Faculty Bibliography

Objective: A recent clinical trial with 0.7mg/kg/day of fenfluramine (FFA) showed 62.3% (IC 95%: -47.7%; -72.8%; p<0.001) reduction in convulsive seizure frequency (CSF) compared to placebo. However, the impact of the disease on the patient and their caregivers may depend on other variables. This alternative analysis value the impact of other results.
Method(s): After a baseline period of 6 weeks patients with DS ages 2 to 18 years, was randomized to FFA 0.7 or 0.2mg/kg/day or placebo added. Time to new event (time required to experience the same number of crisis as in the reference period [TTE]) was analyzed. Intervals without crisis and number of days without crisis was analyzed too.
Result(s): 119 patients with DS receiving FFA 0.7mg/kg/day; FFA0.2mg/kg/day; or placebo. TTE was significantly longer in active groups. Placebo: 6 weeks, FFA 0.2mg/kg/day:8 weeks and FFA 0.7mg/kg/day: >12 weeks (p<0.001; ~60% of patients in the FFA 0.7mg/kg/day group never reached their baseline seizure count and were censored). The number of days without crisis was higher in groups treated with FFA: 33 and 20 days without additional crisis counted in the active groups. The longest average without crisis was higher with FFA 0.7mg/kg/day (25 days; p<0.001) and FFA 0.2mg/kg/day (15 days; Px0.035) than with placebo (9.5 days).
Conclusion(s): FFA extended TTE and provided significantly more days without crisis and longer periods without crisis than placebo. Our analysis can help assess the ability of a treatment to reduce the burden of seizures in patients with SD and their caregivers

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.

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.

Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain's ability to discover regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words), while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.

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.

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).

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.

Epilepsy is a heterogenous group of disorders defined by recurrent seizure activity due to abnormal synchronized activity of neurons. A growing number of epilepsy cases are believed to be caused by genetic factors and copy number variants (CNV) contribute to up to 5% of epilepsy cases. However, CNVs in epilepsy are usually large deletions or duplications involving multiple neurodevelopmental genes. In patients who underwent seizure focus resection for treatment-resistant epilepsy, whole genome DNA methylation profiling identified 3 main clusters of which one showed strong association with receptor tyrosine kinase (RTK) genes. We identified focal copy number gains involving epidermal growth factor receptor (EGFR) and PDGFRA loci. The dysplastic neurons of cases with amplifications showed marked overexpression of EGFR and PDGFRA, while glial and endothelial cells were negative. Targeted sequencing of regulatory regions and DNA methylation analysis revealed that only enhancer regions of EGFR and gene promoter of PDGFRA were amplified, while coding regions did not show copy number abnormalities or somatic mutations. Somatic focal copy number gains of noncoding regulatory represent a previously unrecognized genetic driver in epilepsy and a mechanism of abnormal activation of RTK genes. Upregulated RTKs provide a potential avenue for therapy in seizure disorders.

OBJECTIVE:To analyze the association between peri-ictal brainstem posturing semiologies with post-ictal generalized electroencephalographic suppression (PGES) and breathing dysfunction in generalized convulsive seizures (GCS). METHODS:Prospective, multicenter analysis of GCS. Ictal brainstem semiology was classified as (1) decerebration: bilateral symmetric tonic arm extension, (2) decortication: bilateral symmetric tonic arm flexion only, (3) hemi-decerebration: unilateral tonic arm extension with contralateral flexion and (4) absence of ictal tonic phase. Post-ictal posturing was also assessed. Respiration was monitored using thoraco-abdominal belts, video and pulse oximetry. RESULTS:= 0.035). CONCLUSIONS:recovery. Peri-ictal brainstem posturing may be surrogate biomarkers for GCS severity identifiable without in-hospital monitoring. CLASSIFICATION OF EVIDENCE/METHODS:This study provides Class III evidence that peri-ictal brainstem posturing is associated with the GCS with more prolonged PGES and more severe breathing dysfunction.

Subependymal giant-cell astrocytomas (SEGAs) are slow-growing brain tumors that are a hallmark feature seen in 5-10% of patients with Tuberous Sclerosis Complex (TSC). Though histologically benign, they can cause serious neurologic symptoms, leading to death if untreated. SEGAs consistently show biallelic loss of TSC1 or TSC2. Herein, we aimed to define other somatic events beyond TSC1/TSC2 loss and identify potential transcriptional drivers that contribute to SEGA formation. Paired tumor-normal whole-exome sequencing was performed on 21 resected SEGAs from 20 TSC patients. Pathogenic variants in TSC1/TSC2 were identified in 19/21 (90%) SEGAs. Copy neutral loss of heterozygosity (size range: 2.2-46 Mb) was seen in 76% (16/21) of SEGAs (44% chr9q and 56% chr16p). An average of 1.4 other somatic variants (range 0-7) per tumor were identified, unlikely of pathogenic significance. Whole transcriptome RNA-sequencing analyses revealed 190 common differentially expressed genes in SEGA (n = 16, 13 from a prior study) in pairwise comparison to each of: low grade diffuse gliomas (n = 530) and glioblastoma (n = 171) from The Cancer Genome Atlas (TCGA) consortium, ganglioglioma (n = 10), TSC cortical tubers (n = 15), and multiple normal tissues. Among these, homeobox transcription factors (TFs) HMX3, HMX2, VAX1, SIX3; and TFs IRF6 and EOMES were all expressed >12-fold higher in SEGAs (FDR/q-value < 0.05). Immunohistochemistry supported the specificity of IRF6, VAX1, SIX3 for SEGAs in comparison to other tumor entities and normal brain. We conclude that SEGAs have an extremely low somatic mutation rate, suggesting that TSC1/TSC2 loss is sufficient to drive tumor growth. The unique and highly expressed SEGA-specific TFs likely reflect the neuroepithelial cell of origin, and may also contribute to the transcriptional and epigenetic state that enables SEGA growth following two-hit loss of TSC1 or TSC2 and mTORC1 activation.