Hiba Arif Haider

Objective: This study was undertaken to examine acute and chronic electroencephalographic (EEG) and seizure characteristics in new onset refractory status epilepticus (NORSE). Methods: Multicenter inpatient and follow‐up clinical and EEG data were analyzed using International League Against Epilepsy definitions and American Clinical Neurophysiology Society (ACNS) EEG terminology. Results: Forty‐four patients were included from the Yale NORSE/FIRES Biorepository (median age = 29 years, 33 female, 38 cryptogenic). Nine hundred twenty inpatient EEG days were reviewed (median = 13.5 days/patient). Presenting status epilepticus (SE) types included 24 convulsive SE (CSE), five focal–motor (FMSE), and 15 nonconvulsive (NCSE). Of 39 patients with post discharge follow‐up (median = 14.7 months), 61.5% ( n = 24) had seizures and required more antiseizure medications than seizure‐free patients (median = 4 vs. 1, p < .001). Inpatient EEG captured epileptiform discharges in all and periodic discharges in 38 patients. Seizures were captured in 38 patients: 33 electrographic (11 only electrographic) and 27 electroclinical. Total seizure burden ranged 6.5–29 615.0 min (median = 50.7). Seizures fulfilling ACNS EEG criteria for SE were captured in 20 patients; two had CSE and the remainder NCSE with coma, nine of whom also had FMSE. Inpatient seizure days (median = 50.0 vs. 15.5%, p = .004) and seizure burden (median = 42.9 vs. 0 min, p = .009) were higher in the first half of monitored days. Interictal findings were equally present and did not predict postdischarge seizures, whereas electroclinical seizures (90.9% vs. 45.5%, p = .037), seizure burden (median = 116.8 vs. 32.5 min, p = .045), and proportion of seizure days (36.3% vs. 20.0%, p = .037) did. Among 12 patients with follow‐up EEGs (median = 19.6 months post onset), posterior‐dominant rhythm (PDR) returned in nine; five had periodic discharges, and three had seizure captured. Significance: Seizures are most commonly convulsive upon presentation and nonconvulsive or clinically subtle throughout the inpatient course in NORSE. Inpatient seizures, but not interictal abnormalities, occur earlier in the inpatient course and presence of electroclinical seizures, seizure burden, and proportion of seizure days but not interictal findings are associated with postdischarge seizures. Although return of PDR reflects neurological recovery, seizure persistence and EEG abnormalities post‐NORSE are common.

Objective: Ceribell Inc.'s point‐of‐care electroencephalographic (EEG) system and artificial intelligence‐based Automated Seizure Burden Estimator (ASBE; ClarityPro) have US Food and Drug Administration clearance for diagnosing electrographic status epilepticus (ESE). The AccuRASE study using ASBE version 6 (V6) showed high negative predictive value (NPV) but limited sensitivity and positive predictive value (PPV) at certain thresholds. Version 8 (V8) is the updated algorithm trained with additional EEG samples. Methods: We tested V8 on the previously used rapid‐response EEG test dataset (not used to train V8). Sensitivity, specificity, PPV, and NPV were compared against blinded expert annotations. ESE, ESE and possible ESE (ESE/pESE), electrographic seizures (ESz), and Esz with highly epileptiform patterns (Esz/HEPs) at burden thresholds of >1%, >10%, >20%, >50%, and >90% were analyzed. Additionally, we evaluated the built‐in ESE and ESE/pESE alerts (≥90% over 5 min, ≥20% over 1 h, or ≥10 continuous minutes of seizure). Results: V8 showed 100% sensitivity at lower thresholds (>10%–20%) for ESE (V6 .86), without a specificity loss (V8 .9 vs. V6 .85), and retained NPV (V8 1.0 vs. V6 .99). Sensitivity at 50% threshold was .86 for ESE (V6 .71), and specificity was .94 (V6 .91); PPV for ESE was 30% for ESE and 80% for ESE/pESE. The specificity for ESE remained high for 90% burden (V8 .96, V6 .97) but the sensitivity dropped (.29 vs. V6 .43). Sensitivity was lower for ESz/HEPs (.54 → .44 at >10% threshold), although the algorithm is not designed to detect HEPs. Built‐in alert analysis showed significantly improved sensitivity for ESE (.57 → .86); specificity and NPV remained high (>.94). Significance: ASBE V8 showed meaningful improvements in sensitivity, continued excellent NPV, and improved built‐in alert accuracy for ESE and ESE/pESE. The best seizure burden threshold for ruling out ESE now is 20% (vs. 10% with V6) and that for ruling in ESE/pESE with reasonable certainty is 50% (vs. 90% with V6).

The hippocampus has been proposed to support visual processing and perception, challenging longstanding accounts that emphasize navigation or declarative memory. A key prediction of visual-processing accounts is that the hippocampus should exhibit similar visuospatial coding properties to those of higher-order visual neocortical areas, such as sensitivity to the size of visual stimuli and contralateral visual field biases. We tested for these properties using intracranial EEG to measure hippocampal neural activity during a retinotopic mapping task. The hippocampus exhibited characteristic slow (∼2 Hz) and fast (∼8 Hz) theta oscillations throughout the task. Fast theta was responsive to the presence but not the amount of visual stimulation. In contrast, slow theta did not generally respond to stimulus presence but scaled with the size of the visual stimulus, consistent with larger receptive fields. Slow theta also showed a contralateral bias, an effect that was specific to the right hippocampus. None of these effects were attributable to microsaccades or performance of the concurrent vigilance task. These findings provide electrophysiological evidence for visual field coding by human hippocampus, supporting accounts of hippocampal function that emphasize its role atop the visual hierarchy. Visual processing of this kind may combine with self-motion, memory, and other signals to support the broader spatial and mnemonic functions with which hippocampal theta oscillations have long been associated.

Epileptiform discharges, or spikes, within electroencephalogram (EEG) recordings are essential for diagnosing epilepsy and localizing seizure origins. Artificial intelligence (AI) offers a promising approach to automating detection, but current models are often hindered by artifact-related false positives and often target either event- or EEG-level classification, thus limiting clinical utility. We developed SpikeNet2, a deep-learning model based on a residual network architecture, and enhanced it with hard-negative mining to reduce false positives. Our study analyzed 17,812 EEG recordings from 13,523 patients across multiple institutions, including Massachusetts General Brigham (MGB) hospitals. Data from the Human Epilepsy Project (HEP) and SCORE-AI (SAI) were also included. A total of 32,433 event-level samples, labeled by experts, were used for training and evaluation. Performance was assessed using the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), calibration error, and a modified area under the curve (mAUC) metric. The model’s generalizability was evaluated using external datasets. SpikeNet2 demonstrated strong performance in event-level spike detection, achieving an AUROC of 0.973 and an AUPRC of 0.995, with 44% of experts surpassing the model on the MGB dataset. In external validation, the model achieved an AUROC of 0.942 and an AUPRC of 0.948 on the HEP dataset. For EEG-level classification, SpikeNet2 recorded an AUROC of 0.958 and an AUPRC of 0.959 on the MGB dataset, an AUROC of 0.888 and an AUPRC of 0.823 on the HEP dataset, and an AUROC of 0.995 and an AUPRC of 0.991 on the SAI dataset, with 32% of experts outperforming the model. The false-positive rate was reduced to an average of nine spikes per hour. SpikeNet2 offers expert-level accuracy in both event-level spike detection and EEG-level classification, while significantly reducing false positives. Its dual functionality and robust performance across diverse datasets make it a promising tool for clinical and telemedicine applications, particularly in resource-limited settings. (Funded by the National Institutes of Health and others.)

We propose and prioritize important outcome domains that should be considered for future research investigating long‐term outcomes (LTO) after new onset refractory status epilepticus (NORSE). The study was led by the international NORSE Institute LTO Working Group. First, literature describing the LTO of NORSE survivors was identified using a PubMed search and summarized to identify knowledge gaps. Subsequently, a consensus‐building process was performed to prioritize and rank important LTO domains for further research. The prioritization of LTO domains was qualitative, enabling the expert panel to generate ideas, share opinions, and provide reasons for the rankings. A second round took place to allow expansion and agreement regarding specific details for each domain. Outcomes were classified into eight main domains: (1) Function: Neuropsychological, Neurological (other than seizures), and Psychiatric (mood and behavior); (2) Quality of Life; (3) Epilepsy; (4) Nonneurological (medical); (5) Social; (6) Caregiver Burden; (7) Long‐Term Mortality; and (8) Health Care System Impact. In addition, the working group suggested obtaining outcome measures for each domain at 6 months and 1 year after discharge and annually thereafter until stability has been reached. There are no currently established time frames set for when LTO in NORSE begin or plateau, and previously there existed no consensus regarding which LTO should be considered. This consensus process identifies and recommends NORSE LTO domains that should be considered in future research studies to provide more consistent results that can be compared between studies. Survivors of NORSE should be evaluated serially and at fixed points over time to maximize our understanding of the recovery trajectory for all LTO domains. Establishing reliable and standardized data describing LTO (beyond seizures) after NORSE will support discussions with families during the acute stages, prognostication, the development of targeted management strategies for survivors, and future comparative research globally helping to identify biomarkers that may predict LTO.

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.