Towards Testable Neuromechanical Control of Architectures for Running

Our objective is to provide experimentalists with neuromechanical control hypotheses that can be tested with kinematic data sets. To illustrate the approach, we select legged animals responding to perturbations during running. In the following sections, we briefly outline our dynamical systems approach, state our over-arching hypotheses, define four neuromechanical control architectures (NCAs) and conclude by proposing a series of perturbation experiments that can begin to identify the simplest architecture that best represents an animal\u27s controller

An investigation into the relationship between locomotor dynamics and adaptability

Over the last 40 years, a new paradigm has been posited where the variability observed in physiological systems is a consequence of the interactions occurring between the various components that affect the system. While quantifying the magnitude of variability can be useful, analyses that measure how the structure of the variability (dynamics) changes over time have been posited to reflect the health of the system. Many researchers interpret the results of these analyses to be indicative of the system’s adaptive capacity. While there is ample indirect evidence to support this notion, a lack of direct findings has left the literature lacking a definitive foundation to move forward with this interpretation. While many physiological systems are too invasive to safely perturb, the movement-based systems are routinely perturbed in real-world environments without dire consequences. Of particular interest is the locomotor system, which is constantly challenged in real-world environments via slips and trips. Furthermore, the locomotor system can be safely and validly perturbed in the laboratory. A range of locomotor dynamics-based measures have been used to describe differences between various clinical populations, but none have been directly associated with a person’s ability to remain upright when perturbed. The objectives of this study are to (1) examine the relationship between locomotor dynamics/stability to overall fall-risk prior, (2) examine how locomotor dynamics relate to the ability to recover from a trip via global stability, and (3) determine the extent to which an acute trip-training session alters locomotor dynamics and global stability. Forty healthy, older adults (75.2±4.9 yrs) were recruited by convenience from the local community. The participants completed a variety of clinical assessments in order to determine overall fall-risk. Afterwards, they participated in three walking trials consisting of: 1) a 15-minute unperturbed walking session, 2) a 10-minute unperturbed walking session (control) or a 10-minute trip-training session (intervention), and 3) a 15-minute unperturbed walking session. Various measurements of locomotor dynamics and adaptability were calculated from full-body 3-D kinematics collected at 100Hz. Multiple regression and repeated measure analysis of variance models were calculated to determine to what extent locomotor dynamics and adaptability relate to one another and how an acute trip-training session affects their relationship. The results from our first experiment suggested that locomotor dynamics and stability during steady state do not significantly relate to overall fall-risk. However, the second experiment showed that locomotor dynamics are predictive of an individual’s ability to recover from a trip. Our last experiment showed the feasibility of using an acute trip-training session to alter locomotor dynamics and stability. These data represent the first direct evidence of physiological variability being indicative of adaptive capacity in the locomotor system. Further investigation will be necessary to determine the robustness of the analyses to indicate adaptive capacity across perturbations and populations

Semaphorin 7A restricts serotonergic innervation and ensures recovery after spinal cord injury

Descending serotonergic (5-HT) projections originating from the raphe nuclei form an important input to the spinal cord that control basic locomotion. The molecular signals that control this projection pattern are currently unknown. Here, we identify Semaphorin7A (Sema7A) as a critical cue that restricts serotonergic innervation in the spinal cord. Sema7A deficient mice show a marked increase in serotonergic fiber density in all layers of the spinal cord while the density of neurons expressing the corresponding 5-HTR2α receptor remains unchanged. These alterations appear to be successfully compensated as no obvious changes in rhythmic locomotion and skilled stepping are observed in adult mice. When the system is challenged with a spinal lesion, serotonergic innervation patterns in both Sema7A-deficient and -competent mice evolve over time with excessive innervation becoming most pronounced in the dorsal horn of Sema7A-deficient mice. These altered serotonergic innervation patterns correlate with diminished functional recovery that predominantly affects rhythmic locomotion. Our findings identify Sema7A as a critical regulator of serotonergic circuit formation in the injured spinal cord

Principles of sensorimotor learning.

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved

Investigating the brain mechanisms involved in learning abstract sensorimotor mappings

PhD ThesisMyoelectric-computer interfaces (MCIs) provide a unique opportunity to study mechanisms of motor learning and adaptation as they allow the creation of abstract sensorimotor tasks disassociated from biomechanical constraints, and the manipulation of visuomotor mappings at the level of individual muscles. In addition, study of MCI use provides a useful basis for designing optimal prosthetics, by understanding how the motor system deals with new patterns of muscle co-ordination. Here I used MCI tasks in order to examine subjects’ ability to learn and adapt to abstract sensorimotor mappings. In the tasks, subjects moved a 2D cursor controlled by electromyogram (EMG) recorded from between two and eight hand and forearm muscles. Each muscle was assigned a direction of action (DoA) and cursor position was determined using the vector sum of the EMG. Subjects were able to quickly learn abstract mappings, and adapt successfully to rotations of the full muscle-DoA mapping (global) and rotations where subsets of the muscle-DoA relationships were perturbed (local). Adaptation was biased by naturalistic behaviour, but that did not impede subjects from solving the tasks. Strategies that subjects used to solve local adaptation tasks could be biased via tDCS of M1 and the cerebellum. Global and local rotations were adapted to in different ways, with local adaptation lacking the after-effects associated with classical adaptation, indicating the creation of a new internal model for the adapted state, as opposed to alteration of a single one. tDCS affected these forms of adaptation in different ways, with stimulation of M1 predominantly affecting global adaptation and stimulation of the cerebellum predominantly affecting local adaptation. In conclusion, I have demonstrated that the motor system can successfully learn and adapt to abstract motor tasks, with the underlying processes being dependent on M1 and the cerebellum in ways that have a structural dependence

Neural Coordination of Distinct Motor Learning Strategies: Latent Neurofunctional Mechanisms Elucidated via Computational Modeling

In this dissertation, a neurofunctional theory of learning is presented as an extension of functional analysis. This new theory clarifies the distinction— via applied quantitative analysis— between functionally intrinsic (essential) mechanistic structures and irrelevant structural details. This thesis is supported by a review of the relevant literature to provide historical context and sufficient scientific background. Further, the scope of this thesis is elucidated by two questions that are posed from a neurofunctional perspective— (1) how can specialized neuromorphology contribute to the functional dynamics of neural learning processes? (2) Can large-scale neurofunctional pathways emerge via inter-network communication between disparate neural circuits? These questions motivate the specific aims of this dissertation. Each aim is addressed by posing a relevant hypothesis, which is then tested via a neurocomputational experiment. In each experiment, computational techniques are leveraged to elucidate specific mechanisms that underlie neurofunctional learning processes. For instance, the role of specialized neuromorphology is investigated via the development of a computational model that replicates the neurophysiological mechanisms that underlie cholinergic interneurons’ regulation of dopamine in the striatum during reinforcement learning. Another research direction focuses on the emergence of large-scale neurofunctional pathways that connect the cerebellum and basal ganglia— this study also involves the construction of a neurocomputational model. The results of each study illustrate the capability of neurocomputational models to replicate functional learning dynamics of human subjects during a variety of motor adaptation tasks. Finally, the significance— and some potential applications— of neurofunctional theory are discussed

Physical exercise-induced adult neurogenesis: A good strategy to prevent cognitive decline in neurodegenerative diseases?

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Anomalous Perception of Biological Motion in Autism: A Conceptual Review and Meta-Analysis

Despite its popularity, the construct of biological motion (BM) and its putative anomalies in autism spectrum disorder (ASD) are not completely clarified. In this article, we present a meta-analysis investigating the putative anomalies of BM perception in ASD. Through a systematic literature search, we found 30 studies that investigated BM perception in both ASD and typical developing peers by using point-light display stimuli. A general meta-analysis including all these studies showed a moderate deficit of individuals with ASD in BM processing, but also a high heterogeneity. This heterogeneity was explored in different additional meta-analyses where studies were grouped according to levels of complexity of the BM task employed (first-order, direct and instrumental), and according to the manipulation of low-level perceptual features (spatial vs. temporal) of the control stimuli. Results suggest that the most severe deficit in ASD is evident when perception of BM is serving a secondary purpose (e.g., inferring intentionality/action/emotion) and, interestingly, that temporal dynamics of stimuli are an important factor in determining BM processing anomalies in ASD. Our results question the traditional understanding of BM anomalies in ASD as a monolithic deficit and suggest a paradigm shift that deconstructs BM into distinct levels of processing and specific spatio-temporal subcomponents