About

Nishant Sinha, Ph.D., is an Assistant Professor of Informatics in Biostatistics and Epidemiology at the Hospital of the University of Pennsylvania. He is the lead faculty at the Penn Center for Neuroengineering and Therapeutics and is affiliated with the Graduate Group in Epidemiology and Biostatistics. His research focuses on bridging brain science and clinical practice through the development of translational methods that quantify disease severity, progression, and outcomes, with the aim of removing barriers to clinical translation and guiding personalized treatments across various neurological disorders including epilepsy, traumatic brain injury, movement disorders, brain tumors, and neurodegeneration. Dr. Sinha's expertise encompasses engineering, informatics, biostatistics, and neurology, with a specialization in translational and clinical research. He directs the Penn NeuroBridge Laboratory, where his core research areas include discovering novel methods to integrate multimodal data, developing scalable tools for multi-site application, and deploying products for validation in clinical trials. His work aims to develop better therapies through quantitative biomarkers and digital twins, understand the natural history of human disease for patient classification and outcome prediction, advance understanding of brain networks, and foster a federated neural-data ecosystem to promote collaboration and data sharing in neurology.

Research topics

• Psychology
• Neuroscience
• Internal medicine
• Medicine

Selected publications

• Penn 3T-7T Paired Epilepsy Imaging

  Pennsieve Discover · 2026-01-01

  datasetOpen access

  This dataset comprises multimodal paired 3T and 7T MRI scans collected from 30 patients with drug-resistant focal epilepsy along with intracranial EEG data.

  PublisherDOI

• Multi-expert consensus annotations of spontaneous and stimulation-induced seizures in stereotactic EEG

  medRxiv · 2026-01-21 · 1 citations

  article

  Automated seizure detection and localization from intracranial EEG requires validated benchmark datasets with expert annotations, yet existing open datasets lack multi-expert consensus annotations and exclude stimulation-induced seizures. We present stereotactic EEG recordings from 83 seizures (46 spontaneous, 37 stimulation-induced) across 32 patients (19 from the University of Pennsylvania, 13 from the Children's Hospital of Philadelphia) with drug-resistant epilepsy. Three board-certified epileptologists independently annotated each seizure for onset time, onset channels, and channels seizing at 10 seconds post-onset using a standardized protocol. Consensus annotations were determined through majority voting. Inter-rater agreement was κ = 0.64 for onset channels and κ = 0.62 for spread channels. Individual rater agreement with consensus was κ = 0.81 for onset and κ = 0.80 for spread. Agreement metrics did not differ between spontaneous and stimulation-induced seizures. All data follow Brain Imaging Data Structure (BIDS) standards and include electrode localizations, patient demographics, and clinical outcomes. This dataset enables the validation of seizure onset and spread detection and localization against human expert performance and supports comparative analysis of seizure networks across spontaneous and stimulation-induced seizures.

  PublisherDOI

• Intrinsic and extrinsic connectivity of the seizure onset zone at rest and during stimulation

  medRxiv · 2026-03-02

  articleOpen access

  , 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.

  PublisherOA PDFDOI

• Prediction of available soil nitrogen using Vis–NIR and MIR spectroscopy coupled with chemometric and machine learning approaches in inceptisols of India

  Soil Advances · 2026-01-07

  articleOpen access

  Soil nitrogen is a fundamental indicator of soil fertility and crop productivity, and its rapid assessment is essential for efficient land management and sustainable agricultural practices. This study investigates the potential of Vis–NIR and MIR spectroscopy for predicting available soil nitrogen collected from the intensively cultivated Indo-Gangetic Plains of India. Soil samples were analyzed using the standard laboratory method and corresponding spectral signatures were processed using multiple preprocessing techniques, among which Standard Normal Variate (SNV) showed the greatest enhancement in model performance. Four regression algorithms Partial Least Squares Regression (PLSR), Random Forest (RF), Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) were calibrated for both the Vis–NIR and MIR spectral domains. Model evaluation demonstrated that MIR spectroscopy combined with PLSR yielded the highest prediction accuracy (R² = 0.84, RMSE = 9.95, RPD = 2.70), outperforming all Vis-NIR models and other machine-learning approaches. RF, MARS and SVR exhibited comparatively lower performance, with RPD values between 1.06 and 1.66. Based on the results, MIR spectroscopy combined with the PLSR algorithm is recommended as a rapid and reliable approach for estimating available nitrogen in Inceptisol soils of the Indo-Gangetic Plains. Adopting this technique can enhance nitrogen management while reducing time, cost and waste for sustainable soil fertility monitoring.

  PublisherDOI

• Unsupervised seizure annotation and detection with neural dynamic divergence

  medRxiv · 2026-02-17 · 1 citations

  articleOpen access

  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.

  PublisherDOI

• Annotating neurophysiologic data at scale with optimized human input

  Journal of Neural Engineering · 2025-06-12 · 7 citations

  articleOpen accessCorresponding

  Abstract 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.

  PublisherDOI

• Harvard Electroencephalography Database: A comprehensive clinical electroencephalographic resource from four Boston hospitals

  Epilepsia · 2025-06-04 · 5 citations

  articleOpen access

  OBJECTIVE: This article presents the Harvard Electroencephalography Database (HEEDB), a large-scale, deidentified, and standardized electroencephalographic (EEG) resource supporting artificial intelligence-driven and reproducible research in epilepsy and broader clinical neuroscience. METHODS: HEEDB aggregates more than 280 000 EEG recordings from more than 108 000 patients across four Harvard-affiliated hospitals. Data are harmonized using the Brain Imaging Data Structure and hosted on the Brain Data Science Platform. EEG data are linked with clinical notes, International Classification of Diseases, 10th Revision codes, medications, and EEG reports. Deidentification follows Health Insurance Portability and Accountability Act Safe Harbor standards. RESULTS: The database includes routine, epilepsy monitoring unit, and intensive care unit EEGs across all age groups, with 73% linked to deidentified clinical reports and 96% of those matched to recordings. Findings are extracted using expert curation, regular expressions, and medical natural language processing models. Auxiliary data include diagnoses, medications, and hospital course, supporting multimodal analysis. SIGNIFICANCE: HEEDB fills a critical gap in EEG data availability for epilepsy research. By enabling large-scale, privacy-compliant, and clinically relevant analysis, it accelerates the development of diagnostic tools, improves training datasets for machine learning, and promotes data-sharing in alignment with FAIR (Findable, Accessible, Interoperable, Reusable) and National Institutes of Health data policies.

  PublisherOA PDFDOI

• Seizure characteristics and outcomes in patients with pleomorphic xanthoastrocytoma

  Neuro-Oncology Advances · 2025-01-01

  articleOpen access

  Abstract Background Pleomorphic xanthoastrocytomas (PXAs) are rare brain tumors that are often associated with seizures. There are limited data characterizing epilepsy phenotypes in relation to PXA tumor biology and survival outcomes. Methods This is a retrospective observational study of 35 patients with PXA who received treatment at the University of Pennsylvania or Dana-Farber Cancer Institute. Demographic and clinical features were assessed in PXA patients with or without seizures and with respect to seizure freedom following tumor resection. Results During their clinical course, 27 (77%) developed tumor-related epilepsy (TRE), with 25 (71%) initially presenting with a seizure. Compared to those without TRE, patients with TRE were more likely to have a BRAF-mutated PXA and less likely to have frontal lobe tumor localization. Patients with TRE who became seizure-free after the initial resection up to the time of progressive disease were found to have a lower age of seizure onset, smaller tumor diameter, and more likely to have BRAF-mutated tumors compared to those who were not seizure-free. However, following the last tumor resection and accounting for tumor recurrences, there were no significant differences in clinical features between those who were seizure-free and those who were not. Overall survival was 88% after 5 years and 59% after 10 years, with similar survival rates between patients with and without TRE. Conclusion These findings indicate that BRAF-mutated and BRAF-wildtype PXAs have distinct epilepsy phenotypes. Further investigation of the interplay between tumor biology and seizures may help guide counseling and targeted therapeutic strategies for PXA-related epilepsy.

  PublisherOA PDFDOI

• Dynamic Interplay Between Wake Slow Waves and Epileptiform Discharges in the Epileptogenic Zone

  Neurology · 2025-08-20 · 4 citations

  articleOpen access

  BACKGROUND AND OBJECTIVES: Outcome of epilepsy surgery remains suboptimal, calling for the identification of new, complementary biomarkers of the epileptogenic zone (EZ). Recently, we identified local wake slow waves (LoWS) as a potential regulator of network excitability that interacts with interictal epileptiform discharges (IEDs). In this study, we tested whether this interaction is associated with surgical outcome. METHODS: In this retrospective study, we analyzed intracranial recordings from patients with intractable focal epilepsy who underwent surgery at the Hospital of the University of Pennsylvania. We used surgical success as an indicator that most or all of the EZ had been resected. We used linear mixed models to test whether the incidence of IEDs and LoWS, as well as their interaction, can accurately delineate the EZ in patients with successful vs poor outcome. RESULTS: < 0.0001), underscoring its potential utility as an additional biomarker of the EZ. DISCUSSION: The temporal proximity of LoWS to a preceding IED in the resected cortex is associated with surgical outcome. This may reflect changes in the regulation of network excitability in the EZ as a form of homeostatic regulation. It raises the possibility to use this index as an additional prognostic biomarker in epilepsy surgery.

  PublisherOA PDFDOI

• Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond

  Scientific Data · 2025-11-19 · 3 citations

  articleOpen access

  The exponential growth of neuroscientific data necessitates platforms for data management and multidisciplinary collaboration. In this paper, we introduce Pennsieve, an open-source, cloud-based scientific data management platform that supports findable, accessible, interoperable, and reusable (FAIR) data sharing. It has integrated tools for data visualization, processing, and peer-reviewed data publishing that promote collaborative research and high-quality datasets optimized for downstream analysis, both in the cloud and on-premises. Pennsieve welcomes data submissions from individual investigators and small labs through entire consortia. It already serves more than 80 research groups worldwide and forms the core for several large-scale, interinstitutional projects and major government neuroscience research programs. Pennsieve stores over 125 TB of scientific data, with 35 TB of data publicly available in more than 350 high-impact datasets. By facilitating scientific data management, discovery, and analysis, Pennsieve fosters a robust and collaborative research ecosystem for neuroscience and beyond.

  PublisherOA PDFDOI

Recent grants

• Scalable methods to quantify epileptic network and guide epilepsy surgery

  NIH · $298k · 2024–2026

Frequent coauthors

• Peter N. Taylor

  Newcastle University

  145 shared

• Yujiang Wang

  Newcastle University

  141 shared

• John S. Duncan

  National Hospital for Neurology and Neurosurgery

  130 shared

• Gavin P. Winston

  National Hospital for Neurology and Neurosurgery

  123 shared

• Jane de Tisi

  National Hospital for Neurology and Neurosurgery

  120 shared

• Sjoerd B. Vos

  University of Western Australia

  115 shared

• Andrew W. McEvoy

  National Hospital for Neurology and Neurosurgery

  114 shared

• Anna Miserocchi

  National Hospital for Neurology and Neurosurgery

  111 shared

Education

• Doctorate of Philosophy (PhD), Institute of Neuroscience, Faculty of Medical Sciences

  Newcastle University

  2021

• Master of Science (MSc), Dynamical Control and Automation

  Nanyang Technological University

  2013

• Bachelor of Technology (B.Tech.), Department of Electrical and Electronics Engineering

  Sikkim Manipal University

  2010