Human motor augmentation - spinal motor neurons control of redundant degrees-of-freedom

In 1963, Stan Lee introduced a new villain to the Spiderman Universe: Dr Octopus – a human equipped with multiple robotic arms that can be controlled seamlessly in coordination with his natural limbs. Throughout the last decades, turning such fiction into real-life applications gave rise to the research field of human motor augmentation, ultimately aiming to enable humans to perform motor tasks that are sheer impossible with our natural limbs alone. While a significant process was made in designing artificial supernumerary limbs, a central problem remains: identifying adequate bodily signals that allow moving supernumerary degrees-of-freedom together with our natural ones. So far, neural activity in the brain seems to hold the greatest potential for providing all the flexibility needed to ensure such coordination between natural and supernumerary degrees-of-freedom. However, accessing neural populations in the cortical regions is accompanied by an unacceptable risk for most users. A different group of neural cells can be found in the outmost layer of the motor pathway, driving the contraction of muscles and generation of force – spinal motor neurons. The development of novel neural interfaces has made it possible to study single motor neuron activity with minimal harm to the user. This allows a direct and non-invasive window into the neural activity orchestrating human movement. In this dissertation, I investigate whether these neurons innervating our muscles could provide supernumerary control signals. The results indicate, in essence, that features extracted non-invasively from motor neuron activity have the potential to overcome current limitations in supernumerary control and thus could significantly advance human motor augmentation.Open Acces

Causal evidence that intrinsic beta frequency is relevant for enhanced signal propagation in the motor system as shown through rhythmic TMS

Correlative evidence provides support for the idea that brain oscillations underpin neural computations. Recent work using rhythmic stimulation techniques in humans provide causal evidence but the interactions of these external signals with intrinsic rhythmicity remain unclear. Here, we show that sensorimotor cortex precisely follows externally applied rhythmic TMS (rTMS) stimulation in the beta-band but that the elicited responses are strongest at the intrinsic individual beta-peak-frequency. While these entrainment effects are of short duration, even subthreshold rTMS pulses propagate through the network and elicit significant cortico-spinal coupling, particularly when stimulated at the individual beta-frequency. Our results show that externally enforced rhythmicity interacts with intrinsic brain rhythms such that the individual peak frequency determines the effect of rTMS. The observed downstream spinal effect at the resonance frequency provides evidence for the causal role of brain rhythms for signal propagation

Surface EMG and muscle fatigue: multi-channel approaches to the study of myoelectric manifestations of muscle fatigue

In a broad view, fatigue is used to indicate a degree of weariness. On a muscular level, fatigue posits the reduced capacity of muscle fibres to produce force, even in the presence of motor neuron excitation via either spinal mechanisms or electric pulses applied externally. Prior to decreased force, when sustaining physically demanding tasks, alterations in the muscle electrical properties take place. These alterations, termed myoelectric manifestation of fatigue, can be assessed non-invasively with a pair of surface electrodes positioned appropriately on the target muscle; traditional approach. A relatively more recent approach consists of the use of multiple electrodes. This multi-channel approach provides access to a set of physiologically relevant variables on the global muscle level or on the level of single motor units, opening new fronts for the study of muscle fatigue; it allows for: (i) a more precise quantification of the propagation velocity, a physiological variable of marked interest to the study of fatigue; (ii) the assessment of regional, myoelectric manifestations of fatigue; (iii) the analysis of single motor units, with the possibility to obtain information about motor unit control and fibre membrane changes. This review provides a methodological account on the multi-channel approach for the study of myoelectric manifestation of fatigue and on the experimental conditions to which it applies, as well as examples of their current applications

Modeling the neurophysiology of tremor to develop a peripheral neuroprosthesis for tremor suppression

Pathological tremor is an involuntary oscillation of the body parts around joints. Pharmaceu- ticals and surgical treatments are approved approaches for tremor management; however, their side effects limit their usability. The main objective of this study is, therefore, to design a closed-loop non-invasive electrical stimulation system that could suppress tremor without serious side effects. We started our system design by investigating motor unit (MU) behaviors during postural tremor via decomposition of high-density surface electromyography (EMG) recordings of antagonist pairs of wrist muscles of essential tremor (ET) patients. The common input strength that influences voluntary and tremor movements and the phase difference between activation of motor neurons in antagonist pairs of muscles were assessed to find the correlation of the motor unit activity during different tasks. We observed that, during postural tremor, the motor units in antagonist pairs of muscles were activated with a phase difference that varies over time. An online EMG decomposition method and a phase-locked-loop system were, therefore, implemented in our tremor suppression system to real-timely discriminate motor unit discharge timings, track the phase of the motor unit activity and use that real-time phase estimation to control the stimulation timing. We applied sub-threshold stimulation to the muscle pairs in an out-of-phase manner. The system was validated offline with the data recorded from 13 ET patients before it was tested with an ET patient to prove the concept. Since the spinal cord is the termination of the afferent neurons from the peripheral nervous system and connection to the central nervous system and motor neurons, we hypothesized that electrical stimulation at the spinal cord could also modulate tremor-related neural commands. Russian currents with a 5 kHz-carrier frequency modulated with a slow burst at tremor frequencies were used with sub-threshold intensity to stimulate at C5-C6 cervical spine of 9 ET patients. The reduction of the tremor power was observed via an analysis of the wrist angle recorded using an accelerometer. We present, in this thesis, two electrical stimulation approaches for tremor suppression via the peripheral nerves and spinal cord, providing options for patients to utilize based on their preference.Open Acces

A non-invasive human-machine interfacing framework for investigating dexterous control of hand muscles

The recent fast development of virtual reality and robotic assistive devices enables to augment the capabilities of able-body individuals as well as to overcome the motor missing functions of neurologically impaired or amputee individuals. To control these devices, movement intentions can be captured from biological structures involved in the process of motor planning and execution, such as the central nervous system (CNS), the peripheral nervous system (in particular the spinal motor neurons) and the musculoskeletal system. Thus, human-machine interfaces (HMI) enable to transfer neural information from the neuro-muscular system to machines. To prevent any risks due to surgical operations or tissue damage in implementing these HMIs, a non-invasive approach is proposed in this thesis. In the last five decades, surface electromyography (sEMG) has been extensively explored as a non-invasive source of neural information. EMG signals are constituted by the mixed electrical activity of several recruited motor units, the fundamental components of muscle contraction. High-density sEMG (HD-sEMG) with the use of blind source separation methods enabled to identify the discharge patterns of many of these active motor units. From these decomposed discharge patterns, the net common synaptic input (CSI) to the corresponding spinal motor neurons was quantified with cross-correlation in the time and frequency domain or with principal component analysis (PCA) on one or few muscles. It has been hypothesised that this CSI would result from the contribution of spinal descending commands sent by supra-spinal structures and afferences integrated by spinal interneurons. Another motor strategy implying the integration of descending commands at the spinal level is the one regarding the coordination of many muscles to control a large number of articular joints. This neurophysiological mechanism was investigated by measuring a single EMG amplitude per muscle, thus without the use of HD-sEMG and decomposition. In this case, the aim was to understand how the central nervous system (CNS) could control a large set of muscles actuating a vast set of combinations of degrees of freedom in a modular way. Thus, time-invariant patterns of muscle coordination, i.e. muscle synergies , were found in animals and humans from EMG amplitude of many muscles, modulated by time-varying commands to be combined to fulfil complex movements. In this thesis, for the first time, we present a non-invasive framework for human-machine interfaces based on both spinal motor neuron recruitment strategy and muscle synergistic control for unifying the understanding of these two motor control strategies and producing control signals correlated to biomechanical quantities. This implies recording both from many muscles and using HD-sEMG for each muscle. We investigated 14 muscles of the hand, 6 extrinsic and 8 intrinsic. The first two studies, (in Chapters 2 and 3, respectively) present the framework for CSI quantification by PCA and the extraction of the synergistic organisation of spinal motor neurons innervating the 14 investigated muscles. For the latter analysis, in Chapter 3, we proposed the existence of what we named as motor neuron synergies extracted with non-negative matrix factorisation (NMF) from the identified motor neurons. In these first two studies, we considered 7 subjects and 7 grip types involving differently all the four fingers in opposition with the thumb. In the first study, we found that the variance explained by the CSI among all motor neuron spike trains was (53.0 ± 10.9) % and its cross-correlation with force was 0.67 ± 0.10, remarkably high with respect to previous findings. In the second study, 4 motor neuron synergies were identified and associated with the actuation of one finger in opposition with the thumb, finding even higher correlation values with force (over 0.8) for each synergy associated with a finger during the actuation of the relative finger. In Chapter 4, we then extended the set of analysed movements in a vast repertoire of gestures and repeated the analysis of Chapter 3 by finding a different synergistic organisation during the execution of tens of tasks. We divided the contribution among extrinsic and intrinsic muscles and we found that intrinsic better enable single-finger spatial discrimination, while no difference was found in regression of joint angles by dividing the two groups of muscles. Finally, in Chapter 5 we proposed the techniques of the previous chapters for cases of impairment due both to amputation and stroke. We analysed one case of pre and post rehabilitation sessions of a trans-humeral amputee, the case of a post-stroke trans-radial amputee and three cases of acute stroke, i.e. less than one month from the stroke event. We present future perspectives (Chapter 6) aimed to design and implement a platform for both rehabilitation monitoring and myoelectric control. Thus, this thesis provides a bridge between two extensively studied motor control mechanisms, i.e. motor neuron recruitment and muscle synergies, and proposes this framework as suitable for rehabilitation monitoring and control of assistive devices.Open Acces

Analysis of Simulated Electromyography (EMG) Signals Using Integrated Computer Muscle Model

Introduction Electromyography (EMG) is a technique used to study the activity of muscle through detection and analysis of the electrical signals generated during muscular contractions. Electromyographic activity is recorded from skeletal muscles to obtain information about their anatomy and physiology. Electromyography, in interplay with various anatomical techniques, provides the present knowledge of the structural organization and the nervous control of muscle. EMG is the prime source of information about the status of the neuromuscular system, and EMG has developed into a diagnostic tool that allows the clinician to follow changes in nerve and muscle caused by neuromuscular diseases. EMG provides both invasive and noninvasive means for the study of muscular functions [1, 2]. It is also useful in interpreting pathologic states of musculoskeletal or neuromuscular systems [3, 4]. In particular, EMG offers valuable information concerning the timing of muscular activity and its relative intensity [5, 6]. Standard EMG is typically recorded from fine wire or two surface electrodes placed at discrete sites over a muscle or muscle belly. Currently surface grid electrode EMG is widely used. The cell bodies of these neurons reside in the brainstem and spinal cord. The interfacing fiber between motor neuron and muscle is called axon. At the distal end, an axon divides 1 into many terminal branches. Each terminal branch innervates a group of muscle fibers. When a nerve signal approaches the end of an axon, it spreads out over all its terminal branches and stimulates all the muscle fibers supplied by them. So, all the excited muscle fibers contract almost simultaneously. Since they behave as a single functional unit, one nerve fiber and all the muscle fibers innervated by it are called a motor unit (MU) [7, 8]. Generally, the muscle fibers of a motor unit are distributed throughout muscle rather than being clustered together. The fine control of the muscle force is performed through the intricate mechanism and interaction of the brain and muscle. During contraction, these motor units are recruited systematically and the recruited motor units discharge in a train of pulses in a complex manner [9, 10]. The recorded EMG is the temporal summation of all the recruited motor unit action potential trains. Because movement is controlled by motor unit activity, an understanding of motor unit physiology can have a significant impact on the evaluation and treatment of movement disorders. The neuromuscular system is an intricate physiological organization of brain, nerve and muscle. These neural control properties are not well understood mostly because of the experimental difficulties in quantifying the neural input to the muscle. Moreover, the muscle itself is a complex system. It is necessary to address these complexities as accurately as possible. Understanding of these complex systems facilitates the understanding of EMG generation, which is a highly complex signal by itself

An artificial neural network to predict the effects of fatigue on the electromyographical signal and generated torque of knee extensor muscles

A muscle\u27s electromyographical (EMG) signal has been used extensively to provide insight into the dynamics of muscle behavior. However, since there has been no consistent use of EMG signal detection, the use of EMG signal analysis for the determination of muscle force has not been widely explored. Also, unwanted frequencies from neighboring muscles, electrical fields, and the equipment used to detect the EMG signal itself corrupt the signal of interest. The purpose of this study is to correlate a contracting muscle\u27s EMG signal with the generated torque and an estimate of the muscle\u27s fatigue using an artificial neural network. An artificial neural network is a form of artificial intelligence software that is able to learn and detect patterns in order to generalize and classify data. Using EMG signals and torque data collected from four human subjects while they performed maximal effort contractions of the knee joint, fatigue was assumed to increase linearly with time.It was assumed that subjects experienced a maximum fatigue equal to the percent drop in the peak torque values generated at the beginning and ending of the experiment. The data collected from one of the subjects was used to train an artificial neural network. The network training method used a backpropagation training algorithm. Data from the remaining subjects were used to test the neural network. It is believed that the results of this research can be applied to the fields of functional electrical stimulation (FES) and myoelectric prosthetic limb development. In FES a muscle is made to contract through the application of an external current. FES has shown the most potential in the rehabilitation of patients with spinal cord injuries (SCI). A myoelectric prosthetic limb uses electrodes to detect the EMG signal from a user\u27s remaining limb to control an electric motor. Insight into how fatigue affects the EMG patterns of a muscle during prolonged contraction can assist in the design of myoelectric prosthesis

A role for sensory areas in coordinating active sensing motions

Active sensing, which incorporates closed-loop behavioral selection of information during sensory acquisition, is an important feature of many sensory modalities. We used the rodent whisker tactile system as a platform for studying the role cortical sensory areas play in coordinating active sensing motions. We examined head and whisker motions of freely moving mice performing a tactile search for a randomly located reward, and found that mice select from a diverse range of available active sensing strategies. In particular, mice selectively employed a strategy we term contact maintenance, where whisking is modulated to counteract head motion and sustain repeated contacts, but only when doing so is likely to be useful for obtaining reward. The context dependent selection of sensing strategies, along with the observation of whisker repositioning prior to head motion, suggests the possibility of higher level control, beyond simple reflexive mechanisms. In order to further investigate a possible role for primary somatosensory cortex (SI) in coordinating whisk-by-whisk motion, we delivered closed-loop optogenetic feedback to SI, time locked to whisker motions estimated through facial electromyography. We found that stimulation regularized whisking (increasing overall periodicity), and shifted whisking frequency, changes that emulate behaviors of rodents actively contacting objects. Importantly, we observed changes to whisk timing only for stimulation locked to whisker protractions, possibly encoding that natural contacts are more likely during forward motion of the whiskers. Simultaneous neural recordings from SI show cyclic changes in excitability, specifically that responses to excitatory stimulation locked to whisker retractions appeared suppressed in contrast to stimulation during protractions that resulted in changes to whisk timing. Both effects are evident within single whisks. These findings support a role for sensory cortex in guiding whisk-by-whisk motor outputs, but suggest a coupling that depends on behavioral context, occurring on multiple timescales. Elucidating a role for sensory cortex in motor outputs is important to understanding active sensing, and may further provide novel insights to guide the design of sensory neuroprostheses that exploit active sensing context

ELECTROPHYSIOLOGY OF BASAL GANGLIA (BG) CIRCUITRY AND DYSTONIA AS A MODEL OF MOTOR CONTROL DYSFUNCTION

The basal ganglia (BG) is a complex set of heavily interconnected nuclei located in the central part of the brain that receives inputs from the several areas of the cortex and projects via the thalamus back to the prefrontal and motor cortical areas. Despite playing a significant part in multiple brain functions, the physiology of the BG and associated disorders like dystonia remain poorly understood. Dystonia is a devastating condition characterized by ineffective, twisting movements, prolonged co-contractions and contorted postures. Evidences suggest that it occurs due to abnormal discharge patterning in BG-thalamocortocal (BGTC) circuitry. The central purpose of this study was to understand the electrophysiology of BGTC circuitry and its role in motor control and dystonia. Toward this goal, an advanced multi-target multi-unit recording and analysis system was utilized, which allows simultaneous collection and analysis of multiple neuronal units from multiple brain nuclei. Over the cause of this work, neuronal data from the globus pallidus (GP), subthalamic nucleus (STN), entopenduncular nucleus (EP), pallidal receiving thalamus (VL) and motor cortex (MC) was collected from normal, lesioned and dystonic rats under awake, head restrained conditions. The results have shown that the neuronal population in BG nuclei (GP, STN and EP) were characterized by a dichotomy of firing patterns in normal rats which remains preserved in dystonic rats. Unlike normals, neurons in dystonic rat exhibit reduced mean firing rate, increased irregularity and burstiness at resting state. The chaotic changes that occurs in BG leads to inadequate hyperpolarization levels within the VL thalamic neurons resulting in a shift from the normal bursting mode to an abnormal tonic firing pattern. During movement, the dystonic EP generates abnormally synchronized and elongated burst duration which further corrupts the VL motor signals. It was finally concluded that the loss of specificity and temporal misalignment between motor neurons leads to corrupted signaling to the muscles resulting in dystonic behavior. Furthermore, this study reveals the importance of EP output in controlling firing modes occurring in the VL thalamus