About

John Donoghue is the Henry Merritt Wriston Professor of Neuroscience and Professor of Engineering at Brown University. He is recognized for pioneering work in developing brain-computer interfaces, which enable the restoration of voluntary communication and limb function in people with paralysis. His contributions to engineering and neuroscience have been acknowledged with the Queen Elizabeth Prize for Engineering, one of the most prestigious honors in the field. His research focuses on advancing understanding and technology related to brain-machine interfaces, aiming to improve the quality of life for individuals with neurological impairments.

Research topics

• Medicine
• Physical medicine and rehabilitation
• Internal medicine
• Surgery

Selected publications

• Observation-Related Activity in Human Motor Cortex Increases with Effector Anthropomorphicity

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-28

  articleOpen access

  ABSTRACT Neurons in motor cortex can be engaged not only in motor execution but also during observation of movements performed by other anthropomorphic agents (i.e. humans or monkeys). However, it is unknown how motor cortical neurons respond during observation of the range of assistive or prosthetic devices controlled by people using intracortical brain-computer interfaces (iBCIs). We recorded single-unit activity in the precentral gyrus while iBCI users viewed grasp-like movements performed by a spectrum of virtual effectors that included human, robotic, and hand-like dot stimuli. We found a relationship between neural modulation and effector anthropomorphicity (i.e. human-likeness) that existed on an ensemble-wide and individual neuron level, suggesting that human motor cortex activity incrementally increases in response to the visually observed agent’s human-likeness. Both solicited and spontaneous feedback from the participant indicated a relationship between neural activity and subjective assessments of anthropomorphicity, revealing a powerful contribution of context on observation-induced activity in motor cortex. The activity of motor cortex remained similar during attempted hand movements while different effectors were being observed, suggesting that intuitive external device control via iBCIs may not be overtly affected by the anthropomorphicity of the effector. SIGNIFICANCE STATEMENT The tendency for neurons in motor cortex to respond during movement observation has been proposed to underlie cognitive processes from motor learning and language development to empathy and theory of mind. Understanding how the motor cortex is engaged during observation of abstract and anthropomorphic agents informs our understanding of these processes and may guide development of neural prostheses which harness the activity of motor cortical neurons to restore lost neurologic function. Here we provide unique neuron-level evidence that human motor cortex activity is gradually modulated by how human-like an observed agent appears and moves. This finding advances our interpretation of “mirror” activity in the brain and could help guide the design of brain-controlled prostheses used by people with tetraplegia.

  PublisherOA PDFDOI

• Mapping the computational similarity of individual neurons within large-scale ensemble recordings using the SIMNETS analysis framework

  Frontiers in Neuroscience · 2025-08-14 · 1 citations

  articleOpen accessSenior author

  The expansion of large-scale neural recording capabilities has provided new opportunities to examine multi-scale cortical network activity at single neuron resolution. At the same time, the growing scale and complexity of these datasets introduce new conceptual and technical challenges beyond what can be addressed using traditional analysis techniques. Here, we present the Similarity Networks (SIMNETS) analysis framework: an efficient and scalable pipeline designed to embed simultaneously recorded neurons into low dimensional maps according to the intrinsic relationship between their spike trains, making it possible to identify and visualize groups of neurons performing similar computations. The critical innovation is the use of pairwise spike train similarity (SSIM) matrices to capture the intrinsic relationship between the spike trains emitted by a neuron at different points in time (i.e., different experimental conditions), reflecting how the neuron responds to time-varying internal and external drives and making it possible to easily compare the information processing properties across neuronal populations. We use three publicly available neural population test datasets from the visual, motor, and hippocampal CA1 brain regions to validate the SIMNETS framework and demonstrate how it can be used to identify putative subnetworks (i.e., clusters of neurons with similar computational properties). Our analysis pipeline includes a novel statistical test designed to evaluate the likelihood of detecting spurious neuron clusters to validate network structure results. The SIMNETS framework provides a way to rapidly examine the computational structure of neuronal networks at multiple scales based on the intrinsic structure of single unit spike trains.

  PublisherOA PDFDOI

• Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants

  medRxiv · 2025-07-02 · 8 citations

  preprintOpen access

  Brain-computer interfaces have enabled people with paralysis to control computer cursors, operate prosthetic limbs, and communicate through handwriting, speech, and typing. Most high-performance demonstrations have used silicon microelectrode "Utah" arrays to record brain activity at single neuron resolution. However, reports so far have typically been limited to one or two individuals, with no systematic assessment of the longevity, decoding accuracy, and day-to-day stability properties of chronically implanted Utah arrays. Here, we present a comprehensive evaluation of 20 years of neural data from the BrainGate and BrainGate2 pilot clinical trials. This dataset spans 2,319 recording sessions and 20 arrays from the first 14 participants in these trials. On average, arrays successfully recorded neural spiking waveforms on 35.6% of electrodes, with only a 7% decline over the study enrollment period (up to 7.6 years, with a mean of 2.8 years). We assessed movement intention decoding performance using a "decoding signal-to-noise ratio" (dSNR) metric, and found that 11 of 14 arrays provided meaningful movement decoding throughout study enrollment (dSNR > 1). Three arrays reached a peak dSNR greater than 4.5, approaching that achieved during able-bodied computer mouse control (6.29). We also found that dSNR increases logarithmically with the number of electrodes, providing a pathway for scaling performance. Longevity and reliability of Utah array recordings in this study were better than in prior nonhuman primate studies. However, achieving peak performance consistently will require addressing unknown sources of variability.

  PublisherOA PDFDOI

• Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design

  Frontiers in Human Neuroscience · 2025-02-21

  articleOpen access

  Introduction: Intracortical Brain-computer interfaces (iBCIs) are a promising technology to restore function after stroke. It remains unclear whether iBCIs will be able to use the signals available in the neocortex overlying stroke affecting the underlying white matter and basal ganglia. Methods: Here, we decoded both local field potentials (LFPs) and spikes recorded from intracortical electrode arrays in a person with chronic cerebral subcortical stroke performing various tasks with his paretic hand, with and without a powered orthosis. Analysis of these neural signals provides an opportunity to explore the electrophysiological activities of a stroke affected brain and inform the design of medical devices that could restore function. Results: The frequency domain analysis showed that as the distance between an array and the stroke site increased, the low frequency power decreased, and high frequency power increased. Coordinated cross-channel firing of action potentials while attempting a motor task and cross-channel simultaneous low frequency bursts while relaxing were also observed. Using several offline analysis techniques, we propose three features for decoding motor movements in stroke-affected brains. Discussion: Despite the presence of unique activities that were not reported in previous iBCI studies with intact brain functions, it is possible to decode motor intents from the neural signals collected from a subcortical stroke-affected brain.

  PublisherOA PDFDOI

• Feasibility, Safety, and Performance of Full-Head Subscalp EEG Using Minimally Invasive Electrode Implantation

  Neurology · 2024-06-06 · 10 citations

  articleOpen access

  BACKGROUND AND OBJECTIVES: Current practice in clinical neurophysiology is limited to short recordings with conventional EEG (days) that fail to capture a range of brain (dys)functions at longer timescales (months). The future ability to optimally manage chronic brain disorders, such as epilepsy, hinges upon finding methods to monitor electrical brain activity in daily life. We developed a device for full-head subscalp EEG (Epios) and tested here the feasibility to safely insert the electrode leads beneath the scalp by a minimally invasive technique (primary outcome). As secondary outcome, we verified the noninferiority of subscalp EEG in measuring physiologic brain oscillations and pathologic discharges compared with scalp EEG, the established standard of care. METHODS: Eight participants with pharmacoresistant epilepsy undergoing intracranial EEG received in the same surgery subscalp electrodes tunneled between the scalp and the skull with custom-made tools. Postoperative safety was monitored on an inpatient ward for up to 9 days. Sleep-wake, ictal, and interictal EEG signals from subscalp, scalp, and intracranial electrodes were compared quantitatively using windowed multitaper transforms and spectral coherence. Noninferiority was tested for pairs of neighboring subscalp and scalp electrodes with a Bland-Altman analysis for measurement bias and calculation of the interclass correlation coefficient (ICC). RESULTS: As primary outcome, up to 28 subscalp electrodes could be safely placed over the entire head through 1-cm scalp incisions in a ∼1-hour procedure. Five of 10 observed perioperative adverse events were linked to the investigational procedure, but none were serious, and all resolved. As a secondary outcome, subscalp electrodes advantageously recorded EEG percutaneously without requiring any maintenance and were noninferior to scalp electrodes for measuring (1) variably strong, stage-specific brain oscillations (alpha in wake, delta, sigma, and beta in sleep) and (2) interictal spikes peak-potentials and ictal signals coherent with seizure propagation in different brain regions (ICC >0.8 and absence of bias). DISCUSSION: Recording full-head subscalp EEG for localization and monitoring purposes is feasible up to 9 days in humans using minimally invasive techniques and noninferior to the current standard of care. A longer prospective ambulatory study of the full system will be necessary to establish the safety and utility of this innovative approach. TRIAL REGISTRATION INFORMATION: clinicaltrials.gov/study/NCT04796597.

  PublisherOA PDFDOI

• An application-based taxonomy for brain–computer interfaces

  Nature Biomedical Engineering · 2024-12-23 · 14 citations

  articleOpen access
  PublisherOA PDFDOI

• Low frequency independent components: Internal neuromarkers linking cortical LFPs to behavior

  iScience · 2023-10-28 · 7 citations

  articleOpen access

  Local field potentials (LFPs) in the primate motor cortex have been shown to reflect information related to volitional movements. However, LFPs are composite signals that receive contributions from multiple neural sources, producing a complex mix of component signals. Using a blind source separation approach, we examined the components of neural activity recorded using multielectrode arrays in motor areas of macaque monkeys during a grasping and lifting task. We found a set of independent components in the low-frequency LFP with high temporal and spatial consistency associated with each task stage. We observed that ICs often arise from electrodes distributed across multiple cortical areas and provide complementary information to external behavioral markers, specifically in task stage detection and trial alignment. Taken together, our results show that it is possible to separate useful independent components of the LFP associated with specific task-related events, potentially representing internal markers of transition between cortical network states.

  PublisherDOI

• Mapping the Computational Similarity of Individual Neurons within Large-Scale Ensemble Recordings Using the SIMNETS Analysis Framework

  SSRN Electronic Journal · 2023-01-01

  preprintOpen access
  PublisherDOI

• Broader Estimates of Gastrocnemius Activity Generated a More Representative Cocontraction Index: A Study in Pediatric Population

  IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2023-01-01 · 2 citations

  articleOpen access

  The electromyography (EMG) cocontraction index (CCI) given by the antagonistic/agonistic Root Mean Square (RMS) amplitude ratio of the same muscle is a qualified biomarker used for spastic cocontraction quantification and management in cerebral palsy children. However, this normative EMG ratio is likely subject to a potential source of errors with biased estimates when measuring the gastrocnemius plantar flexors activity. Due to the uneven distribution of electrical activity within the muscle volume, cocontraction levels can be misestimated, if EMGs are obtained from the sole traditional bipolar sensor location recommended by SENIAM. This preliminary study, on 10 healthy children (mean age 10 yr), investigated whether surface EMG detected proximally and distally via two pairs of bipolar electrodes, within the medial gastrocnemius (MG), provides a significant difference in CCI estimates during non-dynamic (isometric dorsiflexion) and dynamic (swing phases of gait) conditions. Gait cycles were extracted from Inertial Measurement Unit sensors. Medial gastrocnemius activity was greater distally than proximally during plantar flexion when it acts as an agonist (~24±18%) and it was greater proximally during dorsiflexion (~23±9%) when it is acting as an antagonist. As a direct consequence, CCI estimates from the conventional sensor location were significantly different (~36%) from the CCIs computed by considering broader MG regions. This difference arose in all subjects during isometric efforts and in two of 10 healthy children during the swing phase of gait who presented cocontraction patterns ( [Formula: see text]). EMG bipolar sampling encompassing proximal and distal gastrocnemius muscle regions may reduce bias in CCI computation and provide a more representative and accurate cocontraction index that is especially important for comparisons to the diseased state.

  PublisherOA PDFDOI

• Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System

  Neurology · 2023 · 71 citations

  • Medicine
  • Physical medicine and rehabilitation
  • Surgery

  BACKGROUND AND OBJECTIVES: Brain-computer interfaces (BCIs) are being developed to restore mobility, communication, and functional independence to people with paralysis. Though supported by decades of preclinical data, the safety of chronically implanted microelectrode array BCIs in humans is unknown. We report safety results from the prospective, open-label, nonrandomized BrainGate feasibility study (NCT00912041), the largest and longest-running clinical trial of an implanted BCI. METHODS: Adults aged 18-75 years with quadriparesis from spinal cord injury, brainstem stroke, or motor neuron disease were enrolled through 7 clinical sites in the United States. Participants underwent surgical implantation of 1 or 2 microelectrode arrays in the motor cortex of the dominant cerebral hemisphere. The primary safety outcome was device-related serious adverse events (SAEs) requiring device explantation or resulting in death or permanently increased disability during the 1-year postimplant evaluation period. The secondary outcomes included the type and frequency of other adverse events and the feasibility of the BrainGate system for controlling a computer or other assistive technologies. RESULTS: From 2004 to 2021, 14 adults enrolled in the BrainGate trial had devices surgically implanted. The average duration of device implantation was 872 days, yielding 12,203 days of safety experience. There were 68 device-related adverse events, including 6 device-related SAEs. The most common device-related adverse event was skin irritation around the percutaneous pedestal. There were no safety events that required device explantation, no unanticipated adverse device events, no intracranial infections, and no participant deaths or adverse events resulting in permanently increased disability related to the investigational device. DISCUSSION: The BrainGate Neural Interface system has a safety record comparable with other chronically implanted medical devices. Given rapid recent advances in this technology and continued performance gains, these data suggest a favorable risk/benefit ratio in appropriately selected individuals to support ongoing research and development. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT00912041. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that the neurosurgically placed BrainGate Neural Interface system is associated with a low rate of SAEs defined as those requiring device explantation, resulting in death, or resulting in permanently increased disability during the 1-year postimplant period.

  PublisherDOI

Recent grants

• NIH Grant R37NS025074

  NIH · $3.4M · 2011

• NIH Grant R01NS022517

  NIH · $2.6M · 2003

• NIH Grant R03MH040972

  NIH · $19k · 1986

• Static and Dynamic Organization of Primate Motor Cortex

  NIH · $6.8M · 1987–2018

• NIH Grant S10OD016366

  NIH · $600k · 2014

Frequent coauthors

• Leigh R. Hochberg

  Harvard University

  587 shared

• John D. Simeral

  251 shared

• Wilson Truccolo

  Providence College

  161 shared

• Carlos E. Vargas-Irwin

  John Brown University

  110 shared

• Donna Hourican

  University of Nottingham

  108 shared

• Dawn Melley

  108 shared

• Jeffrey Cichocki

  Institute of Electrical and Electronics Engineers

  108 shared

• Kevin Lisankie

  Institute of Electrical and Electronics Engineers

  108 shared

Awards & honors

• Queen Elizabeth Prize for Engineering (2026)