Biology subjects

Sergi, F.

Publications and source records attributed to Sergi, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Validation of Dynamic Bayesian Optimization for Human-in-the-Loop Optimization of Exoskeleton Control at User-Driven Walking Speed

Human-in-the-loop optimization (HILO) is an established method for identifying subject-specific optimal controllers for performance augmentation. For HILO algorithms to be useful in rehabilitation, however, the optimization algorithm may need to account for how the human response changes over time in response to assistance. In this study, we tested a modified version of Bayesian optimization (BO), dynamic Bayesian optimization (DBO), in a three-parameter optimization problem that sought to identify participant-specific optimal solutions for increasing walking speed. As opposed to BO, DBO accounts for the non-stationarity of human responses. Sixteen healthy participants received bilateral hip torque pulses delivered by a hip exoskeleton. The exoskeleton torque parameters were determined using HILO with either DBO or BO. Validation iterations were introduced to objectively compare performance across optimizers at different time points of HILO. The results showed that both DBO and BO significantly increased walking speed compared to baseline. When comparing performance between DBO and BO, DBO emerged as an improvement over BO both in terms of efficacy, modeling accuracy, and personalization. DBO induced changes in walking speed relative to baseline that exceeded those induced by BO at three of the four validation iterations. DBO outperformed BO in modeling accuracy in later validation iterations. DBO personalization induced changes in walking speed that were significantly greater than those induced by previously identified assistive solutions, while this was not the case of BO. Overall, our results indicate that DBO outperformed BO due to its greater ability to account for non-stationary aspects of the human response.

bioengineering↗

bioRxiv2026

Stretch-Evoked Motor Responses in the Brainstem are Modulated by Task Instructions

Reliable noninvasive measurement of human brainstem activity during motor control remains challenging due to small anatomical structures and physiological noise, yet it is essential for understanding descending contributions to rapid feedback control. We used brainstem-optimized whole brain functional magnetic resonance imaging (fMRI) to examine whether task-dependent modulation of stretch-evoked motor responses in humans are associated with changes in activation within reticulospinal regions of the human brainstem. A behavioral validation experiment (N=10), in which participants were instructed to resist or yield to brief wrist perturbations, confirmed task-dependent modulation of long-latency responses (LLRs). In a separate imaging cohort (N=26), participants performed the same tasks during fMRI using an MRI-compatible robotic device and a multi-echo acquisition with physiological noise compensation. Imaging analyses revealed greater blood-oxygen-level-dependent signal during Resist compared to Yield while controlling for background contraction and proprioceptive input, with activation distributed bilaterally across the pons and medulla in regions consistent with major reticulospinal nuclei. Laterality analyses demonstrated a rostrocaudal gradient, with relatively ipsilateral-biased activation in the medulla shifting toward more contralateral patterns in the pons. These findings indicate that instruction-dependent modulation of stretch-evoked motor responses is associated with measurable changes in human brainstem activation, providing evidence that reticulospinal regions contribute to task-dependent feedback control.

neuroscience↗

bioRxiv2026

Multi-objective optimization-based design of a compliant gravity balancing orthosis: development and validation

Wearable devices for gravity balancing have high potential for impact across domains, including neuromotor rehabilitation and occupational systems. Devices made from compliant mechanisms, optimized to achieve specific compensation moments at target joints, have proven effective, but thus far have solely been optimized towards gravity compensation and not other wearability criteria. In this work, we propose a multi-objective optimization framework, based on particle swarm optimization, to design a soft, gravity balancing shoulder orthosis, while taking into account wearability constraints such as undesired loading directions and device size. Using this custom framework, we pursued multiple stages of orthosis design and optimization, selecting multiple solutions to be translated to real-world prototypes. These solutions were realized via 3D printing with thermoplastic polyurethane and evaluated for mechanical performance on benchtop and in-vivo. In-vivo testing on 6 healthy individuals demonstrated relative reductions in muscle activity for the anterior deltoid and upper trapezius, by 53 % and 71 % respectively when operating the orthosis for static tasks within functional shoulder ranges of motion. Changes in muscle activation were also were observed across other muscles, including the posterior deltoid, as well as in dynamic tasks at different speeds.

bioengineering↗

bioRxiv2025

Validation of Dynamic Bayesian Optimization for a Non-Stationary Human-in-the-Loop Optimization Problem

Human-in-the-Loop Optimization (HILO) has demonstrated efficacy in achieving a plethora of assistive or augmentative effects. However, conventional optimizers such as Bayesian optimization (BO) do not account for the non-stationary aspects of the human-robot system, and may thus be limited in the domains of robot-assisted training or rehabilitation. In this study, we implemented HILO using dynamic Bayesian optimization (DBO) to define the optimal value of a single control parameter to target a desired effect in propulsion mechanics, specifically in the maximum hip extension (HE) angle during stance. Sixteen healthy participants received unilateral hip torque pulses via a hip exoskeleton for about 15 minutes while walking on a treadmill. Exoskeleton torques were determined via HILO using DBO or BO. Unknown to the optimizers, treadmill speed was gradually increased to amplify the non-stationary behavior of the system. Experimental results revealed that DBO outperformed BO in the later stages of training, leading to improved cost and to final values of torques closer to the true value. The primary difference between optimizers arose from their ability to model the history-dependent relationship between applied torque and output. BO associated later deviations between expected and measured output to process noise, while DBO developed a more accurate predictive model of the input-output relationship that gave proper weighing to past datapoints for making predictions.

bioengineering↗

bioRxiv2025

Mapping the Neural Control of Force and Impedance of Wrist Movements Using Robotics and fMRI

While robots are becoming increasingly valuable tools in neurorehabilitation, our limited understanding of the brains response during human-robot interaction tasks restricts advancements in training programs to restore neural pathways after injury. Co-contraction is characteristic of several neuro-muscular disorders, such as stroke and cerebral palsy, and it is often targeted by assessments or rehabilitation programs. Despite its importance, the neural mechanisms underlying co-contraction remain poorly understood. To address this gap, this study investigates the neural substrates of muscle co-contraction via functional magnetic resonance imaging (fMRI) during a dynamic motor task with an MR-compatible wrist robot. To establish suitable fMRI experimental conditions, we first conducted a behavioral study assessing muscle activity during a wrist-pointing task with four participants. Participants reached toward a target while experiencing four main perturbation conditions (no force, divergent force, constant force up, and constant force down), designed to elicit distinct force and impedance responses. Following this behavioral validation, five additional participants performed the wrist-pointing task during fMRI. Our results suggest localization of force and impedance control within the cortico-thalamic-cerebellar network. These findings provide new insights into the neural mechanisms of co-contraction, supporting the development of neurorehabilitation paradigms.

bioengineering↗

bioRxiv2024

Using Dynamic Bayesian Optimization to Induce Desired Effects in the Presence of Motor Learning: a Simulation Study

Human-in-the-loop (HIL) optimization is a control paradigm used for tuning the control parameters of human-interacting devices while accounting for variability among individuals. A limitation of state-of-the-art HIL optimization algorithms such as Bayesian Optimization (BO) is that they assume that the relationship between control parameters and user response does not change over time. BO can be modified to account for the dynamics of the user response by implementing time into the kernel function, a method known as Dynamic Bayesian Optimization (DBO). However, it is unknown if DBO outperforms BO when the human response is characterized by models of human motor learning. In this work, we simulated runs of HIL optimization using BO and DBO towards establishing if DBO is a suitable paradigm for HIL optimization in the presence of motor learning. Simulations were conducted assuming either purely time-dependent participant responses, or assuming that responses would arise from state-space models of motor learning capable of describing both adaptation and use-dependent learning behavior. Statistical comparisons indicated that DBO was never inferior to BO, and, after a certain number of iterations, generally outperformed BO in convergence to optimal inputs and outputs. The number of iterations beyond which DBO was superior to BO occurred earlier when the input-output relationship of the simulated responses was more dynamic. Our results suggest that DBO may improve the performance of HIL optimization over BO when a sufficient number of iterations can be evaluated to accurately distinguish between unstructured variability (noise) and learning.

bioengineering↗

bioRxiv2024

Inhibitory Effect of Subthreshold TMS on the Long-latency Response in the Flexor Carpi Radialis

Transcranial magnetic stimulation (TMS) can modulate corticospinal excitability during stretch-evoked long-latency responses (LLR). It has been previously established that suprathreshold TMS intensities can partially inhibit the cortical contribution to LLRs relying on a cortical silent period occurring after TMS. However, it is unknown whether the TMS-induced inhibition of stretch-evoked LLRs that relies on the cortical silent period can also be achieved via subthreshold stimulation. In this study, twelve healthy participants performed a protocol combining surface electromyography (EMG), robot-evoked wrist perturbations, and single pulse TMS applied to the motor cortex to study the effect of TMS intensity on the LLR amplitude in the flexor carpi radialis. We tested two TMS intensities of subthreshold (90%) and suprathreshold (130%) of the active motor threshold applied such that the motor evoked potential (MEP) peak would arrive 50 ms prior to perturbation onset. In suprathreshold TMS trials, TMS significantly reduced the cortical contribution to a LLR when applied prior to perturbation onset. When comparing the effects measured in the presence and absence of robot-applied muscle stretch, we observed that only suprathreshold conditions achieved inhibition of LLR, while subthreshold conditions did not result in any inhibition. Overall, our findings establish a clear distinction between the effect of subthreshold and suprathreshold TMS on the LLR inhibition via the cortical component of the silent period.

bioengineering↗

bioRxiv2024

Development of an MRI-compatible robotic perturbation system for studying the task-dependent contribution of the brainstem to long-latency responses

Methodological constraints have hindered direct in vivo measurement of reticulospinal tract (RST) function. The RST is thought to contribute to the increase in the amplitude of a long latency response (LLR), a stereotypical response evoked in stretched muscles, that arises when participants are asked to "resist" a perturbation. Thus, functional magnetic resonance imaging (fMRI) during robot-evoked LLRs under different task goals may be a method to measure motor-related RST function. We have developed the Dual Motor StretchWrist (DMSW), a new MR-compatible robotic perturbation system, and validated its functionality via experiments that used surface electromyography (sEMG) and fMRI. A first study was conducted outside the MRI scanner on six participants using sEMG to measure wrist flexor muscle activity associated with LLRs under different task instructions. Participants were given a Yield or Resist instruction before each trial and performance feedback based on the measured resistive torque was provided after every "Resist" trial to standardize LLR amplitude (LLRa). In a second study, ten participants completed two sessions of blocked perturbations under 1) Yield, 2) Resist, and 3) Yield Slow task conditions (control) during whole-brain fMRI. Statistical analysis of sEMG data shows significantly greater LLRa in Resist relative to Yield. Analysis of functional images shows increased activation primarily in the bilateral medulla and midbrain, and contralateral pons and primary motor cortex in the Resist condition. The results validate the capability of the DMSW to elicit LLRs of wrist muscles with different amplitudes as a function of task instruction, and its capability of simultaneous operation during fMRI.

bioengineering↗

bioRxiv2024

Modeling Neuromotor Adaptation to Pulsed Torque Assistance During Walking

Multiple mechanisms of motor learning contribute to the response of individuals to robot-aided gait training, including error-based learning and use-dependent learning. Previous models described either of these mechanisms, but not both, and their relevance to gait training is unknown. In this paper, we establish the validity of existing models to describe the response of healthy individuals to robot-aided training of propulsion via a robotic exoskeleton, and propose a new model that accounts for both use-dependent and error-based learning. We formulated five state-space models to describe the stride-by-stride evolution of metrics of propulsion mechanics during and after robot-assisted training, applied by a hip/knee robotic exoskeleton for 200 consecutive strides. The five models included a single-state, a two-state, a two-state fast and slow, a use-dependent learning (UDL), and a newly-developed modified UDL model, requiring 4, 9, 5, 3, and 4 parameters, respectively. The coefficient of determination (R2) and Akaike information criterion (AIC) values were calculated to quantify the goodness of fit of each model. Model fit was conducted both at the group and at the individual participant level. At the group level, the modified UDL model shows the best goodness-of-fit compared to other models in AIC values in 15/16 conditions. At the participant level, both the modified UDL model and the two-state model have significantly better goodness-of-fit compared to the other models. In summary, the modified UDL model is a simple 4-parameter model that achieves similar goodness-of-fit compared to a two-state model requiring 9 parameters. As such, the modified UDL model is a promising model to describe the effects of robot-aided gait training on propulsion mechanics.

bioengineering↗

bioRxiv2024

Development of an Experimental Protocol to Study the Neural Control of Force and Impedance in Wrist Movements with Robotics and fMRI

Robotic exoskeletons have emerged as beneficial tools in the field of rehabilitation, yet their full potential is impeded by our limited knowledge of the neural control of movements during human-robot interaction. To personalize exoskeleton protocols and improve individuals motor recovery, we must advance our understanding of how the brain commands movements in physical interaction tasks. However, interpreting the neural function associated with these movements is complex due to the simultaneous expression of at least two control policies: force and impedance control. This hinders our ability to isolate these control mechanisms and pinpoint their neural origins. In this study, we evaluate the capacity of externally applied forces to decouple the expression of force and impedance in a wrist-pointing task, a necessary step in isolating their neural substrates via neuroimaging. We first conducted simulations using a neuromuscular model to examine how both force and impedance commands are updated when participants are asked to perform reaching movements in the presence of an externally applied force. Then, we recruited seven participants to perform a wrist-pointing task with the MR-SoftWrist, an MRI-compatible wrist robot. The task included four different force conditions - no force, positive constant force, negative constant force, and divergent force, each carefully selected to decouple expression of force and impedance control. Furthermore, we evaluated the efficacy of our proposed conditions for a neuroimaging experiment through simulations of neural activity. We show that these applied forces elicit distinct and predictable torque and stiffness expression, laying the groundwork for reliably identifying their associated neural activity in a future neuroimaging study.

bioengineering↗

bioRxiv2024

Estimating Propulsion Kinetics in Absence of a Direct Measurement of the Anterior Component of Ground Reaction Force

Anterior ground reaction force (AGRF) is a common measurement of walking function in post-stroke individuals. It is typically measured using multi-axis force-plates which are not always found in robotic research labs. Here we present a comparison of models using kinematic and kinetic metrics of propulsion to estimate AGRF. Nine models using measurements of maximum vertical ground reaction force (maxVGRF), vertical ground reaction force at peak AGRF (aVGRF), maximum trailing limb angle (maxTLA), trailing limb angle at peak AGRF (aTLA) and stride length (SL) were used to predict different metrics of propulsion kinetics, including maximum AGRF (maxAGRF), propulsive impulse (PI), maximum AGRF normalized by body-weight (maxAGRFnorm), and normalized PI (PInorm) from participants at speeds [0.6 1.4] m/s. R2 and AICc scores were recorded for each model, and the individual participant R2 values for the best single and two-factor models for each outcome were examined. Of the single-factor models, kinematic measurements were the best predictors of the outcome measurements. More specifically, maxAGRF/norm were best predicted by SL (R2 = 0.91, 0.82, respectively), and PI/norm were best predicted by maxTLA (R2 = 0.84, 0.43, respectively). For the two-factor models, maxAGRFnorm and PInorm were both best predicted by SL and aVGRFnorm, and maxVGRF yeilded the best predictions for maxAGRF and PI. Models predicting maxAGRF/norm better fit individual participants than those predicting PI/norm. These results indicate that maxAGRF can be estimated with reasonable accuracy (R2 = 0.92, RMSE of residuals: 1.5% bodyweight, equivalent to a 0.09 m/s increase in velocity) in the absence of a direct measurement of AGRF using both kinematic and kinetic measurements of propulsion.

bioengineering↗

bioRxiv2024

A Multi-objective Simulation-Optimization Framework for the Design of a Compliant Gravity Balancing Orthosis

Flexion-synergy is a stereotypical movement pattern that inhibits independent joint control for those who have been affected by stroke; this abnormal co-activation of elbow flexors with shoulder abductors significantly reduces range of motion when reaching against gravity. While wearable orthoses based around compliant mechanisms have been shown to accurately compensate for the arm at the shoulder, it is unclear if accurate compensation can also be achieved while minimizing device bulk. In this work, we present a novel, multi-objective simulation-optimization framework towards the goal of designing practical gravity-balancing orthoses for the upper-limb. Our framework includes a custom built VB.NET application to run nonlinear finite element simulations in SolidWorks, and interfaces with a MATLAB-based particle swarm optimizer modified for multiple objectives. The framework is able to identify a set of Pareto-optimal compliant mechanism designs, confirming that compensation accuracy and protrusion minimization are indeed conflicting design objectives. The preliminary execution of the simulation-optimization framework demonstrates a capability of achieving designs that compensate for almost 90% of the arms gravity or that exhibit an average protrusion of less than 5% of the arm length, with different trade-offs between these two objectives.

bioengineering↗

bioRxiv2024

Model-Based Estimation of Active and Passive Muscle Forces Using MRE in Forearm Muscles During 2-DOF Wrist Tasks

Magnetic resonance elastography (MRE)-based muscle force estimation methods have been proposed to estimate individual muscle forces based on measurements of shear wave speed and joint torque in multiple postures. To estimate both the slope and offset parameters of the relationship between shear wave speed and muscle force, it is necessary to collect measurements in a plethora of postures in case of substantial muscle redundancy. However, anisotropic MRE requires a structural and diffusion-tensor imaging scan in each posture, which is infeasible given the time constraints of MRI imaging. The objectives of this work were to develop a muscle force estimator with sufficient accuracy that would only require a limited set of postures, and to evaluate its effectiveness under a variety of measurement conditions. We developed a novel MRE-based muscle force estimator, which decouples shear wave speed into its active and passive components and solves for the slope and offset parameters, independently. We assessed the effectiveness of the proposed estimator under different simulated measurement conditions, with varying levels of noise, and compared it to the original MRE-based estimator. The proposed estimator results in a reduction in the estimation error for the offset parameter, for all muscles, without significant degradation in the estimation error for the slope parameter. However, the proposed muscle force estimator does not improve the goodness-of-fit or the cross-validation error compared to the original estimator. In conclusion, the proposed MRE-based muscle force estimator improves the estimation of muscle-specific parameters and may yield increased muscle force estimation performance.

bioengineering↗

bioRxiv2022

Concurrent Contribution of Co-contraction to Error Reduction during Dynamic Adaptation of the Wrist

MRI-compatible robots provide a means of studying brain function involved in complex sensorimotor learning processes, such as adaptation. To properly interpret the neural correlates of behavior measured using MRI-compatible robots, it is critical to validate the measurements of motor performance obtained via such devices. Previously, we characterized adaptation of the wrist in response to a force field applied via an MRI-compatible robot, the MR-SoftWrist. Compared to arm reaching tasks, we observed lower end magnitude of adaptation, and reductions in trajectory errors beyond those explained by adaptation. Thus, we formed two hypotheses: that the observed differences were due to measurement errors of the MR-SoftWrist; or that impedance control plays a significant role in control of wrist movements during dynamic perturbations. To test both hypotheses, we performed a two-session counterbalanced study. In both sessions, participants performed wrist pointing in three force field conditions (zero force, constant, random). Participants used either the MR-SoftWrist or the UDiffWrist, a non-MRI-compatible wrist robot, for task execution in session one, and the other device in session two. To measure anticipatory co-contraction associated with impedance control, we collected surface EMG of four forearm muscles. We found no significant effect of device on behavior, validating the measurements of adaptation obtained with the MR-SoftWrist. EMG measures of co-contraction explained a significant portion of the variance in excess error reduction not attributable to adaptation. These results support the hypothesis that for the wrist, impedance control significantly contributes to reductions in trajectory errors in excess of those explained by adaptation.

bioengineering↗

bioRxiv2021

Changes in resting state functional connectivity associated with dynamic adaptation of wrist movements

Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (1). Adapted motor plans are consolidated into motor memories that contribute to better performance on re-exposure to the same dynamic condition. In parallel, dynamic perturbations can be compensated for by alternate motor control processes, such as co-contraction, that contribute to error reduction (2). Whether these control strategies share the same neural resources for memory formation is unclear. To address this gap in knowledge, we used a novel fMRI-compatible wrist robot, the MR-SoftWrist (3), to identify neural processes specific to dynamic adaptation and subsequent memory formation. Using the MR-SoftWrist, we acquired fMRI during a motor performance and a dynamic perturbation task to localize brain networks of interest. Resting state fMRI scans were acquired immediately before and after task performance to quantify changes in resting state functional connectivity (rsFC) within these networks. Twenty-four hours later, we assessed behavioral retention of training. A variance decomposition analysis was used to isolate behavior associated with adaptation versus alternate error reduction strategies. Immediately after the dynamic perturbation task, rsFC significantly increased within the corticothalamic-cerebellar network of the trained wrist and decreased interhemispherically within the cortical sensorimotor network. These changes were associated to behavioral measures of initial acquisition and retention, indicative of memory formation. Variance decomposition analysis revealed that increases within the cortico-thalamic-cerebellar network were associated with adaptation, while interhemispheric decreases in rsFC within the sensorimotor network were associated with alternate error reduction processes. Significance StatementMotor memory formation processes have been studied using resting state functional connectivity (rsFC) before and after exposure to adaptation tasks. However, due to technical limitations, previous studies have not investigated rsFC within brain regions localized during task execution, nor quantified rsFC within minutes after task performance. The present study used an fMRI-compatible wrist robot to localize relevant brain regions during dynamic adaptation within the cortico-thalamic-cerebellar network and bilateral sensorimotor network. Immediately following dynamic adaptation, we measured significant changes in rsFC within these networks, that were associated with adaptation behavior and with gains in behavior assessed 24 hours later, indicative of memory formation. Variance decomposition analysis identified distinct rsFC networks associated with adaptation specific processes, and with alternate motor control strategies.

bioengineering↗

bioRxiv2021

Using Repetitive Control to Enhance Force Control During Human-Robot Interaction in Quasi-Periodic Tasks

We investigated the use of repetitive control (RC) to enhance force control during human-robot interaction in quasi-periodic tasks. We first developed a two-mass spring damper model and formulated three different RCs under force control: a 1st order RC (RC-1), a 3rd order RC designed for random period error, and a 3rd order RC designed for constant period error. Then, we quantified the performance of these three RCs through simulations and experiments conducted on a bench top linear platform, subject to nominal cyclical inputs (input signal and fundamental frequency: 0.5 Hz), and subject to inputs with random and constant period errors. Moreover, we compared the performance achieved with the RCs with those achievable with a passive proportional controller (PPC), subject to known theoretical limits for passivity and coupled stability. In both simulated and real-world experiments, the root mean square force error under nominal conditions was reduced most effectively by the RC-1 to 0.7% and 12.9%, respectively, of the error achieved with the PPC. Subject to inputs with constant period errors, RCs performed better than PPC for period error values below 0.05 Hz, with the RC-1 performing significantly better than both 3rd order RCs. Subject to inputs with random period errors, all RCs performed better than PPC up to 0.11 Hz of frequency error. Our results indicate that RC can successfully integrated in force control schemes to improve performance beyond the one achievable with a PPC, in the range of period variability expected in applications such as walking assistance and rehabilitation.

bioengineering↗

bioRxiv2021

Using Bayesian Optimization to Identify Optimal Exoskeleton Parameters Targeting Propulsion Mechanics: A Simulation Study

In this study, we determined the feasibility of modeling the relationship between robot control parameters and propulsion mechanics as a Gaussian process. Specifically, we used data obtained in a previous experiment that used pulses of torque applied at the hip and knee joint, at early and late stance, to establish the relationship a 3D control parameter space and the resulting changes in hip extension and propulsive impulse. We estimated Gaussian models both at the group level and for each subject. Moreover, we used the estimated subject-specific models to simulate virtual human-in-the-loop optimization (HIL) experiments based on Bayesian optimization to establish their convergence under multiple combinations of acquisition functions and seed point selection methods. Results of the group-level model are in agreement with those obtained with linear mixed effect model, thus establishing the feasibility of Gaussian process modeling. The estimated subject-specific optimal conditions have large between-subject variability in the metric of propulsive impulse, with only 31% of subjects featuring a subject-specific optimal point in the surrounding of the group-level optimal point. Virtual HIL experiments indicate that expected improvement is the most effective acquisition method, while no significant effect of seed point selection method was observed. Our study may have practical effects on the adoption of HIL robot-assisted training methods focused on propulsion.

bioengineering↗

bioRxiv2020

Effects of Perturbation Velocity, Direction, Background muscle activation, and Task Instruction on Long-Latency Responses Measured from Forearm Muscles

The centeral nervous system uses feedback processes that occur at multiple time scales to control interactions with the environment. Insight on the neuromechanical mechanisms subserving the faster feedback processes can be gained by applying rapid mechanical perturbations to the limb, and observing the ensuing muscle responses using electromyography (EMG). The long-latency response (LLR) is the fastest process that directly involve cortical areas, with a motorneuron response measurable 50 ms following an imposed limb displacement. Several behavioral factors concerning perturbation mechanics and the active role of muscles prior or during the perturbation can modulate the long-latency response amplitude (LLRa) in the upper limbs, but the interaction between many of these factors had not been systematically studied before. We conducted a behavioral study on thirteen healthy individuals to determine the effect and interaction of four behavioral factors -- background muscle torque, perturbation direction, perturbation velocity, and task instruction -- on the LLRa evoked from the flexor carpi radialis (FCR) and extensor carpi ulnaris (ECU) muscles following the application of wrist displacements. The effects of the four factors listed above were quantified using both a 0D statistical analysis on the average perturbation-evoked EMG signal in the period corresponding to an LLR, and using a timeseries analysis of EMG signals. All factors significantly modulated LLRa, and that their combination nonlinearly contributed to modulating the LLRa. Specifically, all the three-way interaction terms that could be computed without including the interaction between instruction and velocity significantly modulated the LLR. Analysis of the three-way interaction terms of the 0D model indicated that for the ECU muscle, the LLRa evoked when subjects are asked to maintain their muscle activation in response to the perturbations (DNI) was greater than the one observed when subjects yielded (Y) to the perturbations ({Delta}LLRa -- DNI vs. Y: 1.76{+/-}0.16 nu, p<0.001), but this effect was not measured for muscles undergoing shortening or in absence of background muscle activation. Moreover, higher perturbation velocity increased the LLRa evoked from the stretched muscle in presence of a background torque ({Delta}LLRa 200-125 deg/s: 0.94{+/-}0.20 nu, p<0.001; {Delta}LLRa 125-50 deg/s: 1.09 {+/-}0.20 nu, p<0.001), but no effects of velocity were measured in absence of background torque, nor effects of any of those factors was measured on muscles shortened by the perturbations. The time-series analysis indicated the significance of some effects in the LLR region also for muscles undergoing shortening. As an example, the interaction between torque and instruction was significant also for the ECU muscle undergoing shortening, in part due to the composition of a positive and negative modulation of the response due to the interaction between of the two terms. The absence of a nonlinear interaction between task instruction and perturbation velocity suggest that the modulation introduced by these two factors are processed by distinct neural pathways.

neuroscience↗