Neural Contributions to Physical Activity: From the Brain to the Muscle and Back Again

The thesis aims to explore the neural contribution to physical activity. The work is divided into two chapters containing research conducted in Italy, France or both states. The first chapter includes four behavioural studies aimed at evaluating the reciprocal relationship between physical activity and cognition. The second chapter contains two neurophysiological studies aimed at investigating the muscle length and the type of contraction's contribution to the activity of the motor system. Chapter one investigates how motor condition influences cognitive processes and vice versa. Several studies suggested that engaging in physical activity programs elicits a wide range of neural changes. One of those is enhancing cognitive performance (e.g., working memory or information processing speed). On the other side, specific cognitive interventions (e.g., action observation or motor imagery) can ameliorate motor performance. This reciprocal interaction could produce adverse effects if people exceed one of these two activities, which induce a fatiguing state. Chapter two looks into the activity of the corticomotor system as a function of muscle length and type of contraction. After a general introduction exploring previous studies investigating the role of muscle length (in static and dynamic form) on the corticomotor system, the chapter will present two studies. The first study examined the influence of muscle length on neuromuscular function and corticospinal excitability. The second study investigated the difference in primary motor cortex excitability when preparing concentric and eccentric contractions

A MULTIPLE REPRESENTATIONS MODEL OF THE HUMAN MIRROR NEURON SYSTEM FOR LEARNED ACTION IMITATION

The human mirror neuron system (MNS) is a fundamental sensorimotor system that plays a critical role in action observation and imitation. Despite a large body of experimental and theoretical MNS studies, the visuospatial transformation between the observed and the imitated actions has received very limited attention. Therefore, this work proposes a neurobiologically plausible MNS model, which examines the dynamics between the fronto-parietal mirror system and the parietal visuospatial transformation system during action observation and imitation. The fronto-parietal network is composed of the inferior frontal gyrus (IFG) and the inferior parietal lobule (IPL), which are postulated to generate the neural commands and the predictions for its sensorimotor consequences, respectively. The parietal regions identified as the superior parietal lobule (SPL) and the intraparietal sulcus (IPS) are postulated to encode the visuospatial transformation for enabling view-independent representations of the observed action. The middle temporal region is postulated to provide the view-dependent representations such as direction and velocity of the observed action. In this study, the SPL/IPS, IFG, and IPL are modeled with artificial neural networks to simulate the neural mechanisms underlying action imitation. The results reveal that this neural model can replicate relevant behavioral and neurophysiological findings obtained from previous action imitation studies. Specifically, the imitator can replicate the observed actions independently of the spatial relationships with the demonstrator while generating similar synthetic functional magnetic resonance imaging blood oxygenation level-dependent responses in the IFG for both action observation and execution. Moreover, the SPL/IPS can provide view-independent visual representations through mental transformation for which the response time monotonically increases as the rotation angle augments. Furthermore, the simulated neural activities reveal the emergence of both view-independent and view-dependent neural populations in the IFG. As a whole, this work suggests computational mechanisms by which visuospatial transformation processes would subserve the MNS for action observation and imitation independently of the differences in anthropometry, distance, and viewpoint between the demonstrator and the imitator

Human factors in instructional augmented reality for intravehicular spaceflight activities and How gravity influences the setup of interfaces operated by direct object selection

In human spaceflight, advanced user interfaces are becoming an interesting mean to facilitate human-machine interaction, enhancing and guaranteeing the sequences of intravehicular space operations. The efforts made to ease such operations have shown strong interests in novel human-computer interaction like Augmented Reality (AR). The work presented in this thesis is directed towards a user-driven design for AR-assisted space operations, iteratively solving issues arisen from the problem space, which also includes the consideration of the effect of altered gravity on handling such interfaces.Auch in der bemannten Raumfahrt steigt das Interesse an neuartigen Benutzerschnittstellen, um nicht nur die Mensch-Maschine-Interaktion effektiver zu gestalten, sondern auch um einen korrekten Arbeitsablauf sicherzustellen. In der Vergangenheit wurden wiederholt Anstrengungen unternommen, Innenbordarbeiten mit Hilfe von Augmented Reality (AR) zu erleichtern. Diese Arbeit konzentriert sich auf einen nutzerorientierten AR-Ansatz, welcher zum Ziel hat, die Probleme schrittweise in einem iterativen Designprozess zu lösen. Dies erfordert auch die Berücksichtigung veränderter Schwerkraftbedingungen

Sensorimotor experience in virtual environments

The goal of rehabilitation is to reduce impairment and provide functional improvements resulting in quality participation in activities of life, Plasticity and motor learning principles provide inspiration for therapeutic interventions including movement repetition in a virtual reality environment, The objective of this research work was to investigate functional specific measurements (kinematic, behavioral) and neural correlates of motor experience of hand gesture activities in virtual environments stimulating sensory experience (VE) using a hand agent model. The fMRI compatible Virtual Environment Sign Language Instruction (VESLI) System was designed and developed to provide a number of rehabilitation and measurement features, to identify optimal learning conditions for individuals and to track changes in performance over time. Therapies and measurements incorporated into VESLI target and track specific impairments underlying dysfunction. The goal of improved measurement is to develop targeted interventions embedded in higher level tasks and to accurately track specific gains to understand the responses to treatment, and the impact the response may have upon higher level function such as participation in life. To further clarify the biological model of motor experiences and to understand the added value and role of virtual sensory stimulation and feedback which includes seeing one\u27s own hand movement, functional brain mapping was conducted with simultaneous kinematic analysis in healthy controls and in stroke subjects. It is believed that through the understanding of these neural activations, rehabilitation strategies advantaging the principles of plasticity and motor learning will become possible. The present research assessed successful practice conditions promoting gesture learning behavior in the individual. For the first time, functional imaging experiments mapped neural correlates of human interactions with complex virtual reality hands avatars moving synchronously with the subject\u27s own hands, Findings indicate that healthy control subjects learned intransitive gestures in virtual environments using the first and third person avatars, picture and text definitions, and while viewing visual feedback of their own hands, virtual hands avatars, and in the control condition, hidden hands. Moreover, exercise in a virtual environment with a first person avatar of hands recruited insular cortex activation over time, which might indicate that this activation has been associated with a sense of agency. Sensory augmentation in virtual environments modulated activations of important brain regions associated with action observation and action execution. Quality of the visual feedback was modulated and brain areas were identified where the amount of brain activation was positively or negatively correlated with the visual feedback, When subjects moved the right hand and saw unexpected response, the left virtual avatar hand moved, neural activation increased in the motor cortex ipsilateral to the moving hand This visual modulation might provide a helpful rehabilitation therapy for people with paralysis of the limb through visual augmentation of skills. A model was developed to study the effects of sensorimotor experience in virtual environments, and findings of the effect of sensorimotor experience in virtual environments upon brain activity and related behavioral measures. The research model represents a significant contribution to neuroscience research, and translational engineering practice, A model of neural activations correlated with kinematics and behavior can profoundly influence the delivery of rehabilitative services in the coming years by giving clinicians a framework for engaging patients in a sensorimotor environment that can optimally facilitate neural reorganization

Congruency of eye movement metrics across motor simulation states: implications for motor (re)learning.

This thesis contains a series of studies that report, for the first time, the congruence between physical, imagined and observed movement though a range of eye movement markers as a test of Jeannerod’s Simulation Theory (Jeannerod, 1994, 2001). First, the eye gaze metrics of healthy young individuals across all the action-related processes in a single paradigm is reported. The finding from this study suggested a temporal and spatial similarity between action execution (AE) and action observation (AO), and a spatial similarity between AE and motor imagery (MI). These findings suggest that AO could be used to simulate actions that involve a critical temporal element. Second, the influence of early ageing on gaze metrics was examined. The findings from this study indicated that whilst the profile of metrics for AE showed age-related decline, it was less evident in AO and MI although there was evidence of some age-related decline across all the three processes. Third, the influence of visual perspective on eye movements during movement simulation is reported. The data analysis in this study was novel and allowed, for the first time, eye gaze to be used to quantify MI and highlighted the importance of social gaze in AO and its absence in MI. Taken together, the finding that some eye metrics are preserved in more covert behaviours provides support for the efficacy of (re)learning optimal eye gaze strategies through AO- and MI-supported movement-based interventions for older adults with movement dysfunction. Therefore, in the final study, the development of a fully-integrated AE-AO-MI toolkit is reported. A new, App-based approach to the integration of movement simulation in rehabilitation is described in detail. Twenty years after he first proposed his Simulation Theory of MI the novel findings from this programme of work provide substantial support for the concept. This thesis highlights the advantage of using advanced eye gaze technology as an important marker to inform the on-going debate on the extent of the neural substrate sharedness as the central tenet to Simulation Theory. The findings of the studies will make an important impact on the use of simulation procedures for motor relearning

Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices

Excellent motor control skills are necessary to live a high-quality life. Activities such as walking, getting dressed, and feeding yourself may seem mundane, but injuries to the neuromuscular system can render these tasks difficult or even impossible to accomplish without assistance. Statistics indicate that well over 100 million people are affected by diseases or injuries, such as stroke, Parkinson’s Disease, Multiple Sclerosis, Cerebral Palsy, peripheral nerve injury, spinal cord injury, and amputation, that negatively impact their motor abilities. This wide array of injuries presents a challenge to the medical field as optimal treatment paradigms are often difficult to implement due to a lack of availability of appropriate assessment tools, the inability for people to access the appropriate medical centers for treatment, or altogether gaps in technology for treating the underlying impairments causing the disability. Addressing each of these challenges will improve the treatment of movement impairments, provide more customized and continuous treatment to a larger number of patients, and advance rehabilitative and assistive device technology. In my research, the key approach was to develop tools to assess and treat upper extremity movement impairment. In Chapter 2.1, I challenged a common biomechanical[GV1] modeling technique of the forearm. Comparing joint torque values through inverse dynamics simulation between two modeling platforms, I discovered that representing the forearm as a single cylindrical body was unable to capture the inertial parameters of a physiological forearm which is made up of two segments, the radius and ulna. I split the forearm segment into a proximal and distal segment, with the rationale being that the inertial parameters of the proximal segment could be tuned to those of the ulna and the inertial parameters of the distal segment could be tuned to those of the radius. Results showed a marked increase in joint torque calculation accuracy for those degrees of freedom that are affected by the inertial parameters of the radius and ulna. In Chapter 2.2, an inverse kinematic upper extremity model was developed for joint angle calculations from experimental motion capture data, with the rationale being that this would create an easy-to-use tool for clinicians and researchers to process their data. The results show accurate angle calculations when compared to algebraic solutions. Together, these chapters provide easy-to-use models and tools for processing movement assessment data. In Chapter 3.1, I developed a protocol to collect high-quality movement data in a virtual reality task that is used to assess hand function as part of a Box and Block Test. The goal of this chapter is to suggest a method to not only collect quality data in a research setting but can also be adapted for telehealth and at home movement assessment and rehabilitation. Results indicate that the data collected in this protocol are good and the virtual nature of this approach can make it a useful tool for continuous, data driven care in clinic or at home. In Chapter 3.2 I developed a high-density electromyography device for collecting motor unit action potentials of the arm. Traditional surface electromyography is limited by its ability to obtain signals from deep muscles and can also be time consuming to selectively place over appropriate muscles. With this high-density approach, muscle coverage is increased, placement time is decreased, and deep muscle activity can potentially be collected due to the high-density nature of the device[GV2] . Furthermore, the high-density electromyography device is built as a precursor to a high-density electromyography-electrical stimulation device for functional electrical stimulation. The customizable nature of the prototype in Chapter 3.2 allows for the implementation both recording and stimulating electrodes. Furthermore, signal results show that the electromyography data obtained from the device are of high quality and are correlated with gold standard surface electromyography sensors. One key factor in a device that can record and then stimulate based on the information from the recorded signals is an accurate movement intent decoder. High-quality movement decoders have been designed by closed-loop device controllers in the past, but they still struggle when the user interacts with objects of varying weight due to underlying alterations in muscle signals. In Chapter 4, I investigate this phenomenon by administering an experiment where participants perform a Box and Block Task with objects of 3 different weights, 0 kg, 0.02 kg, and 0.1 kg. Electromyography signals of the participants right arm were collected and co-contraction levels between antagonistic muscles were analyzed to uncover alterations in muscle forces and joint dynamics. Results indicated contraction differences between the conditions and also between movement stages (contraction levels before grabbing the block vs after touching the block) for each condition. This work builds a foundation for incorporating object weight estimates into closed-loop electromyography device movement decoders. Overall, we believe the chapters in this thesis provide a basis for increasing availability to movement assessment tools, increasing access to effective movement assessment and rehabilitation, and advance the medical device and technology field