Biology subjects

Al Borno, M.

Publications and source records attributed to Al Borno, M..

8 recordsLinked to original sources

bioRxiv2026

A Physiologically Detailed Biomechanical Model of the Mouse Distal Forelimb for Simulation of Fine Motor Control

This study presents a physiologically detailed biomechanical model of the mouse distal forelimb that incorporates intrinsic musculature, tendon routing, and digit-level skeletal anatomy, features simplified or omitted in existing musculoskeletal models. Using high-resolution anatomical reconstruction and computational modeling, we created a physiological representation of the wrist and digits capable of simulating complex forelimb movements. The model enables simulation of coordinated distal forelimb movement and digit-level muscle behavior during grasping-related tasks. Simulations were performed for multiple tasks, including grasping, grasping with supination, wrist flexion, and digit I flexion, with analysis focused on the grasping task due to its integration of both intrinsic and extrinsic musculature. Model performance was evaluated through comparisons of marker trajectories between torque-driven reference motion and muscle-driven simulations, temporal shuffle control, and comparisons between experimentally recorded electromyography (EMG) activity and model-predicted muscle excitation profiles. The model successfully reproduced coordinated distal forelimb kinematics, demonstrated strong agreement between torque-driven and muscle-driven simulation approaches, and generated physiologically plausible muscle excitation patterns consistent with experimentally observed EMG activity during grasping-related movement. These findings establish the model as a framework for studying fine motor control, neuromuscular coordination, and movement-related impairments in mice while providing a foundation for future investigation of neurological disorders and their underlying biomechanical mechanisms.

bioengineering↗

bioRxiv2026

Hindmarsh-Rose neuronal network with spike-timing-dependent plasticity demonstrates coordinated reset neuromodulation

Computational models of brain structures impacted by Parkinson's disease are useful for studying pathological synchronization and exploring potential therapies. We use the Hindmarsh-Rose neuronal model to simulate synchronized activity in the subthalamic nucleus, capturing key features of the pathological rhythms observed in Parkinson's disease. Our model incorporates chemical synapses whose strengths evolve according to a spike-timing-dependent plasticity (STDP) rule. We apply coordinated reset stimulation with rapidly varying sequences (RVS CR) and examine its ability to weaken synaptic weights, thereby reducing neuronal synchrony. This stimulation technique delivers phase-shifted stimuli to distinct sites in a random sequence. We explore how stimulation frequency and the number of stimulation sites affect the efficacy of RVS CR at desynchronizing the network and observe good agreement with previous studies. We demonstrate that RVS CR efficacy is sensitive to the depression-to-potentiation ratio in the STDP rule, which may be an important parameter to tune when reconciling simulations with experimental data. Numerical simulation of neuronal networks is constrained by computational resources when models demand large networks. This work proposes a model that demonstrates similar utility with a relatively small network, enabling researchers to study pathological neuronal activity and treatments more efficiently.

neuroscience↗

bioRxiv2025

Comparing Vibrotactile Stimulation to Combined Visual and Auditory Stimulation for 40 Hz Gamma Entrainment

There is significant interest in combined visual and auditory stimulation to entrain 40 Hz gamma oscillations for the treatment of Alzheimers disease and other neurological conditions such as stroke and insomnia. In this work, we compared another sensory modality--vibrotactile stimulation delivered with a glove--to visual and auditory stimulation in 15 healthy participants in terms of electroencephalogram (EEG) responses and subjective experience. We found that vibrotactile stimulation from the glove could evoke 40 Hz EEG responses in the central, frontal and, to a lesser extent, occipital cortices. We also observed distinct patterns of functional connectivity between the two stimulation modalities. Participants preferred the vibrotactile stimulation over the visual and auditory stimulation. Our study supports future investigations on vibrotactile stimulation for the treatment of neurological conditions.

bioengineering↗

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↗

bioRxiv2024

The Effects of Vibrotactile Stimulation of the Upper Extremity on Sensation and Perception

Vibrotactile stimulation has applications in a variety of fields, including medicine, virtual reality, and human-computer interaction. Eccentric Rotating Mass (ERM) vibrating motors are widely used in wearable haptic devices for their small size, low cost, and low energy features. The effect of ERM motor vibrations on upper extremity sensation and perception have not been thoroughly studied previously, which is important to design better wearable haptic devices. We conducted experiments with vibrotactile stimulation on 15 healthy participants. Eight motors were placed on a consistent set of muscles on the upper extremity and one motor was placed on the index finger. We found a significant correlation between voltage and sensation intensity (r=0.39). However, we did not find a significant aggregate-level correlation on the perceived pleasantness of the simulation. The sensation intensity varied based on the location of the vibration on the upper extremity (with the lowest intensity on the triceps brachii and brachialis) and slightly decreased (5.9 {+/-} 2.9 %) when participants performed reaching movements. When a single motor was vibrating, the participants accuracy at identifying the motor without visual feedback increased as voltage increased, reaching up to 81.4 {+/-} 14.2 %. When we stimulated three muscles simultaneously, we found that most participants were able to identify only two out of three vibrating motors (41.7 {+/-} 32.3 %). Our findings can help tune stimulation parameters in human- machine interaction applications.

bioengineering↗

bioRxiv2021

OpenSense: An open-source toolbox for Inertial-Measurement-Unit-based measurement of lower extremity kinematics over long durations

BackgroundThe ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate, and capable of assessing and mitigating drift. MethodsWe computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-minute trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subjects RMS differences over time. ResultsIMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3-6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60 to 0.87). We observed minimal drift in the RMS differences over ten minutes; the average slopes of the linear fits to these data were near zero (-0.14 to 0.17 deg/min). ConclusionsOur workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, obviating the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

bioengineering↗

bioRxiv2019

High-fidelity Musculoskeletal Modeling Reveals a Motor Planning Contribution to the Speed-Accuracy Tradeoff

The speed-accuracy tradeoff is a fundamental aspect of goal-directed motor behavior, empirically formalized by Fitts law, which relates movement duration to movement distance and target width. Here, we introduce a computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements and is more biomechanically realistic than previous models. Critically, these features arise without the need of signal-dependent noise. We analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural states to movement duration variability was greater for smaller targets than larger targets, and that movements to smaller targets exhibited less variability in preparatory neural states, but greater movement duration variability. Taken together, these results suggest that Fitts law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of "good" control solutions (i.e., faster reaches). Thus, the speed-accuracy tradeoff could be a consequence of motor planning variability and optimal control theory, and not exclusively signal-dependent noise, as is currently held. Significance StatementA long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. We introduce a computational model of reaching movements based on optimal control theory using a realistic model of musculoskeletal dynamics. The model synthesizes three-dimensional point-to-point reaching movements that reproduce kinematics features reported in motor control studies. Such high-fidelity modeling reveals that the speed-accuracy tradeoff as described by Fitts law emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. This suggests an alternative theory based on suboptimal control solutions. The crux of this theory is that some features of human movement are attributable to planning variability rather than execution noise.

neuroscience↗

bioRxiv2019

The Effects of Motor Modularity on Performance, Generalizability and Learning in Upper-Extremity Reaching: a Computational Analysis

It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviors seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks-such as reaching to targets on a higher or lower plane, and starting from alternate initial poses-with average errors similar to a full-dimensional controller.

physiology↗