excepts from rsEEG recordings from 100 different patients. The scorers had moderate inter-scorer reliability and low to moderate intra-scorer reliability. In order to measure the optimal size of this standardized rsEEG database, we used machine learning models to classify paroxysmal EEG activity in our database into ET and non-ET classes. Based on our results, it appears that our database will need to be larger than its current size. Also, our non-parametric classifier, an artificial neural network, performed better than our parametric Bayesian classifier. Of our feature sets, the wavelet feature set proved most useful for classification.

A randomized, double‐blind, placebo‐controlled study of the efficacy, safety, and tolerability of adjunctive carisbamate treatment in patients with partial‐onset seizures

Purpose:  To assess the efficacy, safety, and tolerability of adjunctive carisbamate treatment at 800 mg/day and 1,200 mg/day in patients with partial‐onset seizures (POS).

Automated MRI analysis for identification of hippocampal atrophy in temporal lobe epilepsy

Background:  Hippocampal sclerosis is frequently associated with hippocampal atrophy (HA), which is often observed on routine magnetic resonance imaging (MRI) of patients with medial temporal lobe epilepsy (MTLE). Manual morphometry of the hippocampus is sensitive to detecting HA, but is time‐consuming and prone to operator error. Automated MRI morphometry has the potential to provide rapid and accurate assistance in the clinical detection of HA.

Sensitivity of quantitative EEG for seizure identification in the intensive care unit

Objective: To evaluate the sensitivity of quantitative EEG (QEEG) for electrographic seizure identification in the intensive care unit (ICU). Methods: Six-hour EEG epochs chosen from 15 patients underwent transformation into QEEG displays. Each epoch was reviewed in 3 formats: raw EEG, QEEG + raw, and QEEG-only. Epochs were also analyzed by a proprietary seizure detection algorithm. Nine neurophysiologists reviewed raw EEGs to identify seizures to serve as the gold standard. Nine other neurophysiologists with experience in QEEG evaluated the epochs in QEEG formats, with and without concomitant raw EEG. Sensitivity and false-positive rates (FPRs) for seizure identification were calculated and median review time assessed. Results: Mean sensitivity for seizure identification ranged from 51% to 67% for QEEG-only and 63%–68% for QEEG + raw. FPRs averaged 1/h for QEEG-only and 0.5/h for QEEG + raw. Mean sensitivity of seizure probability software was 26.2%–26.7%, with FPR of 0.07/h. Epochs with the highest sensitivities contained frequent, intermittent seizures. Lower sensitivities were seen with slow-frequency, low-amplitude seizures and epochs with rhythmic or periodic patterns. Median review times were shorter for QEEG (6 minutes) and QEEG + raw analysis (14.5 minutes) vs raw EEG (19 minutes; p = 0.00003). Conclusions: A panel of QEEG trends can be used by experts to shorten EEG review time for seizure identification with reasonable sensitivity and low FPRs. The prevalence of false detections confirms that raw EEG review must be used in conjunction with QEEG. Studies are needed to identify optimal QEEG trend configurations and the utility of QEEG as a screening tool for non-EEG personnel. Classification of evidence review: This study provides Class II evidence that QEEG + raw interpreted by experts identifies seizures in patients in the ICU with a sensitivity of 63%–68% and FPR of 0.5 seizures per hour.