Respiratory, postural and spatio-kinetic motor stabilization, internal models, top-down timed motor coordination and expanded cerebello-cerebral circuitry: a review

Human dexterity, bipedality, and song/speech vocalization in Homo are reviewed within a motor evolution perspective in regard to 

(i) brain expansion in cerebello-cerebral circuitry, 
(ii) enhanced predictive internal modeling of body kinematics, body kinetics and action organization, 
(iii) motor mastery due to prolonged practice, 
(iv) task-determined top-down, and accurately timed feedforward motor adjustment of multiple-body/artifact elements, and 
(v) reduction in automatic preflex/spinal reflex mechanisms that would otherwise restrict such top-down processes. 

Dual-task interference and developmental neuroimaging research argues that such internal modeling based motor capabilities are concomitant with the evolution of 
(vi) enhanced attentional, executive function and other high-level cognitive processes, and that 
(vii) these provide dexterity, bipedality and vocalization with effector nonspecific neural resources. 

The possibility is also raised that such neural resources could 
(viii) underlie human internal model based nonmotor cognitions. 
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Synthesis of Subject-Specific Human Balance Responses using a Task-Level Neuromuscular Control Platform

Many activities of daily living require a high level of neuromuscular coordination and balance control to avoid falls. Complex musculoskeletal models paired with detailed neuromuscular simulations complement experimental studies and uncover principles of coordinated and uncoordinated movements. Here, we created a closed-loop forward dynamic simulation framework that utilizes a detailed musculoskeletal model (19 degrees of freedom, and 92 Muscles) to synthesize human balance responses after support-surface perturbation. In addition, surrogate response models of task-level experimental kinematics from two healthy subjects were provided as inputs to our closedloop simulations to inform the design of the task-level controller. The predicted muscle EMGs and the resulting synthesized subject joint angles showed good conformity with the average of experimental trials. The simulated whole-body center of mass displacements, generated from a single kinematics trial per perturbation direction, were on average, within 7 mm (anterior perturbations) and 13 mm (posterior perturbations) of experimental displacements. Our results confirmed how a complex subject-specific movement can be reconstructed by sequencing and prioritizing multiple task-level commands to achieve desired movements. By combining the multidisciplinary approaches of robotics and biomechanics, the platform demonstrated here offers great potential for studying human movement control and subject-specific outcome prediction

An Optimal Control Model for Human Postural Regulation

Human upright stance is inherently unstable without a balance control scheme. Many biological behaviors are likely to be optimal with respect to some performance measure that involves energy. It is reasonable to believe that the human is (unconsciously) optimizing some performance measure as he regulates his balance posture. In experimental studies, a notable feature of postural control is a small constant sway. Specifically, there is greater sway than would occur with a linear feedback control without delay. A second notable feature of the human postural control is that the response to perturbations varies with their amplitude. Small disturbances produce motion only at the ankles with the hip and knee angles unchanging. Large perturbation evoke ankle and hip angular movement only. Still larger perturbation result in movement of all three joint angles. Inspired by these features, a biomechanical model resembling human balance control is proposed. The proposed model consists of three main components which are the body dynamics, a sensory estimator for delay and disturbance, and an optimal nonlinear control scheme providing minimum required corrective response. The human body is modeled as a multiple segment inverted pendulum in the sagittal plane and controlled by ankle and hip joint torques. A series of nonlinear optimal control problems are devised as mathematical models of human postural control during quiet standing. Several performance criteria that are high even orders in the body state or functions of these states (such as joint angle, Center of Pressure COP or Center of Mass COM) and quadratic in the joint control are utilized. This objective function provides a trade-off between the allowed deviations of the position from its nominal value and the neuromuscular energy required to correct for these deviations. Note that this performance measure reduces the actuator energy used by penalizing small postural errors very lightly. By using the Model Predictive Control (MPC) technique, the discrete-time approximation to each of these problems can be converted into a nonlinear programming problem and then solved by optimization methods. The solution gives a control scheme that agrees with the main features of the joint kinematics and its coordination process. The derived model is simulated for different scenarios to validate and test the performance of the proposed postural control architecture

Intention Tremor and Deficits of Sensory Feedback Control in Multiple Sclerosis: a Pilot Study

Background Intention tremor and dysmetria are leading causes of upper extremity disability in Multiple Sclerosis (MS). The development of effective therapies to reduce tremor and dysmetria is hampered by insufficient understanding of how the distributed, multi-focal lesions associated with MS impact sensorimotor control in the brain. Here we describe a systems-level approach to characterizing sensorimotor control and use this approach to examine how sensory and motor processes are differentially impacted by MS. Methods Eight subjects with MS and eight age- and gender-matched healthy control subjects performed visually-guided flexion/extension tasks about the elbow to characterize a sensory feedback control model that includes three sensory feedback pathways (one for vision, another for proprioception and a third providing an internal prediction of the sensory consequences of action). The model allows us to characterize impairments in sensory feedback control that contributed to each MS subject’s tremor. Results Models derived from MS subject performance differed from those obtained for control subjects in two ways. First, subjects with MS exhibited markedly increased visual feedback delays, which were uncompensated by internal adaptive mechanisms; stabilization performance in individuals with the longest delays differed most from control subject performance. Second, subjects with MS exhibited misestimates of arm dynamics in a way that was correlated with tremor power. Subject-specific models accurately predicted kinematic performance in a reach and hold task for neurologically-intact control subjects while simulated performance of MS patients had shorter movement intervals and larger endpoint errors than actual subject responses. This difference between simulated and actual performance is consistent with a strategic compensatory trade-off of movement speed for endpoint accuracy. Conclusions Our results suggest that tremor and dysmetria may be caused by limitations in the brain’s ability to adapt sensory feedback mechanisms to compensate for increases in visual information processing time, as well as by errors in compensatory adaptations of internal estimates of arm dynamics

Sensorimotor postural control in healthy and pathological stance and gait

Postural control during standing and walking is an inherently unstable task requiring the interaction of various biomechanical, sensory, and neurophysiological mechanisms to shape stable patterns of whole-body coordination that are able to counteract postural disequilibrium. This thesis focused on the examination of central aspects of the functional roles of these mechanisms and the modes of interaction between them. A further aim was to determine the conditions of dynamic stability for healthy standing and walking performance as well as for certain balance and gait disorders. By studying movement fluctuations in the walking pattern it could be demonstrated that dynamic stability during walking depends on gait speed and is differentially regulated for the medio-lateral and the fore-aft walking planes. Stability control in the fore-aft walking plane exhibits attractor dynamics typical for a dynamical system. Accordingly, the most stable pattern of movement coordination in terms of minimal fluctuations in the order parameter (i.e., the relative phase between the two oscillating legs) can be observed at the attractor of self-paced walking. Critical fluctuations occur at increasingly non-preferred speeds, indicating a loss of dynamic gait stability close to the speed boundaries of the walking mode. Moreover, stability control during slow walking is critically dependent on sensory feedback control, whereas dynamic stability during fast walking relies mainly on the smooth operation of cerebellar pacemaker regions. Disturbances of sensory and cerebellar locomotor control in certain gait disorders could be further linked to a loss of dynamic gait stability, in particular an increased risk of falls. Furthermore, this thesis examined alterations in the sensorimotor postural control scheme that may trigger the experience of subjective imbalance and vertigo in the conditions of phobic postural vertigo and visual height intolerance. Both conditions are characterized by an inadequate mode of balance regulation featuring increased levels of open-loop balance control and a precipitate integration of closed-loop sensory feedback into the postural control scheme. This inadequate balance control strategy is accompanied by a stiffening of the anti-gravity musculature and is elicited by specific influences of attention and sensory feedback control. The findings of this thesis contribute to the understanding of central sensorimotor mechanisms involved in the control of dynamic postural stability during standing and walking. They further provide relevant information for the differential diagnosis and fall risk estimation of certain balance and gait disorders

Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

Identifying Plant and Feedback in Human Posture Control

Human upright bipedal stance is a classic example of a control system consisting of a plant (i.e., the physical body and its actuators) and feedback (i.e., neural control) operating continuously in a closed loop. Determining the mechanistic basis of behavior in a closed loop control system is problematic because experimental manipulations or deficits due to trauma/injury influence all parts of the loop. Moreover, experimental techniques to open the loop (e.g., isolate the plant) are not viable because bipedal upright stance is not possible without feedback. The goal of the proposed study is to use a technique called closed loop system identification (CLSI) to investigate properties of the plant and feedback separately. Human upright stance has typically been approximated as a single-joint inverted pendulum, simplifying not only the control of a multi-linked body but also how sensory information is processed relative to body dynamics. However, a recent study showed that a single-joint approximation is inadequate. Trunk and leg segments are in-phase at frequencies below 1 Hz of body sway and simultaneously anti-phase at frequencies above 1 Hz during quiet stance. My dissertation studies have investigated the coordination between the leg and trunk segments and how sensory information is processed relative to that coordination. For example, additional sensory information provided through visual or light touch information led to a change of the in-phase pattern but not the anti-phase pattern, indicating that the anti-phase pattern may not be neurally controlled, but more a function of biomechanical properties of a two-segment body. In a subsequent study, I probed whether an internal model of the body processes visual information relative to a single or double-linked body. The results suggested a simple control strategy that processes sensory information relative to a single-joint internal model providing further evidence that the anti-phase pattern is biomechanically driven. These studies suggest potential mechanisms but cannot rule out alternative hypotheses because the source of behavioral changes can be attributed to properties of the plant and/or feedback. Here I adopt the CLSI approach using perturbations to probe separate processes within the postural control loop. Mechanical perturbations introduce sway as an input to the feedback, which in turn generates muscle activity as an output. Visual perturbations elicit muscle activity (a motor command) as an input to the plant, which then triggers body sway as an output. Mappings of muscle activity to body sway and body sway to muscle activity are used to identify properties of the plant and feedback, respectively. The results suggest that feedback compensates for the low-pass properties of the plant, except at higher frequencies. An optimal control model minimizing the amount of muscle activation suggests that the mechanism underlying this lack of compensation may be due to an uncompensated time delay. These techniques have the potential for more precise identification of the source of deficits in the postural control loop, leading to improved rehabilitation techniques and treatment of balance deficits, which currently contributes to 40% of nursing home admissions and costs the US health care system over $20B per year

A Nonlinear Dynamic Approach for Evaluating Postural Control

Recent research suggests that traditional biomechanical models of postural stability do not fully characterise the nonlinear properties of postural control. In sports medicine, this limitation is manifest in the postural steadiness assessment approach, which may not be sufficient for detecting the presence of subtle physiological change after injury. The limitation is especially relevant given that return-to-play decisions are being made based on assessment results. This update first reviews the theoretical foundation and limitations of the traditional postural stability paradigm. It then offers, using the clinical example of athletes recovering from cerebral concussion, an alternative theoretical proposition for measuring changes in postural control by applying a nonlinear dynamic measure known as ‘approximate entropy’. Approximate entropy shows promise as a valuable means of detecting previously unrecognised, subtle physiological changes after concussion. It is recommended as an important supplemental assessment tool for determining an athlete’s readiness to resume competitive activity

Analysis of Connectivity in EMG Signals to Examine Neural Correlations in Muscular Activation of Lower Leg Muscles for Postural Stability

In quiet standing the central nervous systems implements a pre-programmed ankle strategy of postural control to maintain upright balance and stability. This strategy is comprised of a synchronized common neural drive being delivered to synergistically grouped muscles. In this study connectivity between EMG signals of unilateral and bilateral homologous muscle pairs, of the lower legs, during various standing balance conditions was evaluated using magnitude squared coherence (MSC) and mutual information (MI). The leg muscles of interest were the tibialis anterior (TA), medial gastrocnemius (MG), and the soleus (S) of both legs. MSC is a linear measure of the phase relation between two signals in the frequency domain. MI is an information theoretic measure of the amount of information two signals have in common. Both MSC and MI were analyzed in the delta (0.5 – 4 Hz), theta (4 – 8 Hz), alpha (8 – 13 Hz), beta (13 – 30 Hz), and gamma (30 – 100 Hz) neural frequency bands for feet together and feet tandem, with eyes open and eyes closed conditions. Both MSC and MI found that overall connectivity was highest in the delta band followed by the theta band. Connectivity in the beta and lower gamma bands (30 – 60 Hz) was influenced by standing balance condition and indicative of a neural drive originating from the motor cortex. Instability was evaluated by comparing less stable standing conditions with a baseline eyes open, feet together stance. Changes in connectivity in the beta and gamma bands were found be most significant in the muscle pairs of the back leg of tandem stance regardless of foot dominance. MI was found to be a better connectivity analysis method by identifying significance of increased connectivity in the agonistic muscle pair between the MG:S, the antagonistic muscle pair between TA:S, and all the bilateral homologous muscle pairs. MSC was only able to identify the MG:S muscle pair as significant. The results of this study provided insight into the neural mechanism of postural control and presented an alternative connectivity analysis method of MI

Fractal Analysis Design for Distinguishing Subject Characteristics on Motor Control of Neck Pain Patients

2011-2012 > Academic research: refereed > Chapter in an edited book (author