Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may temporarily interrupt these cycles. We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56-0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may temporarily interrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.

Objective: Large-language models (LLMs) can potentially revolutionize health care delivery and research, but risk propagating existing biases or introducing new ones. In epilepsy, social determinants of health are associated with disparities in care access, but their impact on seizure outcomes among those with access remains unclear. Here we (1) evaluated our validated, epilepsy-specific LLM for intrinsic bias, and (2) used LLM-extracted seizure outcomes to determine if different demographic groups have different seizure outcomes. Materials and Methods: We tested our LLM for differences and equivalences in prediction accuracy and confidence across demographic groups defined by race, ethnicity, sex, income, and health insurance, using manually annotated notes. Next, we used LLM-classified seizure freedom at each office visit to test for demographic outcome disparities, using univariable and multivariable analyses. Results: We analyzed 84 675 clinic visits from 25 612 unique patients seen at our epilepsy center. We found little evidence of bias in the prediction accuracy or confidence of outcome classifications across demographic groups. Multivariable analysis indicated worse seizure outcomes for female patients (OR 1.33, P ≤ .001), those with public insurance (OR 1.53, P ≤ .001), and those from lower-income zip codes (OR ≥1.22, P ≤ .007). Black patients had worse outcomes than White patients in univariable but not multivariable analysis (OR 1.03, P = .66). Conclusion: We found little evidence that our LLM was intrinsically biased against any demographic group. Seizure freedom extracted by LLM revealed disparities in seizure outcomes across several demographic groups. These findings quantify the critical need to reduce disparities in the care of people with epilepsy.

Objective. Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies. Approach. We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features. Main results. The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p = 0.018, 97.5% confidence interval = [−1.242, −0.116]) and lower pre-surgical severity was associated with better surgical outcome (p = 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity. Significance. We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool’s utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.

Objective. Neuroscience experiments and devices are generating unprecedented volumes of data, but analyzing and validating them presents practical challenges, particularly in annotation. While expert annotation remains the gold standard, it is time consuming to obtain and often poorly reproducible. Although automated annotation approaches exist, they rely on labeled data first to train machine learning algorithms, which limits their scalability. A semi-automated annotation approach that integrates human expertise while optimizing efficiency at scale is critically needed. To address this, we present Annotation Co-pilot, a human-in-the-loop solution that leverages deep active learning (AL) and self-supervised learning (SSL) to improve intracranial EEG (iEEG) annotation, significantly reducing the amount of human annotations. Approach. We automatically annotated iEEG recordings from 28 humans and 4 dogs with epilepsy implanted with two neurodevices that telemetered data to the cloud for analysis. We processed 1500 h of unlabeled iEEG recordings to train a deep neural network using a SSL method Swapping Assignments between View to generate robust, dataset-specific feature embeddings for the purpose of seizure detection. AL was used to select only the most informative data epochs for expert review. We benchmarked this strategy against standard methods. Main result. Over 80 000 iEEG clips, totaling 1176 h of recordings were analyzed. The algorithm matched the best published seizure detectors on two datasets (NeuroVista and NeuroPace responsive neurostimulation) but required, on average, only 1/6 of the human annotations to achieve similar accuracy (area under the ROC curve of 0.9628 ± 0.015) and demonstrated better consistency than human annotators (Cohen’s Kappa of 0.95 ± 0.04). Significance. ‘Annotation Co-pilot’ demonstrated expert-level performance, robustness, and generalizability across two disparate iEEG datasets while reducing annotation time by an average of 83%. This method holds great promise for accelerating basic and translational research in electrophysiology, and potentially accelerating the pathway to clinical translation for AI-based algorithms and devices.

Non-human primates (NHPs) are precious resources for cutting-edge neuroscientific research, including large-scale viral vector-based experimentation such as optogenetics. We propose to improve surgical outcomes by enhancing the surgical preparation practices of convection-enhanced delivery (CED), which is an efficient viral vector infusion technique for large brains such as NHPs’. Here, we present both real-time and next-day MRI data of CED in the brains of ten NHPs, and we present a quantitative, inexpensive, and practical bench-side model of the in vivo CED data. Our bench-side model is composed of food coloring infused into a transparent agar phantom, and the spread of infusion is optically monitored over time. Our proposed method approximates CED infusions into the cortex, thalamus, medial temporal lobe, and caudate nucleus of NHPs, confirmed by MRI data acquired with either gadolinium-based or manganese-based contrast agents co-infused with optogenetic viral vectors. These methods and data serve to guide researchers and surgical team members in key surgical preparations for intracranial viral delivery using CED in NHPs, and thus improve expression targeting and efficacy and, as a result, reduce surgical risks.

Annotating seizure onset and spread in intracranial EEG is essential for epilepsy surgical planning, yet manual annotation is unreliable and cannot scale to large datasets. We introduce Neural Dynamic Divergence (NDD), an unsupervised framework that detects seizure activity by measuring deviation from patient-specific baseline neural dynamics using autoregressive models. NDD requires no labeled training data and adapts to individual patients, channels, and brain states. Validating against expert consensus annotations from 46 seizures, NDD achieves human-level agreement ( ϕ = 0.58 vs. inter-rater ϕ = 0.64) and outperforms existing algorithms on 1,019 seizures with soft labels (AUROC = 0.87). We demonstrate clinical utility by automatically annotating 2,017 seizures, revealing that seizure spread patterns distinguish epilepsy subtypes and predict surgical outcomes. NDD also generalizes to continuous ICU scalp EEG monitoring (AUROC = 0.77). We provide NDD as an open-source Python package to enable scalable seizure annotation across research centers.