Faculty Bibliography

Sudden unexplained death in childhood (SUDC) is an understudied problem. Whole-exome sequence data from 124 "trios" (decedent child, living parents) was used to test for excessive de novo mutations (DNMs) in genes involved in cardiac arrhythmias, epilepsy, and other disorders. Among decedents, nonsynonymous DNMs were enriched in genes associated with cardiac and seizure disorders relative to controls (odds ratio = 9.76, P = 2.15 × 10^-4). We also found evidence for overtransmission of loss-of-function (LoF) or previously reported pathogenic variants in these same genes from heterozygous carrier parents (11 of 14 transmitted, P = 0.03). We identified a total of 11 SUDC proband genotypes (7 de novo, 1 transmitted parental mosaic, 2 transmitted parental heterozygous, and 1 compound heterozygous) as pathogenic and likely contributory to death, a genetic finding in 8.9% of our cohort. Two genes had recurrent missense DNMs, RYR2 and CACNA1C Both RYR2 mutations are pathogenic (P = 1.7 × 10^-7) and were previously studied in mouse models. Both CACNA1C mutations lie within a 104-nt exon (P = 1.0 × 10^-7) and result in slowed L-type calcium channel inactivation and lower current density. In total, six pathogenic DNMs can alter calcium-related regulation of cardiomyocyte and neuronal excitability at a submembrane junction, suggesting a pathway conferring susceptibility to sudden death. There was a trend for excess LoF mutations in LoF intolerant genes, where ≥1 nonhealthy sample in denovo-db has a similar variant (odds ratio = 6.73, P = 0.02); additional uncharacterized genetic causes of sudden death in children might be discovered with larger cohorts.

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

The COVID-19 pandemic has had an unprecedented impact on people and healthcare services. The disruption to chronic illnesses, such as epilepsy, may relate to several factors ranging from direct infection to secondary effects from healthcare reorganization and social distancing measures.

OBJECTIVE:Appetite disturbance and growth abnormalities are commonly reported in children with Dravet syndrome (DS). Fenfluramine (Fintepla) has demonstrated profound reduction in convulsive seizure frequency in DS and was recently approved for use in DS in the US and EU. Prior to its use in epilepsy, fenfluramine was approved to suppress appetite in obese adults. Here, we evaluated the impact of fenfluramine on weight and growth in patients with DS treated for ≥12 months or ≥24 months and compared the results with growth curves in normative reference populations and published historical controls among patients with DS. METHODS:Historical control data from a recent study of 68 patients with DS show decreases in height and weight Z-scores of ∼0.1 standard deviation (SD) for every 12-month increase in age (Eschbach K. Seizure. 2017;52:117-22). Anthropometric data for fenfluramine were extracted from an open-label extension (OLE) study of eligible patients with DS (2-18 y/o; fenfluramine dose: 0.2-0.7 mg/kg/day). Z-score analyses were based on the Boston Children's Hospital algorithm and assessed potential impact of fenfluramine on growth at OLE baseline, at Month 12, and at Month 24. A mixed-effect model for repeated measures (MMRM) estimated changes in height and weight over time. Height and weight Z-scores were also analyzed by dose group (0.2-<0.3 mg/kg/day, 0.3-<0.5 mg/kg/day, and 0.5-0.7 mg/kg/day), averaged over time. RESULTS:At the time of analysis, 279 patients were treated with fenfluramine for ≥12 months; 128 were treated for ≥24 months. Relative to the reference population with DS, fenfluramine treatment for ≥12 months or for ≥24 months had minimal impact on height or weight over time as assessed by Z-score analyses. No substantial dose-dependent changes from baseline were observed at Month 12 nor at Month 24. MMRM showed that patients treated with fenfluramine for ≥12 months (N = 262) had an estimated change in Z-score per year of -0.056 for height and -0.166 for weight. For patients with data from all three time points (baseline, 12 months, and 24 months; N = 110), estimated changes in Z-scores per year were -0.025 for height and -0.188 for weight. MMRM projections based on normative reference growth curves were comparable to growth data from historical control populations with DS. SIGNIFICANCE/CONCLUSION:Long-term treatment with fenfluramine had minimal impact on the growth of patients with DS as demonstrated by differences in Z-scores for height and weight at 12 months and at 24 months. Changes in Z-scores for height and weight were consistent with published reports on patients with DS.

Objective/UNASSIGNED:To evaluate current clinical practices and evidence-based literature to establish preliminary recommendations for the management of adults using ketogenic diet therapies (KDTs). Methods/UNASSIGNED:A 12-topic survey was distributed to international experts on KDTs in adults consisting of neurologists and dietitians at medical institutions providing KDTs to adults with epilepsy and other neurologic disorders. Panel survey responses were tabulated by the authors to determine the common and disparate practices between institutions and to compare these practices in adults with KDT recommendations in children and the medical literature. Recommendations are based on a combination of clinical evidence and expert opinion regarding management of KDTs. Results/UNASSIGNED:Surveys were obtained from 20 medical institutions with >2,000 adult patients treated with KDTs for epilepsy or other neurologic disorders. Common side effects reported are similar to those observed in children, and recommendations for management are comparable with important distinctions, which are emphasized. Institutions differ with regard to recommended biochemical assessment, screening, monitoring, and concern for long-term side effects, and further investigation is warranted to determine the optimal clinical management. Differences also exist between screening and monitoring practices among adult and pediatric providers. Conclusions/UNASSIGNED:KDTs may be safe and effective in treating adults with drug-resistant epilepsy, and there is emerging evidence supporting the use in other adult neurologic disorders and general medical conditions as well. Therefore, expert recommendations to guide optimal care are critical as well as further evidence-based investigation.

Models of reading emphasize that visual (orthographic) processing provides input to phonological as well as lexical-semantic processing. Neurobiological models of reading have mapped these processes to distributed regions across occipital-temporal, temporal-parietal, and frontal cortices. However, the role of the precentral gyrus in these models is ambiguous. Articulatory phonemic representations in the precentral gyrus are obviously involved in reading aloud, but it is unclear if the precentral gyrus is recruited during reading silently in a time window consistent with participation in phonological processing contributions. Here, we recorded intracranial electrophysiology during a speeded semantic decision task from 24 patients to map the spatio-temporal flow of information across the cortex during silent reading. Patients selected animate nouns from a stream of nonanimate words, letter strings, and false-font stimuli. We characterized the distribution and timing of evoked high-gamma power (70-170 Hz) as well as phase-locking between electrodes. The precentral gyrus showed a proportion of electrodes responsive to linguistic stimuli (27%) that was at least as high as those of surrounding peri-sylvian regions. These precentral gyrus electrodes had significantly greater high-gamma power for words compared to both false-font and letter-string stimuli. In a patient with word-selective effects in the fusiform, superior temporal, and precentral gyri, there was significant phase-locking between the fusiform and precentral gyri starting at ∼180 msec and between the precentral and superior temporal gyri starting at ∼220 msec. Finally, our large patient cohort allowed exploratory analyses of the spatio-temporal reading network underlying silent reading. The distribution, timing, and connectivity results place the precentral gyrus as an important hub in the silent reading network.

PURPOSE/OBJECTIVE:To assess the impact of fenfluramine (FFA) on the expected mortality incidence, including sudden unexpected death in epilepsy (SUDEP), in persons with Dravet syndrome (DS). METHODS:In this pooled analysis, total time of exposure for persons with DS who were treated with FFA in phase 3 clinical trials, in United States and European Early Access Programs, and in two long-term open-label observational studies in Belgium was calculated. Literature was searched for reports of SUDEP mortality in DS, which were utilized as a comparison. Mortality rates were expressed per 1000 person-years. RESULTS:A total of 732 persons with DS were treated with FFA, representing a total of 1185.3 person-years of exposure. Three deaths occurred, all in the phase 3 program: one during placebo treatment (probable SUDEP) and two during treatment with FFA (one probable SUDEP and one definite SUDEP). The all-cause and SUDEP mortality rates during treatment with FFA was 1.7 per 1000 person-years (95% CI, 0.4 to 6.7), a value lower than the all-cause estimate of 15.8 per 1000 person-years (95% CI, 9.9 to 25.4) and SUDEP estimate of 9.3 (95% CI, 5.0 to 17.3) reported by Cooper et al. (Epilepsy Res 2016;128:43-7) for persons with DS receiving standard-of-care. CONCLUSION/CONCLUSIONS:All-cause and SUDEP mortality rates in DS patients treated with FFA were substantially lower than in literature reports. Further studies are warranted to confirm that FFA reduces SUDEP risk in DS patients and to better understand the potential mechanism(s) by which FFA lowers SUDEP risk. CLINICAL TRIAL REGISTRATION/BACKGROUND:NCT02926898, NCT02682927, NCT02826863, NCT02823145, NCT03780127.

Background and Objectives/UNASSIGNED:syndrome by collecting data, including EEG, from a large cohort of affected patients. Methods/UNASSIGNED:Syndrome Foundation and the literature. Data on clinical, genetic, neuroimaging, and neurophysiologic features were obtained. Results/UNASSIGNED:without any clear genotype-phenotype associations. Discussion/UNASSIGNED:syndrome presents with a developmental and epileptic encephalopathy with characteristics recognizable from neonatal age, which should prompt genetic screening. Sixty percent have drug-resistant epilepsy with focal or generalized seizures. We collected more than 90 pathogenic variants without observing overt genotype-phenotype associations.

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.