Faculty Bibliography

Sudden unexpected death in epilepsy is the leading category of epilepsy-related death and the underlying mechanisms are incompletely understood. Risk factors can include a recent history and high frequency of generalized tonic-clonic seizures, which can depress brain activity postictally, impairing respiration, arousal and protective reflexes. Neuropathological findings in sudden unexpected death in epilepsy cases parallel those in other epilepsy patients, with no implication of novel structures or mechanisms in seizure-related deaths. Few large studies have comprehensively reviewed whole brain examination of such patients. We evaluated 92 North American Sudden unexpected death in epilepsy Registry cases with whole brain neuropathological examination by board-certified neuropathologists blinded to the adjudicated cause of death, with an average of 16 brain regions examined per case. The 92 cases included 61 sudden unexpected death in epilepsy (40 definite, 9 definite plus, 6 probable, 6 possible) and 31 people with epilepsy controls who died from other causes. The mean age at death was 34.4 years and 65.2% (60/92) were male. The average age of death was younger for sudden unexpected death in epilepsy cases than for epilepsy controls (30.0 versus 39.6 years; P = 0.006), and there was no difference in sex distribution respectively (67.3% male versus 64.5%, P = 0.8). Among sudden unexpected death in epilepsy cases, earlier age of epilepsy onset positively correlated with a younger age at death (P = 0.0005) and negatively correlated with epilepsy duration (P = 0.001). Neuropathological findings were identified in 83.7% of the cases in our cohort. The most common findings were dentate gyrus dysgenesis (sudden unexpected death in epilepsy 50.9%, epilepsy controls 54.8%) and focal cortical dysplasia (FCD) (sudden unexpected death in epilepsy 41.8%, epilepsy controls 29.0%). The neuropathological findings in sudden unexpected death in epilepsy paralleled those in epilepsy controls, including the frequency of total neuropathological findings as well as the specific findings in the dentate gyrus, findings pertaining to neurodevelopment (e.g. FCD, heterotopias) and findings in the brainstem (e.g. medullary arcuate or olivary dysgenesis). Thus, like prior studies, we found no neuropathological findings that were more common in sudden unexpected death in epilepsy cases. Future neuropathological studies evaluating larger sudden unexpected death in epilepsy and control cohorts would benefit from inclusion of different epilepsy syndromes with detailed phenotypic information, consensus among pathologists particularly for more subjective findings where observations can be inconsistent, and molecular approaches to identify markers of sudden unexpected death in epilepsy risk or pathogenesis.

Rationale: Seizure clusters may be related to Sudden Unexpected Death in Epilepsy (SUDEP). Two or more generalized convulsive seizures (GCS) were captured during video electroencephalography in 7/11 (64%) patients with monitored SUDEP in the MORTEMUS study. It follows that seizure clusters may be associated with epilepsy severity and possibly with SUDEP risk. We aimed to determine if electroclinical seizure features worsen from seizure to seizure within a cluster and possible associations between GCS clusters, markers of seizure severity, and SUDEP risk. Methods: Patients were consecutive, prospectively consented participants with drug-resistant epilepsy from a multi-center study. Seizure clusters were defined as two or more GCS in a 24-h period during the recording of prolonged video-electroencephalography in the Epilepsy monitoring unit (EMU). We measured heart rate variability (HRV), pulse oximetry, plethysmography, postictal generalized electroencephalographic suppression (PGES), and electroencephalography (EEG) recovery duration. A linear mixed effects model was used to study the difference between the first and subsequent seizures, with a level of significance set at p < 0.05. Results: We identified 112 GCS clusters in 105 patients with 285 seizures. GCS lasted on average 48.7 ± 19 s (mean 49, range 2-137). PGES emerged in 184 (64.6%) seizures and postconvulsive central apnea (PCCA) was present in 38 (13.3%) seizures. Changes in seizure features from seizure to seizure such as seizure and convulsive phase durations appeared random. In grouped analysis, some seizure features underwent significant deterioration, whereas others improved. Clonic phase and postconvulsive central apnea (PCCA) were significantly shorter in the fourth seizure compared to the first. By contrast, duration of decerebrate posturing and ictal central apnea were longer. Four SUDEP cases in the cluster cohort were reported on follow-up. Conclusion: Seizure clusters show variable changes from seizure to seizure. Although clusters may reflect epilepsy severity, they alone may be unrelated to SUDEP risk. We suggest a stochastic nature to SUDEP occurrence, where seizure clusters may be more likely to contribute to SUDEP if an underlying progressive tendency toward SUDEP has matured toward a critical SUDEP threshold.

Rationale: Currently, there is some ambiguity over the role of postictal generalized electro-encephalographic suppression (PGES) as a biomarker in sudden unexpected death in epilepsy (SUDEP). Visual analysis of PGES, known to be subjective, may account for this. In this study, we set out to perform an analysis of PGES presence and duration using a validated signal processing tool, specifically to examine the association between PGES and seizure features previously reported to be associated with visually analyzed PGES. Methods: This is a prospective, multicenter epilepsy monitoring study of autonomic and breathing biomarkers of SUDEP in adult patients with intractable epilepsy. We studied videoelectroencephalogram (vEEG) recordings of generalized convulsive seizures (GCS) in a cohort of patients in whom respiratory and vEEG recording were carried out during the evaluation in the epilepsy monitoring unit. A validated automated EEG suppression detection tool was used to determine presence and duration of PGES. Results: We studied 148 GCS in 87 patients. PGES occurred in 106/148 (71.6%) seizures in 70/87 (80.5%) of patients. PGES mean duration was 38.7 ± 23.7 (37; 1-169) seconds. Presence of tonic phase during GCS, including decerebration, decortication and hemi-decerebration, were 8.29 (CI 2.6-26.39, p = 0.0003), 7.17 (CI 1.29-39.76, p = 0.02), and 4.77 (CI 1.25-18.20, p = 0.02) times more likely to have PGES, respectively. In addition, presence of decerebration (p = 0.004) and decortication (p = 0.02), older age (p = 0.009), and hypoxemia duration (p = 0.03) were associated with longer PGES durations. Conclusions: In this study, we confirmed observations made with visual analysis, that presence of tonic phase during GCS, longer hypoxemia, and older age are reliably associated with PGES. We found that of the different types of tonic phase posturing, decerebration has the strongest association with PGES, followed by decortication, followed by hemi-decerebration. This suggests that these factors are likely indicative of seizure severity and may or may not be associated with SUDEP. An automated signal processing tool enables objective metrics, and may resolve apparent ambiguities in the role of PGES in SUDEP and seizure severity studies.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Seizure clusters, an intermediate between single seizure and status epilepticus, are associated with morbidity, impaired quality of life, and premature mortality. The relationship between seizure clusters and sudden unexplained death in epilepsy (SUDEP) is poorly understood. Here, we define seizure clusters; review comorbid psychiatric disorders and memory deficits associated with seizure clusters; and review cases of witnessed SUDEP for which seizure frequency prior to death is available. Patients with a history of seizure clusters have a 2.5 fold increased risk for SUDEP, and one third of patients with monitored in hospital SUDEP experienced a cluster of generalized tonic clonic seizures prior to death. Understanding the effects of seizure frequency and duration on SUDEP risk could yield new insights in SUDEP pathophysiology and new targets for intervention.

The relationship between anatomic and resting state functional connectivity of large-scale brain networks is a major focus of current research. In previous work, we introduced a model based on eigen decomposition of the Laplacian which predicts the functional network from the structural network in healthy brains. In this work, we apply the eigen decomposition model to two types of epilepsy; temporal lobe epilepsy associated with mesial temporal sclerosis, and MRI-normal temporal lobe epilepsy. Our findings show that the eigen relationship between function and structure holds for patients with temporal lobe epilepsy as well as normal individuals. These results suggest that the brain under TLE conditions reconfigures and rewires the fine-scale connectivity (a process which the model parameters are putatively sensitive to), in order to achieve the necessary structure-function relationship.

Children with attention deficit hyperactivity disorder (ADHD) have an increased risk of seizures, and children with epilepsy have an increased prevalence of ADHD. Adults with epilepsy often have varying degrees of attentional dysfunction due to multiple factors, including anti-seizure medications, frequent seizures, interictal discharges, underlying lesions, and psychiatric comorbidities. Currently, there are no approved medications for the treatment of epilepsy-related attentional dysfunction. Methylphenidate (MPH) is a stimulant, FDA-approved for the treatment of ADHD, and often used for ADHD in the setting of pediatric epilepsy. Large database and registry studies indicate safety of MPH in children with ADHD and epilepsy, with no significant effect on seizure frequency. Small single-dose and open-label studies suggest efficacy of MPH in adults with epilepsy-related attention deficits. Methylphenidate represents a possible treatment for attentional dysfunction due to epilepsy, but large, randomized, placebo-controlled, double-blinded studies are needed.

BACKGROUND:Dravet syndrome is a rare, treatment-resistant developmental epileptic encephalopathy characterised by multiple types of frequent, disabling seizures. Fenfluramine has been reported to have antiseizure activity in observational studies of photosensitive epilepsy and Dravet syndrome. The aim of the present study was to assess the efficacy and safety of fenfluramine in patients with Dravet syndrome. METHODS:In this randomised, double-blind, placebo-controlled clinical trial, we enrolled children and young adults with Dravet syndrome. After a 6-week observation period to establish baseline monthly convulsive seizure frequency (MCSF; convulsive seizures were defined as hemiclonic, tonic, clonic, tonic-atonic, generalised tonic-clonic, and focal with clearly observable motor signs), patients were randomly assigned through an interactive web response system in a 1:1:1 ratio to placebo, fenfluramine 0·2 mg/kg per day, or fenfluramine 0·7 mg/kg per day, added to existing antiepileptic agents for 14 weeks. The primary outcome was the change in mean monthly frequency of convulsive seizures during the treatment period compared with baseline in the 0·7 mg/kg per day group versus placebo; 0·2 mg/kg per day versus placebo was assessed as a key secondary outcome. Analysis was by modified intention to treat. Safety analyses included all participants who received at least one dose of study medication. This trial is registered with ClinicalTrials.gov with two identical protocols NCT02682927 and NCT02826863. FINDINGS/RESULTS:Between Jan 15, 2016, and Aug 14, 2017, we assessed 173 patients, of whom 119 patients (mean age 9·0 years, 64 [54%] male) were randomly assigned to receive either fenfluramine 0·2 mg/kg per day (39), fenfluramine 0·7 mg/kg per day (40) or placebo (40). During treatment, the median reduction in seizure frequency was 74·9% in the fenfluramine 0·7 mg/kg group (from median 20·7 seizures per 28 days to 4·7 seizures per 28 days), 42·3% in the fenfluramine 0·2 mg/kg group (from median 17·5 seizures per 28 days to 12·6 per 28 days), and 19·2% in the placebo group (from median 27·3 per 28 days to 22·0 per 28 days). The study met its primary efficacy endpoint, with fenfluramine 0·7 mg/kg per day showing a 62·3% greater reduction in mean MCSF compared with placebo (95% CI 47·7-72·8, p<0·0001); fenfluramine 0·2 mg/kg per day showed a 32·4% reduction in mean MCSF compared with placebo (95% CI 6·2-52·3, p=0·0209). The most common adverse events (occurring in at least 10% of patients and more frequently in the fenfluramine groups) were decreased appetite, diarrhoea, fatigue, lethargy, somnolence, and decreased weight. Echocardiographic examinations revealed valve function within the normal physiological range in all patients during the trial and no signs of pulmonary arterial hypertension. INTERPRETATION/CONCLUSIONS:In Dravet syndrome, fenfluramine provided significantly greater reduction in convulsive seizure frequency compared with placebo and was generally well tolerated, with no observed valvular heart disease or pulmonary arterial hypertension. Fenfluramine could be an important new treatment option for patients with Dravet syndrome. FUNDING/BACKGROUND:Zogenix.

OBJECTIVES/OBJECTIVE:METHODS: We analyzed prospectively collected data in the Original and Offspring FHS cohorts. To determine the risk of developing epilepsy among participants with dementia and the risk of developing dementia among participants with epilepsy we used separate, nested, case-control designs and matched each case to 3 age-, sex- and FHS cohort-matched controls. We used Cox proportional hazards regression analysis, adjusting for sex and age. In secondary analysis, we investigated the role of education level and apolipoprotein ε4 allele status in modifying the association between epilepsy and dementia. RESULTS:= 0.001) compared to controls of the same educational attainment. CONCLUSIONS:There is a bi-directional association between epilepsy and dementia with either condition carrying a nearly 2-fold risk of developing the other when compared to controls.

OBJECTIVE:The efficacy of cannabidiol (CBD) with and without concomitant clobazam (CLB) was evaluated in stratified analyses of four large randomized controlled trials, two in Lennox-Gastaut syndrome and two in Dravet syndrome. METHODS:Each trial of CBD (Epidiolex® in the US; Epidyolex® in the EU; 10 and 20 mg/kg/day) was evaluated by CLB use. The treatment ratio was analyzed using negative binomial regression for changes in seizure frequency and logistic regression for the 50% responder rate, where the principle analysis combined both indications and CBD doses in a stratified meta-analysis. Pharmacokinetic data were examined for an exposure/response relationship based on CLB presence/absence. Safety data were analyzed using descriptive statistics. RESULTS:The meta-analysis favored CBD vs. placebo regardless of CLB use. The treatment ratio (95% CI) of CBD over placebo for the average reduction in seizure frequency was 0.59 (0.52, 0.68; p<0.0001) with CLB and 0.85 (0.73, 0.98; p=0.0226) without CLB, and the 50% responder rate odds ratio (95% CI) was 2.51 (1.69, 3.71; p<0.0001) with CLB and 2.40 (1.38, 4.16; p=0.0020) without CLB. Adverse events (AEs) related to somnolence, rash, pneumonia, or aggression were more common in patients with concomitant CLB. There was a significant exposure/response relationship for CBD and its active metabolite. CONCLUSIONS:These results indicate CBD is efficacious with and without CLB, but do not exclude the possibility of a synergistic effect associated with the combination of agents. The safety and tolerability profile of CBD without CLB shows a lower rate of certain AEs than with CLB.