Comparative analysis of patients with new onset refractory status epilepticus preceded by fever (febrile infection-related epilepsy syndrome) versus without prior fever: An interim analysis.

Febrile infection-related epilepsy syndrome (FIRES) is a subset of new onset refractory status epilepticus (NORSE) that involves a febrile infection prior to the onset of the refractory status epilepticus. It is unclear whether FIRES and non-FIRES NORSE are distinct conditions. Here, we compare 34 patients with FIRES to 30 patients with non-FIRES NORSE for demographics, clinical features, neuroimaging, and outcomes. Because patients with FIRES were younger than patients with non-FIRES NORSE (median = 28 vs. 48 years old, p = .048) and more likely cryptogenic (odds ratio = 6.89), we next ran a regression analysis using age or etiology as a covariate. Respiratory and gastrointestinal prodromes occurred more frequently in FIRES patients, but no difference was found for non-infection-related prodromes. Status epilepticus subtype, cerebrospinal fluid (CSF) and magnetic resonance imaging findings, and outcomes were similar. However, FIRES cases were more frequently cryptogenic; had higher CSF interleukin 6, CSF macrophage inflammatory protein-1 alpha (MIP-1a), and serum chemokine ligand 2 (CCL2) levels; and received more antiseizure medications and immunotherapy. After controlling for age or etiology, no differences were observed in presenting symptoms and signs or inflammatory biomarkers, suggesting that FIRES and non-FIRES NORSE are very similar conditions.

Trends in management of patients with new-onset refractory status epilepticus (NORSE) from 2016 to 2023: An interim analysis.

In response to the evolving treatment landscape for new-onset refractory status epilepticus (NORSE) and the publication of consensus recommendations in 2022, we conducted a comparative analysis of NORSE management over time. Seventy-seven patients were enrolled by 32 centers, from July 2016 to August 2023, in the NORSE/FIRES biorepository at Yale. Immunotherapy was administered to 88% of patients after a median of 3 days, with 52% receiving second-line immunotherapy after a median of 12 days (anakinra 29%, rituximab 25%, and tocilizumab 19%). There was an increase in the use of second-line immunotherapies (odds ratio [OR] = 1.4, 95% CI = 1.1-1.8) and ketogenic diet (OR = 1.8, 95% CI = 1.3-2.6) over time. Specifically, patients from 2022 to 2023 more frequently received second-line immunotherapy (69% vs 40%; OR = 3.3; 95% CI = 1.3-8.9)-particularly anakinra (50% vs 13%; OR = 6.5; 95% CI = 2.3-21.0), and the ketogenic diet (OR = 6.8; 95% CI = 2.5-20.1)-than those before 2022. Among the 27 patients who received anakinra and/or tocilizumab, earlier administration after status epilepticus onset correlated with a shorter duration of status epilepticus (ρ = .519, p = .005). Our findings indicate an evolution in NORSE management, emphasizing the increasing use of second-line immunotherapies and the ketogenic diet. Future research will clarify the impact of these treatments and their timing on patient outcomes.

Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish.

Identifying new, more efficacious anti-seizure medications (ASMs) is challenging, partly due to limitations in animal-based assays. Zebrafish ( Danio rerio ) can serve as a model of chemical and genetic seizures, but methods for detecting seizure-like activity in zebrafish, though powerful, have been hampered by low sensitivity (locomotor/behavioral assays) or low-throughput (tectal electrophysiology or calcium fluorescence microscopy). To address these issues, we developed a novel approach to assay seizure-like activity using combined locomotor and calcium fluorescence features, measured simultaneously from unrestrained larval zebrafish using a 96-well fluorescent plate reader. Using custom software to track fish movement and changes in fluorescence (deltaF/F0) from high-speed time-series (12.6Hz), we trained logistic classifiers using elastic net regression to distinguish seizure-like activity from non-seizure related changes based on event-specific and subject-specific features in response to the GABA A R antagonist, pentylenetetrazole (PTZ). We demonstrate that a classifier trained on combined movement and fluorescence data achieves high accuracy ("PTZ M+F"; area-under-curve receiver-operator characteristic (AUC-ROC): 0.98; F1 score: 0.912) and out-performs classifiers trained on movement ("PTZ M"; AUC-ROC: 0.9, F1: 0.9) or fluorescence features alone ("PTZ F"; AUC-ROC 0.96; F1: 0.87). The rate of classified seizure-like events increases as a dose-response to PTZ (serial dose escalation, 0, 2.5mM, 15mM) and is strongly suppressed by ASM treatment (valproic acid, VPA; tiagabine, TGB). At high-dose PTZ, we show that VPA reduces seizure-like activity defined by either "PTZ M+F" or "PTZ M" classifiers. Meanwhile, TGB selectively reduces events defined by the "PTZ M+F" classifier, paralleling previous reports that TGB reduces electrographic but not locomotor seizures and highlighting the potential for our approach to combine features of previously orthogonal assays. Using ASM benchmark data, we employ bootstrap simulation to calculate the expected statistical power of our method as a function of sample size. We demonstrate that anti-seizure responses (robust strictly standardized mean difference, RSSMD, versus control) with magnitudes similar to those associated with VPA or TGB can be reliably detected (true positive rate (TPR) > 90%) with as few as N=4 biological replicates per group, while maintaining a 5% false positive rate. In a prospective test screen with 3-6 replicates per group and on-plate controls, the anti-seizure effect of 4 out of 5 tested ASMs (CBZ, LEV, LZP, TGB) was detected. In summary, we demonstrate a simple high-throughput approach to whole organism anti-seizure phenotyping combining two previously reported metrics to facilitate screens for novel anti-seizure interventions in zebrafish.

Automated Quantification of Periodic Discharges in Human Electroencephalogram.

Periodic discharges (PDs) are pathologic patterns of epileptiform discharges repeating at regular intervals, commonly detected in the human electroencephalogram (EEG) signals in patients who are critically ill. The frequency and spatial extent of PDs are associated with the tendency of PDs to cause brain injury, existing automated algorithms do not quantify the frequency and spatial extent of PDs. The present study presents an algorithm for quantifying frequency and spatial extent of PDs. The algorithm quantifies the evolution of these parameters within a short (10-14 second) window, with a focus on lateralized and generalized periodic discharges. We test our algorithm on 300 ``easy'', 300 ``medium'', and 240 ``hard'' examples (840 total epochs) of periodic discharges as quantified by interrater consensus from human experts when analyzing the given EEG epochs. We observe $95.0\%$ agreement with a 95\% confidence interval (CI) of $[94.9\%, 95.1\%]$ between algorithm outputs with reviewer clincal judgement for easy examples, $92.0\%$ agreement (95\% CI $[91.9\%, 92.2\%]$) for medium examples, and $90.4\%$ agreement (95\% CI $[90.3\%, 90.6\%]$) for hard examples. The algorithm is also computationally efficient and is able to run in $0.385 \pm 0.038$ seconds for a single epoch using our provided implementation of the algorithm. The results demonstrate the algorithm's effectiveness in quantifying these discharges and provide a standardized and efficient approach for PD quantification as compared to existing manual approaches.

Zebrafish models of candidate human epilepsy-associated genes provide evidence of hyperexcitability.

Hundreds of novel candidate human epilepsy-associated genes have been identified thanks to advancements in next-generation sequencing and large genome-wide association studies, but establishing genetic etiology requires functional validation. We generated a list of >2,200 candidate epilepsy-associated genes, of which 48 were developed into stable loss-of-function (LOF) zebrafish models. Of those 48, evidence of seizure-like behavior was present in 5 (arfgef1, kcnd2, kcnv1, ubr5, and wnt8b). Further characterization provided evidence for epileptiform activity via electrophysiology in kcnd2 and wnt8b mutants. Additionally, arfgef1 and wnt8b mutants showed a decrease in the number of inhibitory interneurons in the optic tectum of larval animals. Further, RNA sequencing (RNA-seq) revealed convergent transcriptional abnormalities between mutant lines, consistent with their developmental defects and hyperexcitable phenotypes. These zebrafish models provide strongest experimental evidence supporting the role of ARFGEF1, KCND2, and WNT8B in human epilepsy and further demonstrate the utility of this model system for evaluating candidate human epilepsy genes.

Zebrafish models of candidate human epilepsy-associated genes provide evidence of hyperexcitability.

Hundreds of novel candidate human epilepsy-associated genes have been identified thanks to advancements in next-generation sequencing and large genome-wide association studies, but establishing genetic etiology requires functional validation. We generated a list of >2200 candidate epilepsy-associated genes, of which 81 were determined suitable for the generation of loss-of-function zebrafish models via CRISPR/Cas9 gene editing. Of those 81 crispants, 48 were successfully established as stable mutant lines and assessed for seizure-like swim patterns in a primary F2 screen. Evidence of seizure-like behavior was present in 5 (arfgef1, kcnd2, kcnv1, ubr5, wnt8b) of the 48 mutant lines assessed. Further characterization of those 5 lines provided evidence for epileptiform activity via electrophysiology in kcnd2 and wnt8b mutants. Additionally, arfgef1 and wnt8b mutants showed a decrease in the number of inhibitory interneurons in the optic tectum of larval animals. Furthermore, RNAseq revealed convergent transcriptional abnormalities between mutant lines, consistent with their developmental defects and hyperexcitable phenotypes. These zebrafish models provide strongest experimental evidence supporting the role of ARFGEF1, KCND2, and WNT8B in human epilepsy and further demonstrate the utility of this model system for evaluating candidate human epilepsy genes.