Faculty Bibliography

Epilepsy is a common neurological disorder affecting over 70 million people worldwide, with a high rate of pharmaco-resistance, diverse comorbidities including progressive cognitive and behavioural disorders, and increased mortality from direct (e.g. sudden unexpected death in epilepsy, accidents, drowning) or indirect effects of seizures and therapies. Extensive research with animal models and human studies provides limited insights into the mechanisms underlying seizures and epileptogenesis, and these have not translated into significant reductions in pharmaco-resistance, morbidities or mortality. To help define changes in molecular signalling networks associated with seizures in epilepsy with a broad range of aetiologies, we examined the proteome of brain samples from epilepsy and control cases. Label-free quantitative mass spectrometry was performed on the hippocampal cornu ammonis 1-3 region (CA1-3), frontal cortex and dentate gyrus microdissected from epilepsy and control cases (n = 14/group). Epilepsy cases had significant differences in the expression of 777 proteins in the hippocampal CA1 - 3 region, 296 proteins in the frontal cortex and 49 proteins in the dentate gyrus in comparison to control cases. Network analysis showed that proteins involved in protein synthesis, mitochondrial function, G-protein signalling and synaptic plasticity were particularly altered in epilepsy. While protein differences were most pronounced in the hippocampus, similar changes were observed in other brain regions indicating broad proteomic abnormalities in epilepsy. Among the most significantly altered proteins, G-protein subunit beta 1 (GNB1) was one of the most significantly decreased proteins in epilepsy in all regions studied, highlighting the importance of G-protein subunit signalling and G-protein-coupled receptors in epilepsy. Our results provide insights into common molecular mechanisms underlying epilepsy across various aetiologies, which may allow for novel targeted therapeutic strategies.

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.

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:To test the hypothesis that people with focal epilepsy experience diagnostic delays that may be associated with preventable morbidity, particularly when seizures have only nonmotor symptoms, we compared time to diagnosis, injuries, and motor vehicle accidents (MVAs) in people with focal nonmotor versus focal seizures with motor involvement at epilepsy onset. METHODS:This retrospective study analyzed the enrollment data from the Human Epilepsy Project, which enrolled participants between 2012 and 2017 across 34 sites in the USA, Canada, Europe, and Australia, within 4 months of treatment for focal epilepsy. A total of 447 participants were grouped by initial seizure semiology (focal nonmotor or focal with motor involvement) to compare time to diagnosis and prediagnostic injuries including MVAs. RESULTS:Demographic characteristics were similar between groups. There were 246 participants (55%) with nonmotor seizures and 201 participants (45%) with motor seizures at epilepsy onset. Median time to diagnosis from first seizure was 10 times longer in patients with nonmotor seizures compared to motor seizures at onset (P < .001). The number and severity of injuries were similar between groups. However, 82.6% of MVAs occurred in patients with undiagnosed nonmotor seizures. SIGNIFICANCE/CONCLUSIONS:This study identifies reasons for delayed diagnosis and consequences of delay in patients with new onset focal epilepsy, highlighting a treatment gap that is particularly significant in patients who experience nonmotor seizures at epilepsy onset.

We compared sudden unexpected death in epilepsy (SUDEP) diagnosis rates between North American SUDEP Registry (NASR) epileptologists and original death investigators, to determine degree and causes of discordance. In 220 SUDEP cases with post-mortem examination, we recorded the epileptologist adjudications and medical examiner- and coroner- (ME/C) listed causes of death (CODs). COD diagnosis concordance decreased with NASR's uncertainty in the SUDEP diagnosis: highest for Definite SUDEP (84%, n = 158), lower in Definite Plus (50%, n = 36), and lowest in Possible (0%, n = 18). Rates of psychiatric comorbidity, substance abuse, and toxicology findings for drugs of abuse were all higher in discordant cases than concordant cases. Possible SUDEP cases, an understudied group, were significantly older, and had higher rates of cardiac, drug, or toxicology findings than more certain SUDEP cases. With a potentially contributing or competing COD, ME/Cs favored non-epilepsy-related diagnoses, suggesting a bias toward listing CODs with structural or toxicological findings; SUDEP has no pathognomonic features. A history of epilepsy should always be listed on death certificates and autopsy reports. Even without an alternate COD, ME/Cs infrequently classified COD as "SUDEP." Improved collaboration and communication between epilepsy and ME/C communities improve diagnostic accuracy, as well as bereavement and research opportunities.

Importance/UNASSIGNED:The true incidence of sudden unexplained death in childhood (SUDC), already the fifth leading category of death among toddlers by current US Centers for Disease Control and Prevention estimates, is potentially veiled by the varied certification processes by medicolegal investigative offices across the United States. Objective/UNASSIGNED:To evaluate the frequency of SUDC incidence, understand its epidemiology, and assess the consistency of death certification among medical examiner and coroner offices in the US death investigation system. Design, Setting, and Participants/UNASSIGNED:In this case series, 2 of 13 forensic pathologists (FPs) conducted masked reviews of 100 cases enrolled in the SUDC Registry and Research Collaborative (SUDCRRC). Children who died aged 11 months to 18 years from 36 US states, Canada, and the United Kingdom had been posthumously enrolled in the SUDCRRC by family members from 2014 to 2017. Comprehensive data from medicolegal investigative offices, clinical offices, and family members were reviewed. Data analysis was conducted from December 2014 to June 2020. Main Outcomes and Measures/UNASSIGNED:Certified cause of death (COD) characterized as explained (accidental or natural) or unexplained, as determined by SUDCRRC masked review process. Results/UNASSIGNED:In this study of 100 cases of SUDC (mean [SD] age, 32.1 [31.8] months; 58 [58.0%] boys; 82 [82.0%] White children; 92 [92.0%] from the United States), the original pathologist certified 43 cases (43.0%) as explained COD and 57 (57.0%) as unexplained COD. The SUDCRRC review process led to the following certifications: 16 (16.0%) were explained, 7 (7.0%) were undetermined because of insufficient data, and 77 (77.0%) were unexplained. Experts disagreed with the original COD in 40 cases (40.0%). These data suggest that SUDC incidence is higher than the current Centers for Disease Control and Prevention estimate (ie, 392 deaths in 2018). Conclusions and Relevance/UNASSIGNED:To our knowledge, this is the first comprehensive masked forensic pathology review process of sudden unexpected pediatric deaths, and it suggests that SUDC may often go unrecognized in US death investigations. Some unexpected pediatric deaths may be erroneously attributed to a natural or accidental COD, negatively affecting surveillance, research, public health funding, and medical care of surviving family members. To further address the challenges of accurate and consistent death certification in SUDC, future studies are warranted.

OBJECTIVE:There is evidence for central nervous system complications of coronavirus disease 2019 (COVID-19) infection, including encephalopathy. Encephalopathy caused by or arising from seizures, especially nonconvulsive seizures (NCS), often requires electroencephalography (EEG) monitoring for diagnosis. The prevalence of seizures and other EEG abnormalities among COVID-19-infected patients is unknown. METHODS:Medical records and EEG studies of patients hospitalized with confirmed COVID-19 infections over a 2-month period at a single US academic health system (four hospitals) were reviewed to describe the distribution of EEG findings including epileptiform abnormalities (seizures, periodic discharges, or nonperiodic epileptiform discharges). Factors including demographics, remote and acute brain injury, prior history of epilepsy, preceding seizures, critical illness severity scores, and interleukin 6 (IL-6) levels were compared to EEG findings to identify predictors of epileptiform EEG abnormalities. RESULTS:Of 111 patients monitored, most were male (71%), middle-aged or older (median age 64 years), admitted to an intensive care unit (ICU; 77%), and comatose (70%). Excluding 11 patients monitored after cardiac arrest, the most frequent EEG finding was moderate generalized slowing (57%), but epileptiform findings were observed in 30% and seizures in 7% (4% with NCS). Three patients with EEG seizures did not have epilepsy or evidence of acute or remote brain injury, although all had clinical seizures prior to EEG. Only having epilepsy (odds ratio [OR] 5.4, 95% confidence interval [CI] 1.4-21) or seizure(s) prior to EEG (OR 4.8, 95% CI 1.7-13) was independently associated with epileptiform EEG findings. SIGNIFICANCE/CONCLUSIONS:Our study supports growing evidence that COVID-19 can affect the central nervous system, although seizures are unlikely a common cause of encephalopathy. Seizures and epileptiform activity on EEG occurred infrequently, and having a history of epilepsy or seizure(s) prior to EEG testing was predictive of epileptiform findings. This has important implications for triaging EEG testing in this population.

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.