Biology subjects

Farina, D.

Publications and source records attributed to Farina, D..

9 recordsLinked to original sources

bioRxiv2021

Mathematical Relationships between Motoneuron Properties Derived by Empirical Data Analysis: Size Determines All Motoneuron Properties

Our understanding of the behaviour of spinal alpha-motoneurons (MNs) in mammals partly relies on our knowledge of the relationships between MN membrane properties, such as MN size, resistance, rheobase, capacitance, time constant, axonal conduction velocity and afterhyperpolarization period. We reprocessed the data from 40 experimental studies in adult cat, rat and mouse MN preparations, to empirically derive a set of quantitative mathematical relationships between these MN electrophysiological and anatomical properties. This validated mathematical framework, which supports past findings that the MN membrane properties are all related to each other and clarifies the nature of their associations, is besides consistent with the Hennemans size principle and Ralls cable theory. The derived mathematical relationships provide a convenient tool for neuroscientists and experimenters to complete experimental datasets, to explore relationships between pairs of MN properties never concurrently observed in previous experiments, or to investigate inter-mammalian-species variations in MN membrane properties. Using this mathematical framework, modelers can build profiles of inter-consistent MN-specific properties to scale pools of MN models, with consequences on the accuracy and the interpretability of the simulations.

neuroscience↗

bioRxiv2021

The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal

Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces provide a promising basis for human motor augmentation by extracting potential high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurones to effectively increase the number of degrees-of-freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N=7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU control based on independent synaptic inputs to single MUs is not a simple to learn control strategy.

neuroscience↗

bioRxiv2021

Double nerve transfer to a single target muscle: experimental model in the upper extremity

Surgical nerve transfers are used to efficiently treat peripheral nerve injuries, neuromas, phantom limb pain or improve bionic prosthetic control. Commonly, one donor nerve is transferred to one target muscle. However, the transfer of multiple nerves onto a single target muscle may increase the number of muscle signals for myoelectric prosthetic control and facilitate the treatment of multiple neuromas. Currently, no experimental models are available for multiple nerve transfers to a common target muscle in the upper extremity. This study describes a novel experimental model to investigate the neurophysiological effects of peripheral double nerve transfers. For this purpose, we developed a forelimb model to enable tension-free transfer of one or two donor nerves in the upper extremity. Anatomic dissections were performed to design the double nerve transfer model (n=8). In 62 male Sprague-Dawley rats the ulnar nerve of the antebrachium alone (n=30) or together with the anterior interosseus nerve (n=32) was transferred to reinnervate the long head of the biceps brachii. Before neurotization, the motor branch to the biceps long head was transected at the motor entry point and resected up to its original branch to prevent auto-reinnervation. In all animals, coaptation of both nerves to the motor entry point could be performed tension-free. Mean duration of the procedure was 49 {+/-} 13 min for the single nerve transfer and 78 {+/-} 20 min for the double nerve transfer. Twelve weeks after surgery, muscle response to neurotomy, behavioral testing, retrograde labeling and structural analyses were performed to assess reinnervation. These analyses indicated that all nerves successfully reinnervated the target muscle. No aberrant reinnervation was observed by the originally innervating nerve. Our observations suggest a minimal burden for the animal with no signs of functional deficit in daily activities or auto-mutilation in both procedures. Furthermore, standard neurophysiological analyses for nerve and muscle regeneration were applicable. This newly developed nerve transfer model allows for the reliable and standardized investigation of neural and functional changes following the transfer of multiple donor nerves to one target muscle.

neuroscience↗

bioRxiv2021

Ultra fast and highly realistic numerical modelling of the surface EMG

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains. It is currently a crucial component of control systems in robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these new applications. Deep learning approaches have shown the highest potential in this regard. To be effective, deep learning requires a large amount of high-quality annotated data for training; the only option today is the use of experimental electromyography data. Yet the acquisition and labelling of training data is time-consuming and expensive. Moreover, the high-quality annotation of this data is often not possible because the ground truth labels are hidden. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient and realistic models. Here, we present a new highly realistic and ultra-fast computational model tailored for the training of deep learning algorithms. For the first time, we are able to simulate arbitrary large datasets of realistic electromyography signals with high internal variability and leverage it to train deep learning algorithms. Because the computational model provides access to all the hidden parameters of the simulation, it also allows us to use some annotation strategies that are impossible with experimental data. We believe that this concept of Myoelectric Digital Twin allows new unprecedented approaches to muscular signals decoding and will accelerate the development of human-machine interfaces.

bioengineering↗

bioRxiv2021

Non-invasive real-time access to the output of the spinal cord via a wrist wearable interface

Despite the promising features of neural interfaces, their trade-off between information transfer and invasiveness has limited translation and viability outside research settings. Here, we present a non-invasive neural interface that provides access to spinal motoneuron activities from a sensor band at the wrist. The interface decodes electric signals present at the tendon endings of the forearm muscles by using a model of signal generation and deconvolution. First, we evaluated the reliability of the interface to detect motoneuron firings, and thereafter we used the decoded neural activity for the prediction of finger movements in offline and real-time conditions. The results showed that motoneuron activity decoded from the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time control. These findings demonstrate the feasibility of a wearable, non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.

neuroscience↗

bioRxiv2021

Why humans are stronger but not faster after isometric strength training: specific neural, not muscular, motor unit adaptations

While maximal force increases following short-term isometric strength training, the rate of force development (RFD) may remain relatively unaffected. The underlying neural and muscular mechanisms during rapid contractions after strength training are largely unknown. Since strength training increases the neural drive to muscles, it may be hypothesized that there are distinct neural or muscular adaptations determining the change in RFD independently of an increase in maximal force. Therefore, we examined motor unit population data during the rapid generation of force before and after four weeks of strength training. We observed that strength training did not change the RFD because it did not influence the number of motor units recruited per second or their initial discharge rate during rapid contractions. While strength training did not change motoneuron behaviour in the force increase phase of rapid contractions, it increased the discharge rate of motoneurons (by [~]4 spikes/s) when reaching the plateau phase ([~]150 ms) of the rapid contractions, determining an increase in maximal force production. Computer simulations with a motor unit model that included neural and muscular properties, closely matched the experimental observations and demonstrated that the lack of change in RFD following training is primarily mediated by an unchanged maximal recruitment speed of motoneurons. These results demonstrate that maximal force and contraction speed are determined by different adaptations in motoneuron behaviour following strength training and indicate that increases in the recruitment speed of motoneurons are required to evoke training-induced increases in RFD.

neuroscience↗

bioRxiv2021

Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators

There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 {+/-} 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons x 11 contractions/muscles x 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

neuroscience↗

bioRxiv2020

Human-Robot Interaction with Robust Prediction of Movement Intention Surpasses Manual Control

Designing robotic assistance devices for manipulation tasks is challenging. This work aims at improving accuracy and usability of physical human-robot interaction (pHRI) where a user interacts with a physical robotic device (e.g., a human operated manipulator or exoskeleton) by transmitting signals which need to be interpreted by the machine. Typically these signals are used as an open-loop control, but this approach has several limitations such as low take-up and high cognitive burden for the user. In contrast, a control framework is proposed that can respond robustly and efficiently to intentions of a user by reacting proactively to their commands. The key insight is to include context- and user-awareness in the controller, improving decision making on how to assist the user. Context-awareness is achieved by creating a set of candidate grasp targets and reach-to grasp trajectories in a cluttered scene. User-awareness is implemented as a linear time-variant feedback controller (TV-LQR) over the generated trajectories to facilitate the motion towards the most likely intention of a user. The system also dynamically recovers from incorrect predictions. Experimental results in a virtual environment of two degrees of freedom control show the capability of this approach to outperform manual control. By robustly predicting the users intention, the proposed controller allows the subject to achieve superhuman performance in terms of accuracy and thereby usability.

neuroscience↗

bioRxiv2020

Only the fastest corticospinal fibers contribute to beta corticomuscular coherence

A common way to study human corticospinal transmission is with transcranial magnetic stimulation. However, this is biased to activity in the fastest conducting axons. It is unclear whether conclusions obtained in this context are representative of volitional activity in mild-to-moderate contractions. A possible alternative to overcome this limitation is to study the corticospinal transmission of endogenously generated brain activity. Here we study the transmission speeds of cortical beta rhythms travelling to the muscles during steady contractions. To do this, we introduce new methods to improve delay estimates in the corticomuscular transmission of beta rhythms, and which we validate both in simulations and experimentally. Applying these approaches to experimental data from humans, we show that corticomuscular beta transmission delays are only 1-2ms longer than expected from the fastest corticospinal pathway. Simulations using realistic distributions of the conduction velocities for descending axons projecting to lower motoneurons suggest two scenarios that can explain these results: either a very small fraction of only the fastest corticospinal axons selectively transmit beta activity, or else the entire pool does. The implications that these two scenarios have for our understanding of corticomuscular interactions are discussed in the final part of this manuscript. SIGNIFICANCEWe present and validate an improved methodology to measure the delay in the transmission of cortical beta activity to tonically active muscles. The estimated corticomuscular beta transmission delays which this yields are remarkably similar to those expected from transmission in the fastest corticospinal axons. A simulation of beta transmission along a pool of corticospinal axons using a realistic distribution of fiber diameters suggests two possible mechanisms by which fast corticomuscular transmission is achieved: either a very small fraction of descending axons transmits beta activity to the muscles or, alternatively, the entire population does and natural cancellation of slow channels occurs due to the distribution of axon diameters in the corticospinal tract.

neuroscience↗