Biomedical subjects

Ferdinando A Mussa-Ivaldi

Publications and source records attributed to Ferdinando A Mussa-Ivaldi.

15 recordsLinked to original sources

PubMed2005

Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors.

This investigation is one in a series of studies that address the possibility of stroke rehabilitation using robotic devices to facilitate "adaptive training." Healthy subjects, after training in the presence of systematically applied forces, typically exhibit a predictable "after-effect." A critical question is whether this adaptive characteristic is preserved following stroke so that it might be exploited for restoring function. Another important question is whether subjects benefit more from training forces that enhance their errors than from forces that reduce their errors. We exposed hemiparetic stroke survivors and healthy age-matched controls to a pattern of disturbing forces that have been found by previous studies to induce a dramatic adaptation in healthy individuals. Eighteen stroke survivors made 834 movements in the presence of a robot-generated force field that pushed their hands proportional to its speed and perpendicular to its direction of motion--either clockwise or counterclockwise. We found that subjects could adapt, as evidenced by significant after-effects. After-effects were not correlated with the clinical scores that we used for measuring motor impairment. Further examination revealed that significant improvements occurred only when the training forces magnified the original errors, and not when the training forces reduced the errors or were zero. Within this constrained experimental task we found that error-enhancing therapy (as opposed to guiding the limb closer to the correct path) to be more effective than therapy that assisted the subject.

Acceleration↗

PubMed2005

Haptic identification of surfaces as fields of force.

The ability to discriminate an object's shape and mechanical properties from touch is one of the most fundamental somatosensory functions. When exploring physical properties of an object, such as stiffness and curvature, humans probe the object's surface and obtain information from the many sensory receptors in their upper limbs. This sensory information is critical for the guidance of actions. We studied how humans acquire an internal representation of the shape and mechanical properties of surfaces and how this information affects the execution of trajectories over the surface. Experiments involved subjects executing trajectories while holding a planar manipulandum that renders planar virtual objects with variable shape and mechanical properties. Subjects were instructed to make reaching movements with the hand between points on the boundary of a curved virtual disk of varying stiffness and curvature. The results suggest two classifications of adaptive responses: force perturbations and object boundaries. In the first case, a rectilinear hand movement is enforced by opposing the interaction forces. In the second case, the trajectory conforms to the object boundary so as to reduce interaction forces. While this dichotomy is evident for very rigid and very soft objects, the likelihood of an object boundary classification depended, in a smooth and monotonic way, on the average force experienced during the initial movements. Furthermore, the observed response across a variety of stiffness values lead to a constant average interaction force after adaptation. This suggests that the nervous system may select from the two responses through a mechanism that attempts to establish a constant interaction force.

Adaptation, Physiological↗

PubMed2005

Remapping hand movements in a novel geometrical environment.

The issue of how the Euclidean properties of space are represented in the nervous system is a main focus in the study of visual perception, but is equally relevant to motor learning. The goal of our experiments was to investigate how the properties of space guide the remapping of motor coordination. Subjects wore an instrumented data glove that recorded the finger motions. Signals generated by the glove operated a remotely controlled endpoint: a cursor on a computer monitor. The subjects were instructed to execute movements of this endpoint with controlled motions of the fingers. This required inverting a highly redundant map from fingers to cursor motions. We found that 1) after training with visual feedback of the final error (but not of the ongoing cursor motion), subjects learned to map cursor locations into configurations of the fingers; 2) extended practice of movement led to more rectilinear cursor movement, a trend facilitated by training under continuous visual feedback of cursor motions; 3) with practice, subjects reduced motion in the degrees of freedom that did not contribute to the movements of the cursor; 4) with practice, subjects reduced variability of both cursor and hand movements; and 5) the reduction of errors and the increase in linearity generalized beyond the set of movements used for training. These findings suggest that subjects not only learned to produce novel coordinated movement to control the placement of the cursor, but they also developed a representation of the Euclidean space on which hand movements were remapped.

Analysis of Variance↗

PubMed2005

Computational analysis in vitro: dynamics and plasticity of a neuro-robotic system.

When the brain interacts with the environment it constantly adapts by representing the environment in a form that is called an internal model. The neurobiological basis for internal models is provided by the connectivity and the dynamical properties of neurons. Thus, the interactions between neural tissues and external devices provide a fundamental means for investigating the connectivity and dynamical properties of neural populations. We developed this idea, suggested in the 1980s by Valentino Braitenberg, for investigating and representing the dynamical behavior of neuronal populations in the brainstem of the lamprey. The brainstem was maintained in vitro and connected in a closed loop with two types of artificial device: (a) a simulated dynamical system and (b) a small mobile robot. In both cases, the device was controlled by recorded extracellular signals and its output was translated into electrical stimuli delivered to the neural system. The goal of the first study was to estimate the dynamical dimension of neural preparation in a single-input/single-output configuration. The dynamical dimension is the number of state variables that together with the applied input determine the output of a system. The results indicate that while this neural system has significant dynamical properties, its effective complexity, as established by the dynamical dimension, is rather moderate. In the second study, we considered a more specific situation, in which the same portion of the nervous system controls a robotic device in a two-input/two-output configuration. We fitted the input-output data from the neuro-robotic preparation to neural network models having different internal dynamics and we observed the generalization error of each model. Consistent with the first study, this second experiment showed that a simple recurrent dynamical model was able to capture the behavior of the hybrid system. This experimental and computational framework provides the means for investigating neural plasticity and internal representations in the context of brain-machine interfaces.

Animals↗

PubMed2005

Interaction of visual and proprioceptive feedback during adaptation of human reaching movements.

People tend to make straight and smooth hand movements when reaching for an object. These trajectory features are resistant to perturbation, and both proprioceptive as well as visual feedback may guide the adaptive updating of motor commands enforcing this regularity. How is information from the two senses combined to generate a coherent internal representation of how the arm moves? Here we show that eliminating visual feedback of hand-path deviations from the straight-line reach (constraining visual feedback of motion within a virtual, "visual channel") prevents compensation of initial direction errors induced by perturbations. Because adaptive reduction in direction errors occurred with proprioception alone, proprioceptive and visual information are not combined in this reaching task using a fixed, linear weighting scheme as reported for static tasks not requiring arm motion. A computer model can explain these findings, assuming that proprioceptive estimates of initial limb posture are used to select motor commands for a desired reach and visual feedback of hand-path errors brings proprioceptive estimates into registration with a visuocentric representation of limb position relative to its target. Simulations demonstrate that initial configuration estimation errors lead to movement direction errors as observed experimentally. Registration improves movement accuracy when veridical visual feedback is provided but is not invoked when hand-path errors are eliminated. However, the visual channel did not exclude adjustment of terminal movement features maximizing hand-path smoothness. Thus visual and proprioceptive feedback may be combined in fundamentally different ways during trajectory control and final position regulation of reaching movements.

Adaptation, Physiological↗

PubMed2004

Robot-assisted adaptive training: custom force fields for teaching movement patterns.

Based on recent studies of neuro-adaptive control, we tested a new iterative algorithm to generate custom training forces to "trick" subjects into altering their target-directed reaching movements to a prechosen movement as an after-effect of adaptation. The prechosen movement goal, a sinusoidal-shaped path from start to end point, was never explicitly conveyed to the subject. We hypothesized that the adaptation would cause an alteration in the feedforward command that would result in the prechosen movement. Our results showed that when forces were suddenly removed after a training period of 330 movements, trajectories were significantly shifted toward the prechosen movement. However, de-adaptation occurred (i.e., the after-effect "washed out") in the 50-75 movements that followed the removal of the training forces. A second experiment suppressed vision of hand location and found a detectable reduction in the washout of after-effects, suggesting that visual feedback of error critically influences learning. A final experiment demonstrated that after-effects were also present in the neighborhood of training--44% of original directional shift was seen in adjacent, unpracticed movement directions to targets that were 60 degrees different from the targets used for training. These results demonstrate the potential for these methods for teaching motor skills and for neuro-rehabilitation of brain-injured patients. This is a form of "implicit learning," because unlike explicit training methods, subjects learn movements with minimal instructions, no knowledge of, and little attention to the trajectory.

Adaptation, Physiological↗

PubMed2003

Experimentally confirmed mathematical model for human control of a non-rigid object.

Determining the principles used to plan and execute movements is a fundamental question in neuroscience research. When humans reach to a target with their hand, they exhibit stereotypical movements that closely follow an optimally smooth trajectory. Even when faced with various perceptual or mechanical perturbations, subjects readily adapt their motor output to preserve this stereotypical trajectory. When humans manipulate non-rigid objects, however, they must control the movements of the object as well as the hand. Such tasks impose a fundamentally different control problem than that of moving one's arm alone. Here, we developed a mathematical model for transporting a mass-on-a-spring to a target in an optimally smooth way. We demonstrate that the well-known "minimum-jerk" model for smooth reaching movements cannot accomplish this task. Our model extends the concept of smoothness to allow for the control of non-rigid objects. Although our model makes some predictions that are similar to minimum jerk, it predicts distinctly different optimal trajectories in several specific cases. In particular, when the relative speed of the movement becomes fast enough or when the object stiffness becomes small enough, the model predicts that subjects will transition from a uni-phasic hand motion to a bi-phasic hand motion. We directly tested these predictions in human subjects. Our subjects adopted trajectories that were well-predicted by our model, including all of the predicted transitions between uni- and bi-phasic hand motions. These findings suggest that smoothness of motion is a general principle of movement planning that extends beyond the control of hand trajectories.

Algorithms↗

PubMed2003

Generalization of object manipulation skills learned without limb motion.

Recent work suggests that human subjects may learn mappings between object motion and exerted torque during manipulation of freely pivoting or unstable objects. In the present work, we studied an object manipulation task involving no arm movement to determine how subjects internally represent the force-motion relationship of an object during a skilled manipulation task. Human subjects learned to balance a simulated inverted pendulum. The simulation was controlled by pressing on a fixed force sensor, and applied forces resulted in motion of the simulated pendulum on a computer screen according to its equation of motion. Each subject initially learned the task in one arm posture and was tested 1 d later in a new arm posture. In one test condition, the effects of arm torque were matched to the original task, and in the other test condition, the simulation was unchanged. The pattern of skill transfer to different arm postures suggested that subjects had learned joint torque responses rather than a general model of the object interface forces. A second experiment showed that the advantage of training with matched arm torques was object specific, because torque-matched training on a tracking task involving similar forces was not a substitute for training in the balancing task.

Adult↗

PubMed2003

Evidence for a specific internal representation of motion-force relationships during object manipulation.

Human subjects learned a tracking task which required them to point at a moving target with the free end of an inverted pendulum object. In order to determine how subjects represented this object internally, we studied learning interference between variants of this task in which the pendulum object had either stable or unstable dynamics. Using a novel method, agreement between possible internal representations of the two tasks was estimated by analysis of the motion-to-torque relationships experienced by each subject as they manipulated each object. It was possible to predict retention of the primary task on day 2 from our measure of agreement between primary and interfering tasks on day 1. This result suggests that the subjects learned the correct torque patterns to use to produce specific desired patterns of motion as they learned the balancing task. Surprisingly, the analyses indicate that retention was not impaired when similar motions of the two objects required retrieval of incompatible torque responses, but retention was impaired when similar patterns of motion in the two tasks required similar patterns of applied torque. These findings can be accounted for by a simple model of how multiple similar torque responses are selected and retrieved from memory when responses are freely chosen.

Adult↗

PubMed2003

Sequence, time, or state representation: how does the motor control system adapt to variable environments?

Does the observation of well-timed movements imply the existence of some internal representation of time, such as a hypothetical neural clock? Here we report the results of experiments designed to investigate whether subjects form a correct adaptive representation of mechanical environments that change in a very predictable manner. In these experiments, subjects were asked to execute arm movements over a two-dimensional workspace while experiencing time-dependent disturbing forces. We provide a formal definition for time representation and conclude that our subjects didn't use time representation for motor adaptation under the tested conditions. Subjects performed arm-reaching movements in the following experiments: (1) six experiments in a sinusoidal time-varying force field; (2) six experiments in a simple sequence of alternating viscous force fields, in which the number of targets allowed for the approximation of the force by a complex state-dependent force field; and (3) six experiments in the same simple sequence of alternating viscous force fields, in which no state-dependent force field approximation was possible. We found that the subjects did not adapt to the time-varying force field and were unable to form an adequate representation of the simple sequence of force fields. In the latter case, whenever possible, they adapted to a single state-dependent field that produced forces similar to the two alternating fields. This state-dependent field produced the same forces as the applied sequence of fields only over the trajectories that subjects executed during the training phase. However, the state-dependent field was inadequate to produce the correct forces generated by the field sequence over a new set of trajectories.These results are not consistent with the hypothesis that subjects would develop a correct representation of time-dependent forces, at least under the tested circumstances. We speculate that the system responsible for adaptation of movements to external forces may be unable to employ temporal representation. While it is possible that such a representation may emerge in a more prolonged and/or intense training, our findings indicate a preference by the adaptive system to generalize based on representing dependence of external forces upon state rather than upon time.

Adaptation, Physiological↗

PubMed2003

Brain-machine interfaces: computational demands and clinical needs meet basic neuroscience.

As long as 150 years ago, when Fritz and Hitzig demonstrated the electrical excitability of the motor cortex, scientists and fiction writers were considering the possibility of interfacing a machine with the human brain. Modern attempts have been driven by concrete technological and clinical goals. The most advanced of these has brought the perception of sound to thousands of deaf individuals by means of electrodes implanted in the cochlea. Similar attempts are underway to provide images to the visual cortex and to allow the brains of paralyzed patients to re-establish control of the external environment via recording electrodes. This review focuses on two challenges: (1) establishing a 'closed loop' between sensory input and motor output and (2) controlling neural plasticity to achieve the desired behavior of the brain-machine system. Meeting these challenges is the key to extending the impact of the brain-machine interface.

Animals↗

PubMed2003

Dynamical dimension of a hybrid neurorobotic system.

The goal of this work is to understand how neural tissue can be programed to execute predetermined functions. We developed a research tool that includes the brainstem of a lamprey and a two-wheeled robot interconnected in a closed loop. We report here the development of a framework for studying the dynamics of the neural tissue based on the interaction of this tissue with the robot.

Animals↗

PubMed2002

Linear combinations of nonlinear models for predicting human-machine interface forces.

This study presents a computational framework that capitalizes on known human neuromechanical characteristics during limb movements in order to predict human-machine interactions. A parallel-distributed approach, the mixture of nonlinear models, fits the relationship between the measured kinematics and kinetics at the handle of a robot. Each element of the mixture represented the arm and its controller as a feedforward nonlinear model of inverse dynamics plus a linear approximation of musculotendonous impedance. We evaluated this approach with data from experiments where subjects held the handle of a planar manipulandum robot and attempted to make point-to-point reaching movements. We compared the performance to the more conventional approach of a constrained, nonlinear optimization of the parameters. The mixture of nonlinear models accounted for 79 +/- 11% (mean +/- SD) of the variance in measured force, and force errors were 0.73 +/- 0.20% of the maximum exerted force. Solutions were acquired in half the time with a significantly better fit. However, both approaches suffered equally from the simplifying assumptions, namely that the human neuromechanical system consisted of a feedforward controller coupled with linear impedances and a moving state equilibrium. Hence, predictability was best limited to the first half of the movement. The mixture of nonlinear models may be useful in human-machine tasks such as in telerobotics, fly-by-wire vehicles, robotic training, and rehabilitation.

Artificial Intelligence↗

PubMed2002

Manipulating objects with internal degrees of freedom: evidence for model-based control.

There is substantial evidence that humans possess an accurate and adaptable internal model of the dynamics of their arm that is utilized by the nervous system for controlling arm movements. However, it is not known if such model-based strategies are used for controlling dynamical systems outside the body. The need to predict events in the external world is not restricted to the execution of reaching movements or to the handling of rigid tools. Model-based control may also be critical for performing functional tasks with non-rigid objects such as stabilizing a cup of coffee. The present study investigated the strategies used by humans to control simple mass-spring objects. Subjects made straight line reaching movements to a target while interacting with a robotic manipulandum that simulated the dynamics of a one-dimensional mass on a spring. After learning, neither hand nor object kinematics returned to those of free reaching, suggesting that this task was not learned as a perturbation of free reaching. Although there are control strategies (such as slowing the movement of the hand) that would require little or no knowledge of object dynamics, subjects did not adopt these strategies. Instead, they tailored their motor commands to the particular object being manipulated. When object parameters were unexpectedly altered in a way that required no changes in kinematics to successfully complete the task, subjects nonetheless exhibited substantial kinematic deviations. These deviations were consistent with those predicted by a model of the arm-plus-object system driven by a low-impedance controller that incorporated an explicit inverse model of arm-plus-object dynamics. The observed behavior could not be reproduced by a controller that relied on modulating hand impedance alone with no inverse model. These results were therefore consistent with the hypothesis that subjects learn to control the kinematics of manipulated objects by forming an internal model that specified the forces to be exerted by the hand on the object to induce the desired motion of that object.

Adaptation, Physiological↗

PubMedDate not supplied

Custom-designed haptic training for restoring reaching ability to individuals with poststroke hemiparesis.

We present an initial test of a technique for retraining reaching skills in patients with poststroke hemiparesis, in which errors are temporarily magnified to encourage learning and compensation. Individuals with poststroke hemiparesis held a horizontal plane robotic manipulandum that could exert a variety of forces while recording patients' movements. We measured how well the patients recovered movement straightness in a single visit to the laboratory (approximately 3 h). Following training, we returned forces to zero for an additional 50 movements to discern if aftereffects lasted. We found that all subjects showed immediate benefit from the training, although 3 of the 10 subjects did not retain these benefits for the remainder of the experiment. We discuss how these approaches demonstrate great potential for rehabilitation tools that augment error to facilitate functional recovery.

Adult↗