Unravelling developmental disregard in children with unilateral cerebral palsy by measuring event-related potentials during a simple and complex task

Background

In a subset of children with unilateral Cerebral Palsy (CP) a discrepancy between capacity and performance of the affected upper limb can be observed. This discrepancy is known as Developmental Disregard (DD). Though the phenomenon of DD has been well documented, its underlying cause is still under debate. DD has originally been explained based on principles of operant conditioning. Alternatively, it has been proposed that DD results from a diminished automaticity of movements, resulting in an increased cognitive load when using the affected hand. To investigate the amount of involved cognitive load we studied Event-Related Potentials (ERPs) preceding task-related motor responses during a single-hand capacity and a dual-hand performance task. It was hypothesised that children with DD show alterations related to long-latency ERP components when selecting a response with the affected upper limb, reflecting increased cognitive load in order to generate an adequate response and especially so within the dual-hand task.

Methods

Fifteen children with unilateral CP participated in the study. One of the participants was excluded due to major visual impairments. Seven of the remaining participants displayed DD. The other seven children served as a control group. All participants performed two versions of a cue-target paradigm, a single-hand capacity and a dual-hand performance task. The ERP components linked to target presentation were inspected: the mid-latency P2 component and the consecutive long-latency N2b component.

Results

In the dual-hand performance task children with DD showed an enhancement in mean amplitude of the long-latency N2b component when selecting a response with their affected hand. No differences were found regarding the amplitude of the mid-latency P2 component. No differences were observed regarding the single-hand capacity task. The control group did not display any differences in ERPs linked to target evaluation processes between both hands.

Conclusion

These electrophysiological findings show that DD is associated with increased cognitive load when movements are prepared with the affected hand during a dual-hand performance task. These findings confirm behavioural observations, advance our insights on the neural substrate of DD and have implications for therapy.     

From Single Motor Unit Activity to Multiple Grip Forces: Mini-review of Multi-digit Grasping

This paper is a mini review of kinetic and kinematic evidenceon the control of the hand with emphasis on grasping. It isnot meant to be an exhaustive review, rather it summarizes currentresearch examining the mechanisms through which specific patternsof coordination are elicited and observed during reach to graspmovements and static grasping. These coordination patterns includethe spatial and temporal covariation of the rotation at multiplejoints during reach to grasp movements. A basic coordinationbetween grip forces produced by multiple digits also occursduring whole hand grasping such that normal forces tend to beproduced in a synchronous fashion across pairs of digits. Finally,we address current research that suggests that motor unit synchronyacross hand muscles and muscle compartments might be one ofthe neural mechanisms underlying the control of grasping.     

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

There are several advantages that functional near-infrared spectroscopy (fNIRS) presents in the study of the neural control of human movement. It is relatively flexible with respect to participant positioning and allows for some head movements during tasks. Additionally, it is inexpensive, light weight, and portable, with very few contraindications to its use. This presents a unique opportunity to study functional brain activity during motor tasks in individuals who are typically developing, as well as those with movement disorders, such as cerebral palsy. An additional consideration when studying movement disorders, however, is the quality of actual movements performed and the potential for additional, unintended movements. Therefore, concurrent monitoring of both blood flow changes in the brain and actual movements of the body during testing is required for appropriate interpretation of fNIRS results. Here, we show a protocol for the combination of fNIRS with muscle and kinematic monitoring during motor tasks. We explore gait, a unilateral multi-joint movement (cycling), and two unilateral single-joint movements (isolated ankle dorsiflexion, and isolated hand squeezing). The techniques presented can be useful in studying both typical and atypical motor control, and can be modified to investigate a broad range of tasks and scientific questions.     

fMRI responses of the brain during active and passive movements in left-handed subjects

The hemodynamic (magnetic resonance imaging, fMRI, 3T) brain responses were studied in 15 left-handed healthy subjects performing active and passive movements of the dominant and non-dominant hands. Group and individual fMRI responses to the motor load were analyzed. It was found that, during the active movements of dominant and non-dominant hands, the main activation cluster appeared in the preand postcentral gyrus of the contralateral hemisphere and which topographically similar during active and passive movements. The activation cluster of greater volume was identified in these areas; the response was more diffused during the non-dominant hand movements in comparison with the dominant hand. During passive movements, the cortical activation clusters of a smaller volume in comparison with the active movements were found, which was expressed most clearly during the performance of non-dominant hand movements and could reflect the weakening of the control from the cortical structures in these conditions.     

Does the brain use sliding variables for the control of movements?

Delays in the transmission of sensory and motor information prevent errors from being instantaneously available to the central nervous system (CNS) and can reduce the stability of a closed-loop control strategy. On the other hand, the use of a pure feedforward control (inverse dynamics) requires a perfect knowledge of the dynamic behavior of the body and of manipulated objects. Sensory feedback is essential both to accommodate unexpected errors and events and to compensate for uncertainties about the dynamics of the body. Experimental observations concerning the control of posture, gaze and limbs have shown that the CNS certainly uses a combination of closed-loop and open-loop control. Feedforward components of movement, such as eye saccades, occur intermittently and present a stereotyped kinematic profile. In visuo-manual tracking tasks, hand movements exhibit velocity peaks that occur intermittently. When a delay or a slow dynamics are inserted in the visuo-manual control loop, intermittent step-and-hold movements appear clearly in the hand trajectory. In this study, we investigated strategies used by human subjects involved in the control of a particular dynamic system. We found strong evidence for substantial nonlinearities in the commands produced. The presence of step-and-hold movements seemed to be the major source of nonlinearities in the control loop. Furthermore, the stereotyped ballistic-like kinematics of these rapid and corrective movements suggests that they were produced in an open-loop way by the CNS. We analyzed the generation of ballistic movements in the light of sliding control theory assuming that they occurred when a sliding variable exceeded a constant threshold. In this framework, a sliding variable is defined as a composite variable (a combination of the instantaneous tracking error and its temporal derivatives) that fulfills a specific stability criterion. Based on this hypothesis and on the assumption of a constant reaction time, the tracking error and its derivatives should be correlated at a particular time lag before movement onset. A peak of correlation was found for a physiologically plausible reaction time, corresponding to a stable composite variable. The direction and amplitude of the ongoing stereotyped movements seemed also be adjusted in order to minimize this variable. These findings suggest that, during visually guided movements, human subjects attempt to minimize such a composite variable and not the instantaneous error. This minimization seems to be obtained by the execution of stereotyped corrective movements. Received: 18 February 1997 / Accepted in revised form: 29 July 1997     

Two Agents in the Brain: Motor Control of Unimanual and Bimanual Reaching Movements

Previous studies have suggested that the left and right hands have different specialties for motor control that can be represented as two agents in the brain. This study examined how coordinated movements are performed during bimanual reaching tasks to highlight differences in the characteristics of the hands. We examined motor movement accuracy, reaction time, and movement time in right-handed subjects performing a three-dimensional motor control task (visually guided reaching). In the no-visual-feedback condition, right-hand movement had lower accuracy and a shorter reaction time than did left-hand movement, whereas bimanual movement had the longest reaction time, but the best accuracy. This suggests that the two hands have different internal models and specialties: closed-loop control for the right hand and open-loop control for the left hand. Consequently, during bimanual movements, both models might be used, creating better control and planning (or prediction), but requiring more computation time compared to the use of one hand only.     

Concurrent adaptation of force and impedance in the redundant muscle system

This article examines the validity of a model to explain how humans learn to perform movements in environments with novel dynamics, including unstable dynamics typical of tool use. In this model, a simple rule specifies how the activation of each muscle is adapted from one movement to the next. Simulations of multijoint arm movements with a neuromuscular plant that incorporates neural delays, reflexes, and signal-dependent noise, demonstrate that the controller is able to compensate for changing internal or environment dynamics and noise properties. The computational model adapts by learning both the appropriate forces and required limb impedance to compensate precisely for forces and instabilities in arbitrary directions with patterns similar to those observed in motor learning experiments. It learns to regulate reciprocal activation and co-activation in a redundant muscle system during repeated movements without requiring any explicit transformation from hand to muscle space. Independent error-driven change in the activation of each muscle results in a coordinated control of the redundant muscle system and in a behavior that reduces instability, systematic error, and energy.     

Concatenation of Observed Grasp Phases with Observer’s Distal Movements: A Behavioural and TMS Study

The present study aimed at determining how actions executed by two conspecifics can be coordinated with each other, or more specifically, how the observation of different phases of a reaching-grasping action is temporary related to the execution of a movement of the observer. Participants observed postures of initial finger opening, maximal finger aperture, and final finger closing of grasp after observation of an initial hand posture. Then, they opened or closed their right thumb and index finger (experiments 1, 2 and 3). Response times decreased, whereas acceleration and velocity of actual finger movements increased when observing the two late phases of grasp. In addition, the results ruled out the possibility that this effect was due to salience of the visual stimulus when the hand was close to the target and confirmed an effect of even hand postures in addition to hand apparent motion due to the succession of initial hand posture and grasp phase. In experiments 4 and 5, the observation of grasp phases modulated even foot movements and pronunciation of syllables. Finally, in experiment 6, transcranial magnetic stimulation applied to primary motor cortex 300 ms post-stimulus induced an increase in hand motor evoked potentials of opponens pollicis muscle when observing the two late phases of grasp. These data suggest that the observation of grasp phases induced simulation which was stronger during observation of finger closing. This produced shorter response times, greater acceleration and velocity of the successive movement. In general, our data suggest best concatenation between two movements (one observed and the other executed) when the observed (and simulated) movement was to be accomplished. The mechanism joining the observation of a conspecific’s action with our own movement may be precursor of social functions. It may be at the basis for interactions between conspecifics, and related to communication between individuals.     

Generalization in Adaptation to Stable and Unstable Dynamics

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.     

Evidence of muscle synergies during human grasping

Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas, so far, the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e. that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.     

Cortical control of grasp in non-human primates

The skilled use of the hand for grasping and manipulation of objects is a fundamental feature of the primate motor system. Grasping movements involve transforming the visual information about an object into a motor command appropriate for the coordinated activation of hand and finger muscles. The cerebral cortex and its descending projections to the spinal cord are known to play a crucial role for the control of grasp. Recent studies in non-human primates have provided some striking new insights into the respective contribution of the parietal and frontal motor cortical areas to the control of grasp. Also, new approaches allowed investigating the coupling of grasp-related activity in different cortical areas for the control of the descending motor command.     

Impedance characteristics of a neuromusculoskeletal model of the human arm I. Posture control

The mechanical impedance of neuromusculoskeletal models of the human arm is studied in this paper. The model analysis provides a better understanding of the contributions of possible intrinsic and reflexive components of arm impedance, makes clear the limitations of second-order mass-viscosity-stiffness models and reveals possible task effects on the impedance. The musculoskeletal model describes planar movements of the upper arm and forearm, which are moved by six lumped muscles with nonlinear dynamics. The motor control system is represented by a neural network which combines feedforward and feedback control. It is optimized for the control of movements or for posture control in the presence of external forces. The achieved impedance characteristics depend on the conditions during the learning process. In particular, the impedance is adapted in a suitable way to the frequency content and direction of external forces acting on the hand during an isometric task. The impedance characteristics of a model, which is optimized for movement control, are similar to experimental data in the literature. The achieved stiffness is, to a large extent, reflexively determined whereas the approximated viscosity is primarily due to intrinsic attributes. It is argued that usually applied Hill-type muscle models do not properly represent intrinsic muscle stiffness. Received: 14 October 1997 / Accepted in revised form: 18 May 1999     

Analysis of an optimal control model of multi-joint arm movements

 In this paper, we propose a model of biological motor control for generation of goal-directed multi-joint arm movements, and study the formation of muscle control inputs and invariant kinematic features of movements. The model has a hierarchical structure that can determine the control inputs for a set of redundant muscles without any inverse computation. Calculation of motor commands is divided into two stages, each of which performs a transformation of motor commands from one coordinate system to another. At the first level, a central controller in the brain accepts instructions from higher centers, which represent the motor goal in the Cartesian space. The controller computes joint equilibrium trajectories and excitation signals according to a minimum effort criterion. At the second level, a neural network in the spinal cord translates the excitation signals and equilibrium trajectories into control commands to three pairs of antagonist muscles which are redundant for a two-joint arm. No inverse computation is required in the determination of individual muscle commands. The minimum effort controller can produce arm movements whose dynamic and kinematic features are similar to those of voluntary arm movements. For fast movements, the hand approaches a target position along a near-straight path with a smooth bell-shaped velocity. The equilibrium trajectories in X and Y show an ‘N’ shape, but the end-point equilibrium path zigzags around the hand path. Joint movements are not always smooth. Joint reversal is found in movements in some directions. The excitation signals have a triphasic (or biphasic) pulse pattern, which leads to stereotyped triphasic (or biphasic) bursts in muscle control inputs, and a dynamically modulated joint stiffness. There is a fixed sequence of muscle activation from proximal muscles to distal muscles. The order is preserved in all movements. For slow movements, it is shown that a constant joint stiffness is necessary to produce a smooth movement with a bell-shaped velocity. Scaled movements can be reproduced by varying the constraints on the maximal level of excitation signals according to the speed of movement. When the inertial parameters of the arm are altered, movement trajectories can be kept invariant by adjusting the pulse height values, showing the ability to adapt to load changes. These results agree with a wide range of experimental observations on human voluntary movements. Received: 4 December 1995 / Accepted in revised form: 17 September 1996     

Flexible Switching of Feedback Control Mechanisms Allows for Learning of Different Task Dynamics

To produce skilled movements, the brain flexibly adapts to different task requirements and movement contexts. Two core abilities underlie this flexibility. First, depending on the task, the motor system must rapidly switch the way it produces motor commands and how it corrects movements online, i.e. it switches between different (feedback) control policies. Second, it must also adapt to environmental changes for different tasks separately. Here we show these two abilities are related. In a bimanual movement task, we show that participants can switch on a movement-by-movement basis between two feedback control policies, depending only on a static visual cue. When this cue indicates that the hands control separate objects, reactions to force field perturbations of each arm are purely unilateral. In contrast, when the visual cue indicates a commonly controlled object, reactions are shared across hands. Participants are also able to learn different force fields associated with a visual cue. This is however only the case when the visual cue is associated with different feedback control policies. These results indicate that when the motor system can flexibly switch between different control policies, it is also able to adapt separately to the dynamics of different environmental contexts. In contrast, visual cues that are not associated with different control policies are not effective for learning different task dynamics.     

Beyond Muscles Stiffness: Importance of State-Estimation to Account for Very Fast Motor Corrections

Feedback delays are a major challenge for any controlled process, and yet we are able to easily control limb movements with speed and grace. A popular hypothesis suggests that the brain largely mitigates the impact of feedback delays (∼50 ms) by regulating the limb intrinsic visco-elastic properties (or impedance) with muscle co-contraction, which generates forces proportional to changes in joint angle and velocity with zero delay. Although attractive, this hypothesis is often based on estimates of limb impedance that include neural feedback, and therefore describe the entire motor system. In addition, this approach does not systematically take into account that muscles exhibit high intrinsic impedance only for small perturbations (short-range impedance). As a consequence, it remains unclear how the nervous system handles large perturbations, as well as disturbances encountered during movement when short-range impedance cannot contribute. We address this issue by comparing feedback responses to load pulses applied to the elbow of human subjects with theoretical simulations. After validating the model parameters, we show that the ability of humans to generate fast and accurate corrective movements is compatible with a control strategy based on state estimation. We also highlight the merits of delay-uncompensated robust control, which can mitigate the impact of internal model errors, but at the cost of slowing feedback corrections. We speculate that the puzzling observation of presynaptic inhibition of peripheral afferents in the spinal cord at movement onset helps to counter the destabilizing transition from high muscle impedance during posture to low muscle impedance during movement.     

Differences between grab rail position and orientation during the assisted sit-to-stand for able-bodied older adults

The aim of this study was to compare the effects of grab rail position, orientation, and number of hands used on the kinetics of assisted sit-to-stand transfers. Participants were 12 able-bodied older adults between the ages of 69 and 88 years. While each one performed the sit-to-stand transfer, a motion analysis system with 9 cameras recording at 60 Hz tracked the 3-D trajectories of retroreflective markers. Bilateral 37-D platform, grab rail, and seat force data were collected at 200 Hz and normalized to participant body weight. Four lateral conditions were tested: vertical, 45 degrees inclined, and horizontal with the hand placed at 150 mm and 400 mm forward of the seat front edge. Four anterior conditions were tested: vertical and horizontal orientations with the use of one hand and two hands. Posterior grab rail force increased with anterior assistance and with two-hand use compared to lateral assistance and single hand use, respectively. The selection of grab rail position and the number of hands incorporated during assistance also determined the symmetry of anteroposterior net joint forces, net joint moments, and joint powers. Grab rail orientation determined the height of the gripping hand which influenced the assistance strategy. Grab rail position, orientation, and the amount of upper body contribution influenced the assisted sit-to-stand transfer. These kinetic responses to grab rail location require careful consideration in order to optimize grab rail assistance during the sit-to-stand transfer.     

Effects of reversible inactivation of the supplementary motor area (SMA) on unimanual grasp and bimanual pull and grasp performance in monkeys

Abstract The supplementary motor area (SMA) was reversibly inactivated by muscimol microinfusion in two monkeys while they were performing two motor tasks: (1) a delayed conditional bimanual drawer pulling and grasping sequence which was initiated on a self-paced basis; (2) a unimanual reach and grasp task (modified Kluver board task). Unilateral or bilateral inactivation of the SMA induced a prominent deficit in trial initiation of bimanual sequential movements, affecting the hand contralateral to the inactivated side or both hands, respectively. The deficit was a long lasting (10-15 min or more) inability of the monkey to place its hand (s) in the ready position on start touch-sensitive pads, a condition required to initiate the drawer task. However, if after such a deficit period, the experimenter put his hand on the start touch-sensitive pad to initiate the trial, then the monkey executed the drawer task without obvious motor deficit. SMA inactivation did not affect unimanual reaching and grasping movements in the board task. In contrast to the SMA, inactivation of other motor areas (primary, premotor dorsal, anterior intraparietal area) did not affect the initiation of movement sequences in the drawer task. These data thus indicate that the SMA plays a crucial and specific role in initiation of self-paced movement sequences. However, SMA inactivation did not prevent the monkeys to perform coordinated movements of the two forelimbs and hands, indicating that SMA is not necessary for bimanual coordination.     

Study of motor cortex excitability in the load holding task

Changes in the amplitude of hand muscle responses to a series of ten stimuli applied to the motor cortex has been studied in subjects holding a small load for 3 min. The amplitude of muscle responses and the background activity decreased with time as compared to the initial level. Regression analysis showed that the muscle response amplitude decreased with the number of stimuli to a greater extent than the background activity. Comparison of the parameters of hand muscle activity during load holding in the stable and unstable equilibrium positions showed that the decrease in the muscle response to motor cortex stimulation during load holding in a state of unstable equilibrium is less pronounced than during load holding in a state of stable equilibrium. For the forearm muscles, the muscle response amplitude and background activity decreased less with the number of stimuli, and this decrease did not depend on the stability of the load position. It may be supposed that the evoked responses decreased more rapidly than the background activity because the motor cortex is involved in the adjustment of the level of muscle activity at the stage of the development of the program for the performance of motor tasks and then transfers the control to subcortical structures.     

Abnormal Movement Preparation in Task-Specific Focal Hand Dystonia

Electrophysiological and behavioral studies in primary dystonia suggest abnormalities during movement preparation, but this crucial phase preceding movement onset has not yet been studied specifically with functional magnetic resonance imaging (fMRI). To identify abnormalities in brain activation during movement preparation, we used event-related fMRI to analyze behaviorally unimpaired sequential finger movements in 18 patients with task-specific focal hand dystonia (FHD) and 18 healthy subjects. Patients and controls executed self-initiated or externally cued prelearnt four-digit sequential movements using either right or left hands. In FHD patients, motor performance of the sequential finger task was not associated with task-related dystonic posturing and their activation levels during motor execution were highly comparable with controls. On the other hand reduced activation was observed during movement preparation in the FHD patients in left premotor cortex / precentral gyrus for all conditions, and for self-initiation additionally in supplementary motor area, left mid-insula and anterior putamen, independent of effector side. Findings argue for abnormalities of early stages of motor control in FHD, manifesting during movement preparation. Since deficits map to regions involved in the coding of motor programs, we propose that task-specific dystonia is characterized by abnormalities during recruitment of motor programs: these do not manifest at the behavioral level during simple automated movements, however, errors in motor programs of complex movements established by extensive practice (a core feature of FHD), trigger the inappropriate movement patterns observed in task-specific dystonia.     

An approximate stochastic optimal control framework to simulate nonlinear neuro-musculoskeletal models in the presence of noise

Optimal control simulations have shown that both musculoskeletal dynamics and physiological noise are important determinants of movement. However, due to the limited efficiency of available computational tools, deterministic simulations of movement focus on accurately modelling the musculoskeletal system while neglecting physiological noise, and stochastic simulations account for noise while simplifying the dynamics. We took advantage of recent approaches where stochastic optimal control problems are approximated using deterministic optimal control problems, which can be solved efficiently using direct collocation. We were thus able to extend predictions of stochastic optimal control as a theory of motor coordination to include muscle coordination and movement patterns emerging from non-linear musculoskeletal dynamics. In stochastic optimal control simulations of human standing balance, we demonstrated that the inclusion of muscle dynamics can predict muscle co-contraction as minimal effort strategy that complements sensorimotor feedback control in the presence of sensory noise. In simulations of reaching, we demonstrated that nonlinear multi-segment musculoskeletal dynamics enables complex perturbed and unperturbed reach trajectories under a variety of task conditions to be predicted. In both behaviors, we demonstrated how interactions between task constraint, sensory noise, and the intrinsic properties of muscle influence optimal muscle coordination patterns, including muscle co-contraction, and the resulting movement trajectories. Our approach enables a true minimum effort solution to be identified as task constraints, such as movement accuracy, can be explicitly imposed, rather than being approximated using penalty terms in the cost function. Our approximate stochastic optimal control framework predicts complex features, not captured by previous simulation approaches, providing a generalizable and valuable tool to study how musculoskeletal dynamics and physiological noise may alter neural control of movement in both healthy and pathological movements.