Search bioRxiv⌕ Search

Biology subjects

Zonnino, A.

Publications and source records attributed to Zonnino, A..

6 recordsLinked to original sources
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↗
bioRxiv2020

Measurement of stretch-evoked brainstem function using fMRI

Knowledge on the organization of motor function in the reticulospinal tract (RST) is limited by the lack of methods for measuring RST function in humans. Behavioral studies suggest the involvement of the RST in long latency responses (LLRs). LLRs, elicited by precisely controlled perturbations, can therefore act as a viable paradigm to measure motor-related RST activity using functional Magnetic Resonance Imaging (fMRI). Here we present StretchfMRI, a novel technique developed to study RST function associated with LLRs. StretchfMRI combines robotic perturbations with electromyography and fMRI to simultaneously quantify muscular and neural activity during stretch-evoked LLRs without loss of reliability. Using StretchfMRI, we established the muscle-specific organization of LLR activity in the brainstem. The observed organization is partially consistent with animal models, with activity primarily in the ipsilateral medulla for flexors and in the contralateral pons for extensors, but also include other areas, such as the midbrain and bilateral pontomedullary contributions.

neuroscience↗
bioRxiv2019

StretchfMRI: a new technique to quantify the contribution of the reticular formation to long-latency responses via fMRI

Increased reticulospinal (RS) function has been observed to cause both positive and negative outcomes in the recovery of motor function after corticospinal lesions such as stroke. Current knowledge of RF function is limited by the lack of accurate, noninvasive methods for measuring RS function. Recent studies suggest that the RS tract may be involved in processing and generating Long Latency Responses (LLRs). LLRs, elicited by applying precisely controlled perturbations, can act as a reliable stimulus to measure LLR-related brainstem function using fMRI with high signal-to-noise ratio.\n\nIn this paper, we present StretchfMRI, a new technique that enables simultaneous recording of neural and muscular activity during motor responses conditioned by velocity-controlled robotic perturbations, which allows for direct investigation of the neural correlates of LLRs using fMRI.\n\nVia preliminary validation experiments, we demonstrate that our technique can reliably elicit and identify LLRs in two wrist muscles-FCR and ECU. Moreover, via a single-subject pilot experiment, we show that the occurrence of an LLR in a flexor and extensor muscle modulates neural activity in distinct regions of the brainstem. The observed somatotopic organization is in agreement with the double reciprocal model of RS function observed in animal models, in which the right medullary and left pontine reticular formation are responsible for control of the motor activity in flexors and extensors, respectively.

neuroscience↗
bioRxiv2019

MM-MRE: a new technique to quantify individual muscle forces during isometric tasks of the wrist using MR elastography

Non-invasive in-vivo measurement of individual muscle force is limited by the infeasibility of placing force sensing elements in series with the musculo-tendon structures. At the same time, estimating muscle forces using EMG measurements is prone to inaccuracies, as EMG is not always measurable for the complete set of muscles acting around the joints of interest. While new methods based on shear wave elastography have been recently proposed to directly characterize muscle mechanics, they can only be used to measure muscle forces in a limited set of superficial muscles. As such, they are not suitable to study the neuromuscular control of movements that require coordinated action of multiple muscles.\n\nIn this work, we present multi-muscle magnetic resonance elastography (MM-MRE), a new technique capable of quantifying individual muscle force from the complete set of muscles in the forearm, thus enabling the study of the neuromuscular control of wrist movements. MM-MRE integrates measurements of joint torque provided by an MRI-compatible instrumented handle with muscle-specific measurements of shear wave speed obtained via MRE to quantify individual muscle force using model-based estimator.\n\nA single-subject pilot experiment demonstrates the possibility of obtaining measurements from individual muscles and establishes that MM-MRE has sufficient sensitivity to detect changes in muscle mechanics following the application of isometric joint torque with self-selected intensity.

bioengineering↗
bioRxiv2018

The effect of physiological and measurement noise on the estimate of individual muscle force from indirect measurements of muscle activity

Several forward dynamics estimators have been proposed to quantify individual muscle force using non-invasive measurements of muscle activity. None of them, however, addresses the inaccuracy that arises when measurements are available only from a subset of the muscles involved in the movement under analysis. We present a novel estimator that integrates a forward dynamics estimation approach with knowledge of the optimal contraction strategy to obtain accurate estimates of individual muscle force when measurements of muscle activity are not available for all muscles. A following in-silico characterization showed that when trying to estimate forces form the forearm muscles acting around the wrist joint, our novel estimator is able to decrease the mean estimation bias by about 25% of the true value of muscle force. With a sensitivity analysis, we show that the model-based estimator is robust against physiological variability in muscle co-contraction strategy.

bioengineering↗
bioRxiv2018

Model-based analysis of the stiffness of the wrist joint in active and passive conditions

The control of joint stiffness is a fundamental mechanism used to control human movements. While many studies have observed how stiffness is controlled for tasks involving shoulder and elbow motion, a limited amount of knowledge is available for wrist movements, though the wrist plays a crucial role in fine manipulation.\n\nWe have developed a computational framework based on a realistic musculoskeletal model, which allows to calculate the passive and active components of the wrist joint stiffness. We first used the framework to validate the musculoskeletal model against experimental measurements of the passive wrist joint stiffness, and then to study the contribution of different muscle groups on the passive joint stiffness. We finally used the framework to study the effect of muscle co - contraction on the active joint stiffness.\n\nThe results show that thumb and finger muscles play a crucial role in determining the passive wrist joint stiff - ness: in the neutral posture, the direction of maximum stiffness aligns with the experimental measurements, and the magnitude increases by 113% when they are included. Moreover, the analysis of the controllability of joint stiffness showed that muscle co - contraction positively correlates with the stiffness magnitude and negatively correlates with the variability of the stiffness orientation (p < 0.01 in both cases). Finally, an exhaustive search showed that with appropriate selection of a muscle activation strategy, the joint stiffness orientation can be arbitrarily modulated. This observation suggests the absence of biomechanical constraints on the controllability of the orientation of the wrist joint stiffness.

bioengineering↗