Faculty Bibliography

We studied mortality in tuberous sclerosis complex (TSC) by analyzing data from the Tuberous Sclerosis Alliance Natural History Database of 2233 patients from 18 United States TSC centers. Among 31 decedents with data; mean age of death was 29 years. Cause of death could be determined in 26 cases: 11 definitely related to TSC, 14 possibly related to TSC, and 1 unrelated to TSC. Causes of death included SUDEP in 11 (35.5%; Definite (5), Probable (4), Possible (2)), respiratory conditions in 6 (23.1%; lymphangiomyelomatosis in one), tumors in 3 (11.5%), suicide in 2 (7.7%), cardiopulmonary in 2 (7.7%), shunt malfunction in one, and drowning in one. For SUDEP cases, mean age of epilepsy onset was 7 months and 10/11 were treated with multiple anti-seizure medications (ASMs) at death; 7 had intractable epilepsy and 3 were controlled with ASMs. Patients with TSC and their families should be counseled about ASM adherence and lifestyle factors, and the potential role of nocturnal supervision or seizure detection devices to prevent SUDEP.

OBJECTIVE:To develop and validate a tool for individualised prediction of Sudden Unexpected Death in Epilepsy (SUDEP) risk, we re-analysed data from one cohort and three case-control studies undertaken 1980-2005. METHODS:We entered 1273 epilepsy cases (287 SUDEP, 986 controls) and 22 clinical predictor variables into a Bayesian logistic regression model. RESULTS:Cross-validated individualized model predictions were superior to baseline models developed from only average population risk or from generalised tonic-clonic seizure frequency (pairwise difference in leave-one-subject-out expected log posterior density = 35.9, SEM +/-12.5, and 22.9, SEM +/-11.0 respectively). The mean cross-validated (95% Credibility Interval) Area Under the Receiver Operating Curve was 0.71 (0.68 to 0.74) for our model versus 0.38 (0.33 to 0.42) and 0.63 (0.59 to 0.67) for the baseline average and generalised tonic-clonic seizure frequency models respectively. Model performance was weaker when applied to non-represented populations. Prognostic factors included generalized tonic-clonic and focal-onset seizure frequency, alcohol excess, younger age of epilepsy onset and family history of epilepsy. Anti-seizure medication adherence was associated with lower risk. CONCLUSIONS:Even when generalised to unseen data, model predictions are more accurate than population-based estimates of SUDEP. Our tool can enable risk-based stratification for biomarker discovery and interventional trials. With further validation in unrepresented populations it may be suitable for routine individualized clinical decision-making. Clinicians should consider assessment of multiple risk factors, and not only focus on the frequency of convulsions.

OBJECTIVE:To identify the molecular signaling pathways underlying sudden unexpected death in epilepsy (SUDEP) and high-risk SUDEP compared to epilepsy control patients. METHODS:For proteomics analyses, we evaluated the hippocampus and frontal cortex from microdissected post-mortem brain tissue of 12 SUDEP and 14 non-SUDEP epilepsy patients. For transcriptomics analyses, we evaluated hippocampus and temporal cortex surgical brain tissue from mesial temporal lobe epilepsy (MTLE) patients: 6 low-risk and 8 high-risk SUDEP as determined by a short (< 50 seconds) or prolonged (≥ 50 seconds) postictal generalized EEG suppression (PGES) that may indicate severely depressed brain activity impairing respiration, arousal, and protective reflexes. RESULTS:In autopsy hippocampus and cortex, we observed no proteomic differences between SUDEP and non-SUDEP epilepsy patients, contrasting with our previously reported robust differences between epilepsy and non-epilepsy control patients. Transcriptomics in hippocampus and cortex from surgical epilepsy patients segregated by PGES identified 55 differentially expressed genes (37 protein-coding, 15 lncRNAs, three pending) in hippocampus. CONCLUSION/CONCLUSIONS:The SUDEP proteome and high-risk SUDEP transcriptome were similar to other epilepsy patients in hippocampus and frontal cortex, consistent with diverse epilepsy syndromes and comorbidities associated with SUDEP. Studies with larger cohorts and different epilepsy syndromes, as well as additional anatomic regions may identify molecular mechanisms of SUDEP.

Image labeling using convolutional neural networks (CNNs) are a template-free alternative to traditional morphometric techniques. We trained a 3D deep CNN to label the hippocampus and amygdala on whole brain 700 μm isotropic 3D MP2RAGE MRI acquired at 7T. Manual labels of the hippocampus and amygdala were used to (i) train the predictive model and (ii) evaluate performance of the model when applied to new scans. Healthy controls and individuals with epilepsy were included in our analyses. Twenty-one healthy controls and sixteen individuals with epilepsy were included in the study. We utilized the recently developed DeepMedic software to train a CNN to label the hippocampus and amygdala based on manual labels. Performance was evaluated by measuring the dice similarity coefficient (DSC) between CNN-based and manual labels. A leave-one-out cross validation scheme was used. CNN-based and manual volume estimates were compared for the left and right hippocampus and amygdala in healthy controls and epilepsy cases. The CNN-based technique successfully labeled the hippocampus and amygdala in all cases. Mean DSC = 0.88 ± 0.03 for the hippocampus and 0.8 ± 0.06 for the amygdala. CNN-based labeling was independent of epilepsy diagnosis in our sample (p = .91). CNN-based volume estimates were highly correlated with manual volume estimates in epilepsy cases and controls. CNNs can label the hippocampus and amygdala on native sub-mm resolution MP2RAGE 7T MRI. Our findings suggest deep learning techniques can advance development of morphometric analysis techniques for high field strength, high spatial resolution brain MRI.

Objective: We sought to identify novel genes and to establish the contribution of known genes in a large cohort of patients with nonsyndromic sporadic polymicrogyria and epilepsy.
Method(s): We enrolled participants with polymicrogyria and their parents through the Epilepsy Phenome/Genome Project. We performed phenotyping and whole exome sequencing (WES), trio analysis, and gene-level collapsing analysis to identify de novo or inherited variants, including germline or mosaic (postzygotic) single nucleotide variants, small insertion-deletion (indel) variants, and copy number variants present in leukocyte-derived DNA.
Result(s): Across the cohort of 86 individuals with polymicrogyria and epilepsy, we identified seven with pathogenic or likely pathogenic variants in PIK3R2, including four germline and three mosaic variants. PIK3R2 was the only gene harboring more than expected de novo variants across the entire cohort, and likewise the only gene that passed the genome-wide threshold of significance in the gene-level rare variant collapsing analysis. Consistent with previous reports, the PIK3R2 phenotype consisted of bilateral polymicrogyria concentrated in the perisylvian region with macrocephaly. Beyond PIK3R2, we also identified one case each with likely causal de novo variants in CCND2 and DYNC1H1 and biallelic variants in WDR62, all genes previously associated with polymicrogyria. Candidate genetic explanations in this cohort included single nucleotide de novo variants in other epilepsy-associated and neurodevelopmental disease-associated genes (SCN2A in two individuals, GRIA3, CACNA1C) and a 597-kb deletion at 15q25, a neurodevelopmental disease susceptibility locus.
Significance: This study confirms germline and postzygotically acquired de novo variants in PIK3R2 as an important cause of bilateral perisylvian polymicrogyria, notably with macrocephaly. In total, trio-based WES identified a genetic diagnosis in 12% and a candidate diagnosis in 6% of our polymicrogyria cohort. Our results suggest possible roles for SCN2A, GRIA3, CACNA1C, and 15q25 deletion in polymicrogyria, each already associated with epilepsy or other neurodevelopmental conditions without brain malformations. The role of these genes in polymicrogyria will be further understood as more patients with polymicrogyria undergo genetic evaluation.
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Epilepsy is characterized by an imbalance in neurotransmitter activity; an increased excitatory to an inhibitory activity. Acetylcholine (ACh), serotonin, and norepinephrine (NE) may modulate neural activity via several mechanisms, mainly through its receptors/transporter activity and alterations in the extracellular potassium (K⁺) concentration via K⁺ ion channels. Seizures may disrupt the regulation of inwardly rectifying K⁺ (Kir) channels and alter the receptor/transporter activity. However, there are limited data present on the immunoreactivity pattern of these neurotransmitter receptors/transporters and K⁺ channels in chronic models of epilepsy, which therefore was the aim of this study. Changes in the immunoreactivity of epileptogenesis-related neurotransmitter receptors/transporters (M2, 5-HT2B, and NE transporter) as well as Kir channels (Kir3.1 and Kir6.2) were determined in the cortex, hippocampus and medulla of adult Wistar rats by utilizing a Pentylenetetrazol (PTZ)-kindling chronic epilepsy model. Increased immunoreactivity of the NE transporter, M2, and 5-HT2B receptors was witnessed in the cortex and medulla. While the immunoreactivity of the 5-HT2B receptor was found increased in the cortex and medulla, it was decreased in the hippocampus, with no changes observed in the M2 receptor in this region. Kir3.1 and Kir6.2 staining showed increase immunoreactivity in the cerebral cortex, but channel contrasting findings in the hippocampus and medulla. Our results suggest that seizure kindling may result in significant changes in the neurotransmitter system which may contribute or propagate to future epileptogenesis, brain damage and potentially towards sudden unexpected death in epilepsy (SUDEP). Further studies on the pathogenic role of these changes in neurotransmitter receptors/transporters and K⁺ channel immunoreactivity may identify newer possible targets to treat seizures or prevent epilepsy-related comorbidities.

OBJECTIVE:People with epilepsy experience increased rates of sexual dysfunction, often affecting quality of life. Sexual dysfunction may result from the underlying disorder, antiseizure or other medications, or comorbid psychosocial factors. This study evaluated the incidence and clinical associations of sexual dysfunction in adult epilepsy patients. METHODS:89 epilepsy patients 18 years and older admitted to the New York University Comprehensive Epilepsy Center epilepsy monitoring unit between 2016 and 2018 completed a survey on sexual functioning. The survey included demographic, clinical, and sexual functioning information with a validated measure of sexual function (the Arizona Sexual Experiences Scale (ASEX). RESULTS:Of 89 surveys completed, 15 (16.9 %) patients had discussed sexual functioning with a medical professional and 20 (22.5 %) reported sexual dysfunction. For the group, the mean ASEX score was 13.6 (SD 4.8). 59 (66.3 %) participants reported not being asked about sexual health by their doctor or nurse practitioner in the last year. The two independent predictors of sexual dysfunction were self-identifying as overweight/obese (OR 6.1, CI 1.4-26.5, P = 0.02) or taking strong enzyme-inducing antiseizure medications (OR 7.8, CI 1.4-44.9, P = 0.02). Other factors such as age, relationship status, duration of epilepsy, the presence of depression or anxiety, cardiovascular risk factors, and opioid/stimulant use, did not predict sexual dysfunction. SIGNIFICANCE/CONCLUSIONS:Our study showed that sexual dysfunction is common in epilepsy patients but infrequently discussed by medical professionals. Two modifiable risk factors, being overweight or taking strong enzyme-inducing antiseizure medications, were independently associated with sexual dysfunction, suggesting interventions to potentially improve sexual health.

OBJECTIVE:Ataluren is a compound that reads through premature stop codons and increases protein expression by increasing translation without modifying transcription or mRNA stability. We investigated the safety and efficacy of ataluren in children with nonsense variants causing Dravet Syndrome (DS) and CDKL5 Deficiency Syndrome (CDD). METHODS:This single-center double-blind, placebo-controlled crossover trial randomized subjects to receive ataluren or placebo for 12 weeks (period 1), a 4-week washout, then another 12-week treatment (period 2). The primary outcome was ataluren's safety profile. The secondary outcome measures were (1) changes in convulsive and/or drop seizure frequency and (2) changes in minor seizure types during ataluren treatment compared to placebo. Exploratory objectives assessed changes in cognitive, motor, and behavioral function as well as quality of life during ataluren therapy. RESULTS:We enrolled seven subjects with DS and eight subjects with CDD. Three treatment-related adverse events (AE) occurred during the blinded phases. Two subjects withdrew due to AE. Ataluren was not effective in reducing seizure frequency or improving cognitive, motor, or behavioral function or quality of life in subjects with either DS or CDD due to nonsense variants. Limitations included a small sample size and 12-week treatment phase, possibly too short to identify a disease-modifying effect. SIGNIFICANCE/CONCLUSIONS:There was no difference between ataluren and placebo; ataluren is not an effective therapy for seizures or other disorders in children with DS or CDD due to nonsense variants. There were no drug-related serious AE during the double-blind period, consistent with ataluren's favorable safety profile in larger studies. (Funded by Epilepsy Foundation, Dravet Syndrome Foundation, Finding A Cure for Seizures and Epilepsy and PTC Therapeutics, Inc.; ClinicalTrials.gov number, NCT02758626).

Although seizures are common in children, they are often overlooked as a potential cause of death. Febrile and nonfebrile seizures can be fatal in children with or without an epilepsy diagnosis and may go unrecognized by parents or physicians. Sudden unexpected infant deaths, sudden unexplained death in childhood, and sudden unexpected death in epilepsy share clinical, neuropathological, and genetic features, including male predominance, unwitnessed deaths, death during sleep, discovery in the prone position, hippocampal abnormalities, and variants in genes regulating cardiac and neuronal excitability. Additionally, epidemiological studies reveal that miscarriages are more common among individuals with a personal or family history of epilepsy, suggesting that some fetal losses may result from epileptic factors. The spectrum of seizure-related deaths in pediatrics is wide and underappreciated; accurately estimating this mortality and understanding its mechanism in children is critical to developing effective education and interventions to prevent these tragedies.