Stochastic order parameter equation of isometric force production revealed by drift-diffusion estimates

Performance, complexity and dynamics of force maintenance and modulation in young and older adults

The present study addresses how task constraints and aging influence isometric force control. We used two tasks requiring either force maintenance (straight line target force) or force modulation (sine-wave target force) around different force levels and at different modulation frequencies. Force levels were defined relative the individual maximum voluntary contraction. A group of young adults (mean age ± SD = 25 ± 3.6 years) and a group of elderly (mean age = 77 ± 6.4 years) took part in the study. Age- and task-related effects were assessed through differences in: (i) force control accuracy, (ii) time-structure of force fluctuations, and (iii) the contribution of deterministic (predictable) and stochastic (noise-like) dynamic components to the expressed behavior. Performance-wise, the elderly showed a pervasive lower accuracy and higher variability than the young participants. The analysis of fluctuations showed that the elderly produced force signals that were less complex than those of the young adults during the maintenance task, but the reverse was observed in the modulation task. Behavioral complexity results suggest a reduced adaptability to task-constraints with advanced age. Regarding the dynamics, we found comparable generating mechanisms in both age groups for both tasks and in all conditions, namely a fixed-point for force maintenance and a limit-cycle for force modulation. However, aging increased the stochasticity (noise-driven fluctuations) of force fluctuations in the cyclic force modulation, which could be related to the increased complexity found in elderly for this same task. To our knowledge this is the first time that these different perspectives to motor control are used simultaneously to characterize force control capacities. Our findings show their complementarity in revealing distinct aspects of sensorimotor adaptation to task constraints and age-related declines. Although further research is still needed to identify the physiological underpinnings, the used task and methodology are shown to have both fundamental and clinical applications.

Improving Precision Force Control With Low-Frequency Error Amplification Feedback: Behavioral and Neurophysiological Mechanisms

Although error amplification (EA) feedback has been shown to improve performance on visuomotor tasks, the challenge of EA is that it concurrently magnifies task-irrelevant information that may impair visuomotor control. The purpose of this study was to improve the force control in a static task by preclusion of high-oscillatory components in EA feedback that cannot be timely used for error correction by the visuomotor system. Along with motor unit behaviors and corticomuscular coherence, force fluctuations (Fc) were modeled with non-linear SDA to contrast the reliance of the feedback process and underlying neurophysiological mechanisms by using real feedback, EA, and low-frequency error amplification (LF-EA). During the static force task in the experiment, the EA feedback virtually potentiated the size of visual error, whereas the LF-EA did not channel high-frequency errors above 0.8 Hz into the amplification process. The results showed that task accuracy was greater with the LF-EA than with the real and EA feedback modes, and that LF-EA led to smaller and more complex Fc. LF-EA generally led to smaller SDA variables of Fc (critical time points, critical point of Fc, the short-term effective diffusion coefficient, and short-term exponent scaling) than did real feedback and EA. The use of LF-EA feedback increased the irregularity of the ISIs of MUs but decreased the RMS of the mean discharge rate, estimated with pooled MU spike trains. Beta-range EEG–EMG coherence spectra (13–35 Hz) in the LF-EA condition were the greatest among the three feedback conditions. In summary, amplification of low-frequency errors improves force control by shifting the relative significances of the feedforward and feedback processes. The functional benefit arises from the increase in the common descending drive to promote a stable state of MU discharges.

Improved Neural Control of Movements Manifests in Expertise-Related Differences in Force Output and Brain Network Dynamics

It is well-established that expertise developed through continuous and deliberate practice has the potential to delay age-related decline in fine motor skills. However, less is known about the underlying mechanisms, that is, whether expertise leads to a higher performance level changing the initial status from which age-related decline starts or if expertise-related changes result in qualitatively different motor output and neural processing providing a resource of compensation for age-related changes. Thus, as a first step, this study aims at a better understanding of expertise-related changes in fine motor control with respect to force output and respective electrophysiological correlates. Here, using a multidimensional approach, we investigated fine motor control of experts and novices in precision mechanics during the execution of a dynamic force control task. On the level of force output, we analyzed precision, variability, and complexity. We further used dynamic mode decomposition (DMD) to analyze the electrophysiological correlates of force control to deduce brain network dynamics. Experts’ force output was more precise, less variable, and more complex. Task-related DMD mean mode magnitudes within the α-band at electrodes over sensorimotor relevant areas were reduced in experts, and lower DMD mean mode magnitudes related to the force output in novices. Our results provide evidence for expertise dependent central adaptions with distinct and more complex organization and decentralization of sensorimotor subsystems. Results from our multidimensional approach can be seen as a step forward in understanding expertise-related changes and exploiting their potential as resources for healthy aging.

Dynamical signatures of isometric force control as a function of age, expertise, and task constraints

From the conceptual and methodological framework of the dynamical systems approach, force control results from complex interactions of various subsystems yielding observable behavioral fluctuations, which comprise both deterministic (predictable) and stochastic (noise-like) dynamical components. Here, we investigated these components contributing to the observed variability in force control in groups of participants differing in age and expertise level. To this aim, young (18-25 yr) as well as late middle-aged (55-65 yr) novices and experts (precision mechanics) performed a force maintenance and a force modulation task. Results showed that whereas the amplitude of force variability did not differ across groups in the maintenance tasks, in the modulation task it was higher for late middle-aged novices than for experts and higher for both these groups than for young participants. Within both tasks and for all groups, stochastic fluctuations were lowest where the deterministic influence was smallest. However, although all groups showed similar dynamics underlying force control in the maintenance task, a group effect was found for deterministic and stochastic fluctuations in the modulation task. The latter findings imply that both components were involved in the observed group differences in the variability of force fluctuations in the modulation task. These findings suggest that between groups the general characteristics of the dynamics do not differ in either task and that force control is more affected by age than by expertise. However, expertise seems to counteract some of the age effects.NEW & NOTEWORTHY Stochastic and deterministic dynamical components contribute to force production. Dynamical signatures differ between force maintenance and cyclic force modulation tasks but hardly between age and expertise groups. Differences in both stochastic and deterministic components are associated with group differences in behavioral variability, and observed behavioral variability is more strongly task dependent than person dependent.

Non-equilibrium thermodynamical description of rhythmic motion patterns of active systems: a canonical-dissipative approach

We derive explicit expressions for the non-equilibrium thermodynamical variables of a canonical-dissipative limit cycle oscillator describing rhythmic motion patterns of active systems. These variables are statistical entropy, non-equilibrium internal energy, and non-equilibrium free energy. In particular, the expression for the non-equilibrium free energy is derived as a function of a suitable control parameter. The control parameter determines the Hopf bifurcation point of the deterministic active system and describes the effective pumping of the oscillator. In analogy to the equilibrium free energy of the Landau theory, it is shown that the non-equilibrium free energy decays as a function of the control parameter. In doing so, a similarity between certain equilibrium and non-equilibrium phase transitions is pointed out. Data from an experiment on human rhythmic movements is presented. Estimates for pumping intensity as well as the thermodynamical variables are reported. It is shown that in the experiment the non-equilibrium free energy decayed when pumping intensity was increased, which is consistent with the theory. Moreover, pumping intensities close to zero could be observed at relatively slow intended rhythmic movements. In view of the Hopf bifurcation underlying the limit cycle oscillator model, this observation suggests that the intended limit cycle movements were actually more similar to trajectories of a randomly perturbed stable focus.

Children with cerebral palsy have greater stochastic features present in the variability of their gait kinematics

Children with CP have a more variable gait pattern. However, it is currently unknown if these variations arise from deterministic variations that are a result of a change in the motor command or stochastic features that are present in the nervous system. The aim of this investigation was to use a Langevin equation methodology to evaluate the deterministic and stochastic features that are present in the variability of the gait kinematics of children with cerebral palsy (CP). Ten children with spastic diplegic CP and nine typically developing (TD) children participated in this investigation. All of the children walked on a treadmill for 2 min while a three-dimensional motion capture system recorded the step kinematics. Our major findings for this investigation were: (1) children with CP had greater variability in their gait patterns than TD children, (2) the variability of the children with CP and TD children had similar deterministic features, (3) the variability had greater stochastic features for the children with CP, and (4) the increase in the amount of variability was strongly correlated with the increase in stochastic features. These results indicate that the variability seen in the gait patterns of children with CP may be due to the inability to suppress the noise that is present in the neuromuscular system.

On a multistable dynamic model of behavioral and perceptual infant development

In this theoretical work, we treat behavioral and perceptual issues on an equal footing and examine the emergence of mutually exclusive behavioral patterns and perceptual variables during infant development from the perspective of multistable competitive dynamic systems. Accordingly, behavioral modes and modes of perception compete with each other for activation. One and only one mode survives the mode-mode competition, which accounts for the incompatibility of modes being considered. However, the winning behavioral or perceptual state is not predefined. Rather, we argue that during particular stages of maturation multiple modes coexist for the same set of developmental, body-scaled, and environmental parameters or constraints. The winning behavioral or perceptual state depends on these parameters as well as on initial conditions as operationalized in terms of previously performed behaviors or utilized perceptual stimuli. We give explicit examples of our approach and address the emergence of two-handed grasping and catching movements and the emergence of monocular and binocular vision during infant development. In particular, we propose that the emergence of midline crossing movements in 3- to 6-month-old infants involves two independent but interaction control parameters: a body-scaled and a developmental one. Likewise, we argue that the onset of binocularity in infants involves two independent but interaction control parameters: a developmental and an environmental one.

Noise in the nervous system

Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.

Changes in the degree of motor variability associated with experimental and chronic neck-shoulder pain during a standardised repetitive arm movement

The aim of the present study was to investigate the effect of experimental and chronic neck-shoulder pain on the magnitude of cycle-to-cycle variability of task timing, kinematics and muscle activation during repetitive arm movement performed for 3 or 5 min. In an experimental part, acute muscle pain was induced in healthy subjects by intramuscular injection of hypertonic saline in trapezius (n = 10) and infraspinatus (n = 10) muscles. In a clinical part, workers with (n = 12) and without (n = 6) chronic neck-shoulder pain were compared. Cycle-to-cycle standard deviations of task duration, arm and trunk movement in 3D and surface electromyographic (EMG) root mean square activity were computed to assess the degree of variability. The variability in task timing increased in presence of both experimental and chronic pain (P < 0.05) compared with non-painful conditions. Experimental pain increased the variability of the starting position of the arm (P < 0.05), the arm range of motion (P < 0.01), the arm and trunk movement area (P < 0.01) and the acceleration of the arm (P < 0.01). In the chronic pain condition, the variability of arm and trunk acceleration (P < 0.01) and EMG activity (P < 0.05) was decreased compared with healthy controls. These results indicate that pain alters the magnitude of motor variability, and that the transition from acute to chronic pain is accompanied by changes in motor patterns. Experimental pain likely resulted in a quest for a motor solution reducing nociceptive influx, while chronic pain was characterised by a diminished motor flexibility.

Probabilistic models in human sensorimotor control

Sensory and motor uncertainty form a fundamental constraint on human sensorimotor control. Bayesian decision theory (BDT) has emerged as a unifying framework to understand how the central nervous system performs optimal estimation and control in the face of such uncertainty. BDT has two components: Bayesian statistics and decision theory. Here we review Bayesian statistics and show how it applies to estimating the state of the world and our own body. Recent results suggest that when learning novel tasks we are able to learn the statistical properties of both the world and our own sensory apparatus so as to perform estimation using Bayesian statistics. We review studies which suggest that humans can combine multiple sources of information to form maximum likelihood estimates, can incorporate prior beliefs about possible states of the world so as to generate maximum a posteriori estimates and can use Kalman filter-based processes to estimate time-varying states. Finally, we review Bayesian decision theory in motor control and how the central nervous system processes errors to determine loss functions and select optimal actions. We review results that suggest we plan movements based on statistics of our actions that result from signal-dependent noise on our motor outputs. Taken together these studies provide a statistical framework for how the motor system performs in the presence of uncertainty.