Biology subjects

Vargas-Irwin, C. E.

Publications and source records attributed to Vargas-Irwin, C. E..

5 recordsLinked to original sources

bioRxiv2026

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

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 agents 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 STATEMENTThe 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.

neuroscience↗

bioRxiv2024

Gesture encoding in human left precentral gyrus neuronal ensembles

Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of [~]70% for all 48 gestures (and [~]90% for sets of 10). Our results show that single unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.

neuroscience↗

bioRxiv2024

Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces

Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.

neuroscience↗

bioRxiv2020

The neural representation of force across grasp types in motor cortex of humans with tetraplegia

Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to force and grasp. Demixed principal component analysis was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: 1) Force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); 2) Grasp type affected force representation within multi-unit neural features and offline force classification accuracy; and 3) Grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type. Significance StatementIntracortical brain-computer interfaces (iBCIs) have emerged as a promising technology to potentially restore hand grasping and object interaction in people with tetraplegia. This study is among the first to quantify the degree to which hand grasp affects force-related - or kinetic - neural activity and decoding performance in individuals with tetraplegia. The study results enhance our overall understanding of how the brain encodes kinetic parameters across varying kinematic behaviors -- and in particular, the degree to which these parameters have independent versus interacting neural representations. Such investigations are a critical first step to incorporating force control into human-operated iBCI systems, which would move the technology towards restoring more functional and naturalistic tasks.

neuroscience↗

bioRxiv2018

SIMNETS: a computationally efficient and scalable framework for identifying networks of functionally similar neurons

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 SIMNETS, a mathematically rigorous and efficient unsupervised relational analysis framework designed to generate intuitive, low-dimensional neuron maps that support a multi-scale view of the computational similarity (CS) relations among individual neurons. The critical innovation is the use of a novel measure of computational similarity that is based on comparing the intrinsic structure of latent spaces representing the spiking output of individual neurons. 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 can facilitate linking computational geometry representations across scales, from single neurons to subnetworks, within large-scale neural recording data.

neuroscience↗