About

Bijan Pesaran, PhD, is a Professor of Teaching and Research in Neurosurgery II at the University of Pennsylvania's Perelman School of Medicine. His research expertise encompasses systems neuroscience, sensory-motor integration, neural engineering, brain-machine interfaces, electrophysiology, multiphoton microscopy, and neural data science. He holds a B.A. with Honors in Physics and Theoretical Physics from the University of Cambridge and a PhD in Physics from the California Institute of Technology. His work involves understanding neural mechanisms through advanced electrophysiological techniques and developing neural interface technologies. Pesaran's research includes the design of surgical approaches for deep brain stimulation in treatment-refractory obsessive-compulsive disorder, real-time multimodal behavioral and electrophysiological data capture systems, and mapping intraoperative epileptiform discharges using high-resolution cortical arrays. His contributions extend to localizing electrophysiologic cue-reactivity within the nucleus accumbens to guide deep brain stimulation for opioid use disorder, and advancing microelectrode design for supracortical microstimulation. His research aims to elucidate neural dynamics and develop innovative interventions for neurological and psychiatric conditions.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Telecommunications
• Neuroscience
• Materials science
• Nanotechnology
• Optoelectronics
• Biomedical engineering
• Statistics
• Mathematics

Selected publications

• 2148 Quantifying Traveling Waves in the Precentral Gyrus Using a High-Density Micro-Electrocorticography Array

  Neurosurgery · 2026-03-26

  article
  PublisherDOI

• Mapping intraoperative interictal epileptiform discharges using high‐resolution, thin‐film cortical arrays

  Epilepsia · 2026-02-26

  article

  OBJECTIVE: Interictal epileptiform discharges (IEDs) are transients observed on the electroencephalogram (EEG) of patients with epilepsy. IEDs have traditionally been recorded from scalp or intracranial EEG macrocontacts, which coarsely sample neural activity. Here, we investigated the use of flexible, high-resolution microelectrocorticographic (μECoG) arrays for measuring IEDs with greater spatiotemporal precision to test whether there exist microscale patterns of IED activity that may be missed on standard intracranial EEG. METHODS: ) to record from seven patients undergoing surgical treatment of epilepsy. We identified IEDs by a combination of expert review and automated detection. We quantified the spatial extent of IEDs, mapped patterns of repeated IED activity, and quantified IED propagation direction using multilinear fit models. We also compared IED detection rates and propagation measurements between μECoG arrays and simulated macroarrays (10-mm spacing, 2.3-mm diameter). RESULTS: We demonstrated successful use of μECoG arrays to map intraoperative microscale patterns of IEDs. The majority of patients (5/7) exhibited elevated IED activity that was highly localized (subcentimeter localization). Across all patients, 40% of detected IEDs were observed within a 4-mm radius of cortex. μECoG arrays also mapped the direction of IED propagation. An average of 39% (range = 4.2%-96.5%, SD = ±36.8%) of the IED events captured by the μECoG arrays were not detectable by simulated macrocontacts. SIGNIFICANCE: These intraoperative data demonstrate that μECoG arrays can map the microscale spatiotemporal activity of IEDs. These patterns of IEDs may be poorly captured by standard, macroscale recording devices. Our findings support the use of high-resolution, large area coverage μECoG arrays for the presurgical and intraoperative mapping of epileptic cortex.

  PublisherDOI

• Thalamus: a real-time system for synchronized, closed-loop multimodal behavioral and electrophysiological data capture

  Communications Engineering · 2026-03-26

  articleOpen access

  Precise and synchronized multimodal data capture in neurosurgical environments is essential for further understanding brain function and will be crucial to advancing the development of brain-computer interface technology. We have developed an open-source software platform named Thalamus, for multimodal data capture integrated with existing sensors and hardware commonly utilized in the operating room and other clinical environments such as pulse oximeters, inertial sensors, electromyography and neural electrophysiology. Thalamus facilitates synchronous recording of neural and behavioral data, enabling real-time computation for closed-loop experiments and detailed analysis of complex motor functions and neural activity. Thalamus uses a modular, configurable node-based pipeline with a tiered Python and C + + architecture. These design elements allow Thalamus to support a wide range of high-resolution sensors for diverse behavioral data types and enable robust closed-loop synchronization of various data streams. Validation experiments demonstrate that Thalamus is capable of data integration and concurrent analysis with up to sub-millisecond precision, offering great potential for enhancing neurosurgical research and clinical applications. By leveraging conventional sensors and hardware already in use, Thalamus supports adoption into the clinical environment, paving the way for more comprehensive, data-driven approaches to neurological care and improving the personalization and rigor of treatment strategies.

  PublisherDOI

• Human orbitofrontal neural activity is linked to obsessive-compulsive behavioral dynamics

  Cell · 2026-01-29 · 2 citations

  articleOpen access

  Biomarkers of obsessive-compulsive disorder (OCD) symptom dynamics and related behavior could advance personalized interventions. Aberrant activity in the orbitofrontal cortex (OFC) has been implicated in symptom exacerbation in OCD. We conducted an intracranial monitoring assay to identify high-resolution neurophysiologic correlates of OCD symptoms in the human OFC. We found that low-gamma power in the anteromedial OFC was consistently elevated during high symptom states in a symptom provocation task. Furthermore, electrical stimulation of the ventral basal ganglia that reduced OCD symptoms also reduced anteromedial OFC gamma power. These results link OFC gamma activity to moment-to-moment expression of OCD symptoms, providing mechanistic insights to guide therapeutic strategies such as deep brain stimulation.

  PublisherOA PDFDOI

• Stereo-encephalography-guided multi-lead deep brain stimulation for treatment-refractory obsessive compulsive disorder – Study design and individualized surgical targeting approach

  Journal of Affective Disorders · 2026-02-05

  article
  PublisherDOI

• 2151 Preferential Subspace Identification Allows for Low Dimensional Continuous Decoding of Finger Speed From Human M1 ECoG Recordings

  Neurosurgery · 2026-03-26

  article
  PublisherDOI

• Electrographic cue-reactivity co-localizes with accumbens deep brain stimulation in a case of opioid use disorder

  Nature Communications · 2026-01-29

  articleOpen access

  Opioid use disorder (OUD) is a significant public health concern, with over 30% of the affected population not responding to available treatments. Severe OUD is characterized by drug-cue reactivity that has been reported to predict treatment failure. We leveraged this pathophysiological feature to optimize deep brain stimulation (DBS) of the nucleus accumbens region (NAc) in a male patient with OUD. A personalized drug-cue-reactivity task was administered while recording NAc electrophysiology from a lead externalized for clinical purposes. We identified a drug-cue-evoked electrophysiological signal in the ventral NAc that was associated with an elevated craving state and attenuated with stimulation delivered to the same area. This electrophysiological biomarker, along with behavioral assessments, informed the re-programming of DBS to a more focal and effective stimulation site. This resulted in sustained suppression of drug-related cravings. This study represents a proof-of-principle for a personalized, biomarker-informed neuromodulation strategy in OUD.

  PublisherDOI

• Microscale organization and separability of upper extremity representations in the human motor homunculus

  Research Square · 2026-04-29

  preprintOpen access
  PublisherDOI

• Functional populations in prefrontal cortex related to working memory encoding and maintenance

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-29

  preprintOpen access

  Abstract Nonlinear mixed selectivity, with neurons responding to diverse combinations of task-relevant variables, has been proposed as a key mechanism to enable flexible behavior and cognition. However, it is debated whether the structure of neural population responses in fronto-parietal cortices is better described as random mixed-selective or as non-random, that is, in terms of multiple subpopulations with stereotypical response profiles. Here, we show that neural activity in the macaque prefrontal cortex during a working memory and a visual response task is organized into subpopulations that provide a comprehensive description of the low-dimensional population dynamics. First, analysis of the demixed Principal Components shows that the neural code faithfully represents stimulus identity, task condition, and elapsed time during the trial. Second, a model-free analysis of the population structure reveals a significant degree of clustering, implying a non-random distribution of feature selectivity that is incompatible with random mixed selectivity. Closer inspection shows that stimulus-selective neurons also tend to be task-selective. Third, examining the contribution of stimulus-selective neurons to task-condition-related variance reveals two contrasting activity profiles that correspond to functionally different populations. One population responds during visual stimulation while the other activates during memory maintenance. Finally, the observed neural geometry explains how stable task and stimulus information can be read out from the population response using a linear decoder. Our results highlight that despite the heterogeneity of prefrontal responses during working memory, neurons do not represent random mixtures of task features but are structured according to neural subpopulations.

  PublisherOA PDFDOI

• Supracortical Microstimulation: Advances in Microelectrode Design and In Vivo Validation

  Annual Review of Biomedical Engineering · 2025-02-06 · 2 citations

  reviewOpen access

  Electrical stimulation of the brain is being developed as a treatment for an increasing number of neurological disorders. Technologies for delivering electrical stimulation are advancing rapidly and vary in specificity, coverage, and invasiveness. Supracortical microstimulation (SCMS), characterized by microelectrode contacts placed on the epidural or subdural cortical surface, achieves a balance between the advantages and limitations of other electrical stimulation technologies by delivering spatially precise activation without disrupting the integrity of the cortex. However, in vivo experiments involving SCMS have not been comprehensively summarized. Here, we review the field of SCMS, focusing on recent advances, to guide the development of clinically translatable supracortical microelectrodes. We also highlight the gaps in our understanding of the biophysical effects of this technology. Future work investigating the unique electrochemical properties of supracortical microelectrodes and validating SCMS in nonhuman primate preclinical studies can enable rapid clinical translation of innovative treatments for humans with neurological disorders.

  PublisherDOI

Recent grants

• NIH Grant U01NS099697

  NIH · $3.5M · 2020

• NIH Grant U01NS103518

  NIH · $2.9M · 2022

• Core Vision Grant - Design & Fabrication

  NIH · $12.7M · 2000–2026

• NIH Grant R01EY024067

  NIH · $1.9M · 2019

• Predictive models of brain dynamics during decision making and their validation using distributed optogenetic stimulation

  NIH · $3.5M · 2017–2023

Frequent coauthors

• Yan T. Wong

  University of Washington

  39 shared

• Agrita Dubey

  California University of Pennsylvania

  30 shared

• Maureen A. Hagan

  Australian Regenerative Medicine Institute

  27 shared

• Katie E. Wingel

  California University of Pennsylvania

  26 shared

• John Choi

  New York University

  22 shared

• Maryam M. Shanechi

  University of Southern California

  21 shared

• Partha P. Mitra

  Semler Research Center (India)

  19 shared

• Richard A. Andersen

  California Institute of Technology

  17 shared

Labs

• Pesaran LabPI

Education

• PhD, Physics

  California Institute of Technology

  2002

• BA (Hons), Physics and Theoretical Physics

  University of Cambridge

  1995