Saurabh R. Sinha

About half of patients who undergo epilepsy surgery for drug-resistant epilepsy have seizure recurrence, supporting the need for approaches that more accurately identify the epileptogenic zone , defined as the brain areas whose removal causes cessation of seizures. Altered network connectivity has emerged as a candidate biomarker of the epileptogenic zone, but how connectivity is altered in the epileptogenic zone remains uncertain, with prior studies reporting inconsistent results. We hypothesized that a difference in intrinsic versus extrinsic connectivity of the epileptogenic zone may explain prior discrepant findings. We studied a multicenter cohort of adult and pediatric patients who underwent intracranial EEG recording and brain stimulation as part of epilepsy surgery planning. We measured spontaneous connectivity using Pearson correlation and perturbational connectivity using stimulation evoked potentials, modeling the connectivity according to the location of contacts in relation to the seizure onset zone (SOZ) while controlling for inter-electrode distance. We analyzed 79 patients (37 adults, 42 children). For both adult and pediatric patients, resting connectivity was higher within compared to outside the SOZ, but resting connectivity between SOZ and non-SOZ contacts was reduced. Stimulation connectivity followed a similar pattern, with elevated within-SOZ connectivity but reduced connectivity between SOZ and non-SOZ. The results support the hypothesis that the epileptogenic zone is disconnected from the rest of the brain but intrinsically hyperconnected. This result helps reconcile prior inconsistencies across studies, aligns with the results of basic science studies, and suggests that future translational work should model this heterogeneous pattern to increase the yield of using connectivity to localize the epileptogenic zone.

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.

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.

Epilepsy surgery can eliminate seizures in patients with drug-resistant focal epilepsy. Surgical intervention requires proper identification of the epileptic network and often involves implanting stereo-EEG electrodes in patients where non-invasive methods are insufficient. However, only ∼60% of patients achieve seizure-freedom following surgery. Quantitative methods have been developed to help improve surgical outcomes. However, previous quantitative methods that localized interictal spike and seizure activity using stereo-EEG recordings did not account for the propagation path encoded by the temporal dynamics of stereo-EEG recordings. Reconstructing the seizure propagation path can aid in determining whether a signal originated from the seizure onset or propagation zone, which directly informs treatment decisions. We developed a novel source reconstruction algorithm, Temporally Dependent Iterative Expansion (TEDIE), that accurately reconstructs propagating and expanding neural sources over time. TEDIE iteratively optimizes the number, location and size of neural sources to minimize the differences between the reconstructed and recorded stereo-EEG signals using temporal information to refine the reconstructions. The TEDIE output comprises a movie of seizure activity projected onto patient-specific brain anatomy. We analysed data from 46 epilepsy patients implanted with stereo-EEG electrodes at Duke Hospital (12 patients) and the Hospital of the University of Pennsylvania (34 patients). We reconstructed seizure recordings and found that TEDIE’s seizure onset zone reconstructions were closer to the resected brain region for Engel 1 compared to Engel 2–4 patients, retrospectively validating the clinical utility of TEDIE. We also demonstrated that TEDIE has prospective clinical value, whereby metrics that can be determined presurgically accurately predict whether a patient would achieve seizure-freedom following surgery. Furthermore, we used TEDIE to delineate new potential surgical targets in 12/23 patients who are currently Engel 2–4. We validated TEDIE by accurately reconstructing various dynamic synthetic neural sources with known locations and sizes. TEDIE generated more accurate, focal and interpretable dynamic reconstructions of seizures compared to other algorithms (sLORETA and IRES). Our findings demonstrate that TEDIE is a promising clinical tool that can greatly improve epileptogenic zone localization and epilepsy surgery outcomes.

Neuroprognostication for disorders of consciousness (DoC) after severe acute brain injury is a major challenge, and the conventional clinical approach struggles to keep pace with a rapidly evolving literature. Lacking specialization, and fragmented between providers, conventional neuroprognostication is variable, frequently incongruent with guidelines, and prone to error, contributing to avoidable mortality and morbidity. We review the limitations of the conventional approach to neuroprognostication and DoC care, and propose a paradigm entitled the Recovery of Consciousness Via Evidence-Based Medicine and Research (RECOVER) program to address them. The aim of the RECOVER program is to provide specialized, comprehensive, and longitudinal care that synthesizes interdisciplinary perspectives, provides continuity to patients and families, and improves the future of DoC care through research and education. This model, if broadly adopted, may help establish neuroprognostication as a new subspecialty that improves the care of this vulnerable patient population.

Mesial temporal lobe epilepsy (mTLE) is a common localization of drug-resistant epilepsy in adults. Patients often undergo intracranial EEG (iEEG) monitoring to confirm localization and determine candidacy for focal ablation or resection. Clinicians primarily base surgical decision-making on seizure onset patterns, with imaging abnormalities and information from interictal epileptiform discharge (spikes) used as ancillary data. How the morphology and timing of spikes within multi-electrode sequences may inform surgical planning is unknown, in part due to the lack of measurement methods for large datasets. We hypothesized that patients with mTLE have a distinct mesial-to-lateral spike pattern that differentiates them from other epilepsy localizations. In a multicenter study at the University of Pennsylvania and the Medical University of South Carolina, we analyzed the timing and morphology of spikes and seizure high frequency energy ratio (HFER) in 75 patients with drug-resistant epilepsy. We compared these features across patients with mTLE, temporal neocortical epilepsy, and other localizations. A logistic regression model combining all features predicted a clinical localization of mTLE in unseen patients with an AUC of 0.82 (compared to an AUC of 0.70 for seizure-only features, DeLong’s test p = 0.08). Spike rate was the most important feature in the combined model. Modeled probability of mTLE was similar between patients who had a good versus a poor 12-month outcome after undergoing a mesial temporal resection or ablation ( p = 0.34). These findings support quantitative spike analysis to supplement analysis of seizures for use in surgical planning.

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data are captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-centre study for model development; two-centre study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using several interictal EEG features derived from recent publications. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. Forty-seven patients (30 female; ages 20–69; 20 left-sided, 10 right-sided and 17 bilateral seizure onsets) were analysed for model development and internal validation. Nineteen patients (10 female; ages 23–73; 5 left-sided, 10 right-sided, 4 bilateral) were analysed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. We replicated the finding that right temporal lobe epilepsy was harder to distinguish in a separate modality of resting-state functional MRI. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome. A potential clinical application of these findings could be to either support or oppose a hypothesis of unilateral temporal lobe epilepsy when deciding to pursue surgical resection or ablation as opposed to device implantation.