Biology subjects

Coltman, S. K.

Publications and source records attributed to Coltman, S. K..

4 recordsLinked to original sources

bioRxiv2026

fNIRS Characterization of Temporal Potentiation During Optimized Nerve Stimulation

Restoring stable somatosensory feedback through transcutaneous nerve stimulation (TNS) represents a significant challenge in neural engineering; however, the cortical dynamics underlying sensory habituation are not yet well characterized. This study utilizes a multimethod functional near-infrared spectroscopy framework to quantify the temporal evolution of cortical engagement during optimized TNS. It was hypothesized that stimulation parameters fundamentally constrain the spatiotemporal profile of the hemodynamic response function. A General Linear Model with temporal and dispersion derivatives was used to analyze hemodynamic responses in 10 participants across three phases: validation, duration optimization (2, 5, or 10 seconds), and sustained engagement (45 minutes). During the optimization phase, 5-second stimulation produced the highest canonical amplitude with optimal dispersion characteristics, while 10-second trains resulted in significant temporal delays. During the sustained phase, parametric modeling indicated no linear decay in cortical response. Instead, the cortex demonstrated temporal potentiation, characterized by a transition from sparse early-phase activation to widespread late-phase recruitment across the sensorimotor network. The left posterior parietal cortex (BA5) served as a continuous integration hub, whereas primary somatosensory regions exhibited dynamic late-onset engagement. These findings quantify a nonlinear adaptation mechanism whereby engineered temporal spacing prevents neural saturation. The observed intersubject heterogeneity highlights the necessity for adaptive, closed-loop TNS controllers guided by real-time hemodynamic biomarkers.

neuroscience↗

bioRxiv2024

A novel biomechanical model of the mouse forelimb predicts muscle activity in optimal control simulations of reaching movements

Mice are key model organisms in neuroscience and motor systems physiology. Fine motor control tasks performed by mice have become widely used in assaying neural and biophysical motor system mechanisms. Although fine motor tasks provide useful insights into behaviors which require complex multi-joint motor control, there is no previously developed physiological biomechanical model of the adult mouse forelimb available for estimating kinematics nor muscle activity or kinetics during behaviors. Here, we developed a musculoskeletal model based on high-resolution imaging of the mouse forelimb that includes muscles spanning the neck, trunk, shoulder, and limbs. Physics-based optimal control simulations of the forelimb model were used to estimate in vivo muscle activity present when constrained to the tracked kinematics during reaching movements. The activity of a subset of muscles was recorded and used to assess the accuracy of the muscle patterning in simulation. We found that the synthesized muscle patterning in the forelimb model had a strong resemblance to empirical muscle patterning, suggesting that our model has utility in providing a realistic set of estimated muscle excitations over time when given a kinematic template. The strength of the similarity between empirical muscle activity and optimal control predictions increases as mice performance improves throughout learning of the reaching task. Our computational tools are available as open-source in the OpenSim physics and modeling platform. Our model can enhance research into limb control across broad research topics and can inform analyses of motor learning, muscle synergies, neural patterning, and behavioral research that would otherwise be inaccessible. NEW & NOTEWORTHYInvestigations into motor planning and execution lack an accurate and complete model of the forelimb, which could bolster or expand on findings. We sought to construct such a model using high detail scans of murine anatomy and prior research into muscle physiology. We then used the model to predict muscle excitations in a set of reaching movements and found that it provided accurate estimations and provided insight into an optimal-control framework of motor learning.

bioengineering↗

bioRxiv2023

Myomatrix arrays for high-definition muscle recording

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous systems actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ("Myomatrix arrays") that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a "motor unit", during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous systems motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and in identifying pathologies of the motor system.

neuroscience↗

bioRxiv2020

Time course of changes in the long latency feedback response parallels the fast process of short term motor adaptation

Adapting to novel dynamics involves modifying both feedforward and feedback control. We investigated whether the motor system alters feedback responses during adaptation to a novel force field in a manner similar to adjustments in feedforward control. We simultaneously tracked the time course of both feedforward and feedback systems via independent probes during a force field adaptation task. Participants (n=35) grasped the handle of a robotic manipulandum and performed reaches to a visual target while the hand and arm were occluded. We introduced an abrupt counter-clockwise velocity-dependent force field during a block of reaching trials. We measured movement kinematics and shoulder and elbow muscle activity with surface EMG electrodes. We tracked the feedback stretch response throughout the task. Using force channel trials we measured overall learning, which was later decomposed into a fast and slow process. We found that the long-latency feedback response (LLFR) was upregulated in the early stages of learning and was correlated with the fast component of feedforward adaptation. The change in feedback response was specific to the long-latency epoch (50-100 ms after muscle stretch) and was observed only in the triceps muscle, which was the muscle required to counter the force field during adaptation. The similarity in time course for the LLFR and the estimated time course of the fast process suggests both are supported by common neural circuits. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller. New & NoteworthyWe investigated whether changes in the feedback stretch response were related to the proposed fast and slow processes of motor adaptation. We found that the long latency component of the feedback stretch response was upregulated in the early stages of learning, and the time course was correlated with the fast process. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller.

neuroscience↗