Gabriel U. Martz

Medial temporal lobe epilepsy is associated with neuronal fibre loss and paradoxical increase in structural connectivity of limbic structures

Background It has been hypothesised that seizure induced neuronal loss and axonal damage in medial temporal lobe epilepsy (MTLE) may lead to the development of aberrant connections between limbic structures and eventually result in the reorganisation of the limbic network. In this study, limbic structural connectivity in patients with MTLE was investigated, using diffusion tensor MRI, probabilistic tractography and graph theory based network analysis. Methods 12 patients with unilateral MTLE and hippocampal sclerosis (five left and seven right MTLE) and 26 healthy controls were studied. The connectivity of 10 bilateral limbic regions of interest was mapped with probabilistic tractography, and the probabilistic fibre density between each pair of regions was used as the measure of their weighted structural connectivity. Binary connectivity matrices were then obtained from the weighted connectivity matrix using a range of fixed density thresholds. Graph theory based properties of nodes (degree, local efficiency, clustering coefficient and betweenness centrality) and the network (global efficiency and average clustering coefficient) were calculated from the weight and binary connectivity matrices of each subject and compared between patients and controls. Results MTLE was associated with a regional reduction in fibre density compared with controls. Paradoxically, patients exhibited (1) increased limbic network clustering and (2) increased nodal efficiency, degree and clustering coefficient in the ipsilateral insula, superior temporal region and thalamus. There was also a significant reduction in clustering coefficient and efficiency of the ipsilateral hippocampus, accompanied by increased nodal degree. Conclusions These results suggest that MTLE is associated with reorganisation of the limbic system. These results corroborate the concept of MTLE as a network disease, and may contribute to the understanding of network excitability dynamics in epilepsy and MTLE.

Epilepsy beyond seizure: A population-based study of comorbidities

Comorbid conditions may affect the quality of life in persons with epilepsy (PWE) more than seizures. Using legally mandated healthcare encounter data, somatic, psychiatric, and neurodevelopmental comorbidities in a large population-based cohort of PWE, were compared to persons with migraine (PWM), a similar neurologic condition, and lower extremity fracture (PWLF), otherwise healthy controls. 64,188 PWE, 121,990 PWM, and 89,808 PWLF were identified from inpatient, outpatient, and emergency department from 2000 to 2011. Epilepsy was ascertained with ICD-9-CM code 345; migraine with 346; fracture of the tibia, fibula, and ankle with 823 and 824. Common comorbidities of epilepsy were identified from the literature. Differences in prevalence among PWE, PWM, and PWLF were assessed by comparison of 95% confidence intervals (CI) constructed under the assumption of independence and normal approximation. The association of the comorbid conditions with epilepsy and migraine, compared to lower extremity fracture, were evaluated with polytomous logistic regression controlling for demographic and mortality covariables. PWE had significantly elevated prevalence of comorbidities compared with PWM and PWLF. Compared with PWLF, the adjusted odds ratios (OR) of having both somatic and psychiatric/neurodevelopmental comorbidities were 5.44 (95% CI=5.25-5.63) and 2.49 (95% CI=2.42-2.55) in PWE and PWM, respectively. The association with epilepsy was the strongest for cognitive dysfunction (OR=28.1; 95% CI=23.3-33.8); autism spectrum disorders (OR=22.2; 95% CI=16.8-29.3); intellectual disability (OR=12.9; 95% CI=11.6-14.3); and stroke (OR=4.2; 95% CI=4.1-4.4). The absolute risk increase in PWE compared with PWM for any somatic or psychiatric/neurodevelopmental comorbidity was 58.8% and 94.3%, respectively. Identifying comorbidities that are strongly and consistently associated with seizures, particularly disorders with shared underlying pathophysiology, is critical in identifying specific research and practice goals that may ultimately improve the quality of life for PWE. This study contributes to that effort by providing population-based comorbidity data for PWE compared with PWM and PWLF.

Subtypes of medial temporal lobe epilepsy: Influence on temporal lobectomy outcomes?

Surgical resection of the hippocampus is the most successful treatment for medication‐refractory medial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis. Unfortunately, at least one of four operated patients continue to have disabling seizures after surgery, and there is no existing method to predict individual surgical outcome. Prior to surgery, patients who become seizure free appear identical to those who continue to have seizures after surgery. Interestingly, newly converging presurgical data from magnetic resonance imaging (MRI) and intracranial electroencephalography (EEG) suggest that the entorhinal and perirhinal cortices may play an important role in seizure generation. These areas are not consistently resected with surgery and it is possible that they continue to generate seizures after surgery in some patients. Therefore, subtypes of MTLE patients can be considered according to the degree of extrahippocampal damage and epileptogenicity of the medial temporal cortex. The identification of these subtypes has the potential to drastically improve surgical results via optimized presurgical planning. In this review, we discuss the current data that suggests neural network damage in MTLE, focusing on the medial temporal cortex. We explore how this evidence may be applied to presurgical planning and suggest approaches for future investigation.

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.

Inter-rater agreement on identification of electrographic seizures and periodic discharges in ICU EEG recordings

OBJECTIVE This study investigated inter-rater agreement (IRA) among EEG experts for the identification of electrographic seizures and periodic discharges (PDs) in continuous ICU EEG recordings. METHODS Eight board-certified EEG experts independently identified seizures and PDs in thirty 1-h EEG segments which were selected from ICU EEG recordings collected from three medical centers. IRA was compared between seizure and PD identifications, as well as among rater groups that have passed an ICU EEG Certification Test, developed by the Critical Care EEG Monitoring Research Consortium (CCEMRC). RESULTS Both kappa and event-based IRA statistics showed higher mean values in identification of seizures compared to PDs (k=0.58 vs. 0.38; p<0.001). The group of rater pairs who had both passed the ICU EEG Certification Test had a significantly higher mean IRA in comparison to rater pairs in which neither had passed the test. CONCLUSIONS IRA among experts is significantly higher for identification of electrographic seizures compared to PDs. Additional instruction, such as the training module and certification test developed by the CCEMRC, could enhance this IRA. SIGNIFICANCE This study demonstrates more disagreement in the labeling of PDs in comparison to seizures. This may be improved by education about standard EEG nomenclature.

Characteristics of EEG Interpreters Associated with Higher Inter-rater Agreement.

Purpose: The goal of the project is to determine characteristics of academic neurophysiologist EEG interpreters (EEGers), which predict good interrater agreement (IRA) and to determine the number of EEGers needed to develop an ideal standardized testing and training data set for epileptiform transient (ET) detection algorithms. Methods: A three-phase scoring method was used. In phase 1, 19 EEGers marked the location of ETs in two hundred 30-second segments of EEG from 200 different patients. In phase 2, EEG events marked by at least 2 EEGers were annotated by 18 EEGers on a 5-point scale to indicate whether they were ETs. In phase 3, a third opinion was obtained from EEGers on any inconsistencies between phase 1 and phase 2 scoring. Results: The IRA for the 18 EEGers was only fair. A select group of the EEGers had good IRA and the other EEGers had low IRA. Board certification by the American Board of Clinical Neurophysiology was associated with better IRA performance but other board certifications, years of fellowship training, and years of practice were not. As the number of EEGers used for scoring is increased, the amount of change in the consensus opinion decreases steadily and is quite low as the group size approaches 10. Conclusions: The IRA among EEGers varies considerably. The EEGers must be tested before use as scorers for ET annotation research projects. The American Board of Clinical Neurophysiology certification is associated with improved performance. The optimal size for a group of experts scoring ETs in EEG is probably in the 6 to 10 range.

Risk of venous thromboembolism in people with epilepsy

Risk of venous thromboembolism (VTE) among people with epilepsy (PWEs) has not been previously reported. Standard VTE prevention methods may increase the risk of complications in this population. This statewide study assessed the risk of VTE in PWEs.

Relationship of number of seizures recorded on video-EEG to surgical outcome in refractory medial temporal lobe epilepsy

INTRODUCTION Surgical planning for refractory medial temporal lobe epilepsy (rMTLE) relies on seizure localization by ictal electroencephalography (EEG). Multiple factors impact the number of seizures recorded. We evaluated whether seizure freedom correlated to the number of seizures recorded, and the related factors. METHODS We collected data for 32 patients with rMTLE who underwent anterior temporal lobectomy. Primary analysis evaluated number of seizures captured as a predictor of surgical outcome. Subsequent analyses explored factors that may seizure number. RESULTS Number of seizures recorded did not predict seizure freedom. More seizures were recorded with more days of seizure occurrence (p<0.001), seizure clusters (p≤0.011) and poorly localized seizures (PLSz) (p=0.004). Regression modeling showed a trend for subjects with fewer recorded poorly localized seizures to have better surgical outcome (p=0.052). CONCLUSIONS Total number of recorded seizures does not predict surgical outcome. Patients with more PLSz may have worse outcome.

Comparison of a Novel Dry Electrode Headset to Standard Routine EEG in Veterans.

Objective: This purpose of this study was to evaluate the usefulness of a prototype battery-powered dry electrode system (DES) EEG recording headset in Veteran patients by comparing it with standard EEG. Methods: Twenty-one Veterans had both a standard electrode system recording and DES recording in nine different patient states at the same encounter. Setup time, patient comfort, and subject preference were measured. Three experts performed technical quality rating of each EEG recording in a blinded fashion using the web-based EEGnet system. Power spectra were compared between DES and standard electrode system recordings. Results: The average time for DES setup was 5.7 minutes versus 21.1 minutes for standard electrode system. Subjects reported that the DES was more comfortable during setup. Most subjects (15 of 21) preferred the DES. On a five-point scale (1—best quality to 5—worst quality), the technical quality of the standard electrode system recordings was significantly better than for the DES recordings, at 1.25 versus 2.41 (P < 0.0001). But experts found that 87% of the DES EEG segments were of sufficient technical quality to be interpretable. Conclusions: This DES offers quick and easy setup and is well tolerated by subjects. Although the technical quality of DES recordings was less than standard EEG, most of the DES recordings were rated as interpretable by experts. Significance: This DES, if improved, could be useful for a telemedicine approach to outpatient routine EEG recording within the Veterans Administration or other health system.

Display of consistent ictal networks in refractory mesial temporal lobe epilepsy.

OBJECTIVE Exploration of emergent ictal networks was performed in homogeneous subjects with refractory medial temporal lobe epilepsy. METHODS Maximal Synchrony Index (SI) values were calculated for all electrode pairs for each second during 25 seizures and displayed as connectivity animations. Consistent temporal patterns of SI value and spatial connectivity were observed across seizures and subjects, and used to define a sequence of network stages. RESULTS Highest SI values were found in electrodes within the area of surgical resection. Analysis of these electrodes by network stage demonstrated lateral temporal cortex dominance at seizure initiation, giving way to hippocampal synchrony during the major portion of the seizure, with lateral temporal regions re-emerging as the seizure terminated. SI values also corresponded to behavioral severity of seizures, and lower SI values were associated with post-surgical seizure freedom. CONCLUSION SI based methods of network characterization consistently display the intrinsic MTLE ictal network and may be sensitive to clinical features. SIGNIFICANCE Consistency of EEG-derived network patterns is an important step as network features are applied towards improvement of clinical management. These data confirm consistency of network patterns within and across subjects and support the potential for these methods to distinguish relevant clinical variables.