About

Brian Litt, MD, is the Perelman Professor of Neurology at the University of Pennsylvania and an attending neurologist at the Hospital of the University of Pennsylvania. His research expertise encompasses epilepsy, EEG, clinical neurophysiology, epilepsy surgery, brain stimulation, implantable devices, network neuroscience, deep brain stimulation, functional imaging, neuroengineering, biomedical engineering, and computational neuroscience. He has contributed to the development and application of neurotechnologies for studying and treating brain disorders, particularly epilepsy. Dr. Litt holds a background in engineering and applied sciences from Harvard University and earned his MD from Johns Hopkins University School of Medicine. His clinical expertise includes epilepsy, status epilepticus, epilepsy surgery, EEG, intraoperative monitoring, antiepileptic drugs, functional brain mapping, brain stimulation, electrocorticography, evoked potentials, and MEG. He has also been involved in entrepreneurship, helping to found companies such as IntelliMedix, Inc. and Bioquantix, Inc., and has served on scientific advisory boards for multiple companies. His work has led to the licensing of technology to various neurotechnology companies and participation in bringing implantable neurodevices to market.

Research topics

• Medicine
• Artificial Intelligence
• Data Mining
• Natural Language Processing
• Computer Science
• Machine Learning
• Neuroscience
• Internal medicine
• Psychology
• Psychiatry

Selected publications

• Artifact-free, colocalized opto-electrophysiology enabled by a flexible, multimodal interface integrating transparent MXene microelectrodes and microLEDs

  Biosensors and Bioelectronics · 2026-03-13

  articleOpen access
  PublisherOA PDFDOI

• On-demand seizures facilitate rapid screening of therapeutics for epilepsy

  eLife · 2026-04-30

  articleOpen access

  Animal models of epilepsy are critical in drug development and therapeutic testing. However, dominant methods for evaluating epilepsy treatments face a tradeoff between higher throughput and etiological relevance. Screening models are either based on acutely induced seizures in wild-type, naive animals or spontaneous seizures in chronically epileptic animals. Each has its disadvantages – acute convulsant or kindling-induced seizures do not account for the myriad neuropathological changes in the diseased, epileptic brains, and spontaneous behavioral seizures are sparse in chronically epileptic models, making it time-intensive to sufficiently power experiments. In this study, we developed the Opto-IHK (optogenetically induced seizures in intrahippocampal kainate mice) model, a mechanistic approach to precipitate seizures ‘on demand’ in chronically epileptic mice. We briefly synchronized principal cells in the CA1 region of the diseased hippocampus to reliably induce stereotyped on-demand behavioral seizures. These induced seizures resembled naturally occurring spontaneous seizures in the epileptic animals and could be stopped by commonly prescribed anti-seizure medications such as levetiracetam and diazepam. Furthermore, we showed that seizures induced in chronically epileptic animals differed from those in naive animals, highlighting the importance of evaluating therapeutics in the diseased circuit. Taken together, we envision the Opto-IHK model to accelerate the evaluation of both pharmacological and closed-loop interventions for epilepsy.

  PublisherDOI

• Sex Differences in Fall Frequency, Risk Factors, and Outcomes in Parkinson's Disease: A Cross‐Sectional Analysis

  Movement Disorders Clinical Practice · 2026-05-07

  articleOpen access

  BACKGROUND: Female sex is an independent fall risk factor in Parkinson's disease (PD), yet sex-specific fall patterns remain unclear. OBJECTIVES: To compare sex-specific fall risk and outcomes across PD, prodromal alpha-synucleinopathy (PAS), and healthy controls (HC); estimate fall frequency across PD progression; and assess how sex modifies fall risk and outcomes. METHODS: Fall outcomes were analyzed in the Parkinson's Progression Markers Initiative. Yearly rates of rare and frequent falls were estimated by time since diagnosis. PD participants were classified as never, rare, or frequent fallers. Clinical measures included motor, cognitive, behavioral, sleep, and autonomic domains. Outcomes included injuries and healthcare utilization. Regression models adjusted for age, sex, and disease duration with Benjamini-Hochberg correction. RESULTS: Among 3100 participants (937 PD, 1926 PAS, 237 HC; 6977 visits), PD participants had higher odds of falling than PAS (OR 1.66, 95% CI 1.46-1.87) and HC (OR 4.03, 95% CI 3.14-5.23). In PD and PAS, females had higher odds of injuries (OR 1.50, 95% CI 1.20-1.88) and fractures (OR 1.62, 95% CI 1.15-2.29), including hip (OR 2.30, 95% CI 1.09-4.91) and upper-extremity fractures (OR 2.67, 95% CI 1.51-4.85). Within PD, falls increased with disease duration and were higher in females (7 years: 42% vs. 32%; 14 years: 88% vs. 61%) despite milder clinical profiles. Across the Neuronal Synuclein Disease-Integrated Staging System (NSD-ISS) stages, fall occurrence was higher in females. CONCLUSION: Falls increase with disease duration and NSD-ISS stage. Female PD participants are at greater risk despite milder symptoms, supporting sex-specific prevention strategies.

  PublisherDOI

• How Much Does the Reduced EEG Montage Matter for Seizure Detection?: A Large-Cohort Simulation Study

  medRxiv · 2026-05-06

  articleOpen access

  Abstract Importance Implantable sub-scalp EEG systems with a small number of channels have emerged as promising solutions for long-term seizure monitoring in patients with epilepsy. How seizure detection performance varies by montage configuration is unknown. Objective To quantify how automated seizure detection performance differs between full and reduced montages, and how these differences vary by epilepsy characteristics. Design Retrospective cross-sectional study. Setting Single-center at the Hospital of the University of Pennsylvania Epilepsy Monitoring Unit (EMU). Participants EEG data from 2281 consecutive EMU admissions between January 2017 and December 2024 were screened. Admissions with at least one annotated seizure and one interictal clip ≥20 minutes from any seizure were included. Exposure Computational simulation of published sub-scalp device montages using standard 10-20 EEG channels. Main Outcomes and Measures The primary outcome was event-based F1 scores evaluated for three published seizure detectors—a one-class support vector machine (SVM), a convolutional neural network (SPaRCNet), and a long short-term memory autoregressive model (NDD)—across montages. Results A total of 466 admissions from 436 patients (mean [SD] age, 39.0 [14.4] years; 54.4% female) met inclusion criteria, comprising 1683 seizures and 1527 interictal clips. SPaRCNet achieved the highest performance (mean [SD] F1, 0.61 [0.30]), followed by NDD (0.56 [0.28]) and SVM (0.39 [0.25]). Performance decreased by at most 0.09 with reduced montages, depending on detectors. Patient factors accounted for the largest proportion of performance variance (29.2%), followed by detector choice (10.3%). Montage effects were minimal (0.4%), despite variation in optimal montage across detectors. Reduced-montage performance correlated moderately to highly with full-montage performance (ρ=0.29–0.73), suggesting full-montage performance could help identify patients suitable for sub-scalp devices. Missed seizures were associated with lower amplitude and bandpowers than detected seizures, though they remained distinguishable from interictal data. Conclusions and Relevance Automated seizure detection achieved comparable accuracy, with only modest reductions, under simulated reduced montages. Performance differences were driven primarily by detector- and patient-level factors rather than montage. These findings support the feasibility of accurately detecting seizures with published sub-scalp devices and highlight the need for improved algorithms to optimize performance. Key Findings Question How do automated seizure detection algorithms perform with reduced-channel montages simulating published sub-scalp devices? Findings In this retrospective cross-sectional study, seizure detection performance decreased only modestly on reduced montages relative to the full montage (absolute F1 change −0.09 to 0.014), whereas patient- and algorithm-level factors accounted for most of performance variance (29.2% and 10.3%, respectively). Algorithm performance on full montage recordings was moderately correlated with performance on reduced channel montages (ρ=0.29–0.73). Meaning Reduced-montage sub-scalp devices are promising for ultra-long-term monitoring, but best performance requires selecting the right patients. Patient-specific seizure detectors will likely be required to optimize long-term performance.

  PublisherOA PDFDOI

• Mesial‐to‐lateral gradients of epileptiform activity to localize mesial temporal lobe epilepsy

  Epilepsia · 2025-05-19 · 3 citations

  articleOpen access

  OBJECTIVE: Mesial temporal lobe epilepsy is a common localization of drug-resistant epilepsy in adults. Patients often undergo intracranial electroencephalographic 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 multielectrode sequences may inform surgical planning is unknown, in part due to the lack of measurement methods for large datasets. We hypothesized that patients with mesial temporal lobe epilepsy have a distinct mesial-to-lateral spike gradient that differentiates them from other epilepsy localizations. METHODS: 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 in 75 patients with drug-resistant epilepsy. We compared these features across patients with mesial temporal lobe epilepsy, temporal neocortical epilepsy, and other localizations. RESULTS: A logistic regression model combining all features predicted a clinical localization of mesial temporal lobe epilepsy in unseen patients with an area under the receiver operating characteristic curve of .82 (compared to an area under the receiver operating characteristic curve of .70 for seizure-only features, DeLong test p = .08) and an average precision of .84. Spike rate was the most important feature in the combined model. SIGNIFICANCE: These findings advance surgical planning by demonstrating that quantitative spike analysis can effectively supplement seizure data in localizing mesial temporal lobe epilepsy. This approach could reduce reliance on prolonged seizure monitoring, potentially decreasing patient risk and hospitalization costs while improving surgical targeting. Our results support incorporating automated spike analysis into standard clinical workflows for epilepsy surgery evaluation.

  PublisherDOI

• 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

• Annotating neurophysiologic data at scale with optimized human input

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

  articleOpen accessSenior authorCorresponding

  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

• 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

• Normative intracranial EEG highlights epileptic abnormalities across wakefulness and sleep

  medRxiv · 2025-09-12

  preprintOpen accessSenior author

  Abstract There is great interest in using quantitative methods to localize epileptic networks from intracranial EEG, a vital part of care for patients with drug resistant epilepsy (DRE). In particular, there is evidence that using interictal data for this purpose, which could eliminate the need to record seizures, has great potential to reduce morbidity from precipitated seizures and to decrease length of stay. How much of this data is required for this purpose, and from what state(s) of consciousness, is not known. In this study we analyzed interictal intracranial EEG (iEEG) data from 30 subjects and compared it against normative reference iEEG derived from 106 additional patients. We summarized brain activity and connectivity by computing spectral power and coherence in 6 frequency bands and computed z -scores relative to normal features within the same anatomical region. We used a validated algorithm to estimate the sleep or wake state. We applied a cross-validated random forest model to assign predicted abnormality value to each channel for each state of wakefulness. To determine the effectiveness of this approach for each unseen patient, we computed the area under the precision recall curve (AUPRC) between predicted abnormality within and outside of the resection zone. We further identified associations between predicted abnormalities and neuropsychological testing performance, highlighting applications of quantitative biomarkers to epilepsy comorbidities. We found that subjects with good seizure outcome (Engel 1) at 2 years had higher AUPRC than subjects with poor seizure outcome for predicted abnormalities in N2 sleep (Mann-Whitney test; p Holm=Bonferroni &lt; 0.05). Combining features from wakefulness and NREM sleep best separated good and poor seizure outcome subgroups (Mann-Whitney test; p Holm=Bonferroni &lt; 0.05, Cohen’s d = 1.62). Combinations of wake and sleep abnormalities and interictal spikes explained the variance in pre-surgical neuropsychological testing ( R 2 = 0.57-0.58).

  PublisherOA PDFDOI

• Epileptiform Activity and Seizure Risk Follow Long‐Term Non‐Linear Attractor Dynamics

  Advanced Science · 2025-04-07

  articleOpen access

  Many biological systems display circadian and slow multi-day rhythms, such as hormonal and cardiac cycles. In patients with epilepsy, these cycles also manifest as slow cyclical fluctuations in seizure propensity. However, such fluctuations in symptoms are consequences of the complex interactions between the underlying physiological, pathophysiological, and external causes. Therefore, identifying an accurate model of the underlying system that governs the multi-day rhythms allows for a more reliable seizure risk forecast and targeted interventions. The primary aim is to develop a personalized strategy for inferring long-term trajectories of epileptiform activity and, consequently, seizure risk for individual patients undergoing long-term ECoG sampling via implantable neurostimulation devices. To achieve this goal, the Hankel alternative view of Koopman (HAVOK) analysis is adopted to approximate a linear representation of nonlinear seizure propensity dynamics. The HAVOK framework leverages Koopman theory and delay-embedding to decompose chaotic dynamics into a linear system of leading delay-embedded coordinates driven by the low-energy coordinate (i.e., forcing). The findings reveal the topology of attractors underlying multi-day seizure cycles, showing that seizures tend to occur in regions of the manifold with strongly nonlinear dynamics. Moreover, it is demonstrated that the identified system driven by forcings with short periods up to a few days accurately predicts patients' slower multi-day rhythms, which improves seizure risk forecasting.

  PublisherOA PDFDOI

Recent grants

• Guiding epilepsy surgery using network models and Stereo EEG

  NIH · $2.8M · 2022–2026

• NIH Grant R01NS048598

  NIH · $2.2M · 2011

• Training Program in Neuroengineering and Medicine

  NIH · $3.0M · 2015–2030

• Virtual Resection to Treat Epilepsy

  NIH · $1.7M · 2016–2021

• NIH Grant U24NS06393005

  NIH · $1.1M · 2016

Frequent coauthors

• Kathryn A. Davis

  Florida State University

  178 shared

• Danielle S. Bassett

  McGill University

  103 shared

• Timothy H. Lucas

  Neurotech (United States)

  81 shared

• Gregory A. Worrell

  WinnMed

  62 shared

• Erin C. Conrad

  California University of Pennsylvania

  49 shared

• Joel M. Stein

  University of Pennsylvania

  39 shared

• Ankit N. Khambhati

  39 shared

• John M. Bernabei

  Neurological Surgery

  38 shared

Education

• AB, Engineering and Applied Sciences

  Harvard University

  1982