In Vivo Dynamics of the Musculoskeletal System Cannot Be Adequately Described Using a Stiffness-Damping-Inertia Model

Dinant Kistemaker has a new paper out in PLoS ONE:

Kistemaker DA, Rozendaal LA, 2011
In Vivo Dynamics of the Musculoskeletal System Cannot Be Adequately Described Using a Stiffness-Damping-Inertia Model.
PLoS ONE 6(5): e19568.
doi:10.1371/journal.pone.0019568

Background

Visco-elastic properties of the (neuro-)musculoskeletal system play a fundamental role in the control of posture and movement. Often, these properties are described and identified using stiffness-damping-inertia (KBI) models. In such an approach, perturbations are applied to the (neuro-)musculoskeletal system and subsequently KBI-model parameters are optimized to obtain a best fit between simulated and experimentally observed responses. Problems with this approach may arise because a KBI-model neglects critical aspects of the real musculoskeletal system.

Methodology/Principal Findings

The purpose of this study was to analyze the relation between the musculoskeletal properties and the stiffness and damping estimated using a KBI-model, to analyze how this relation is affected by the nature of the perturbation and to assess the sensitivity of the estimated stiffness and damping to measurement errors. Our analyses show that the estimated stiffness and damping using KBI-models do not resemble any of the dynamical parameters of the underlying system, not even when the responses are very accurately fitted by the KBI-model. Furthermore, the stiffness and damping depend non-linearly on all the dynamical parameters of the underlying system, influenced by the nature of the perturbation and the time interval over which the KBI-model is optimized. Moreover, our analyses predict a very high sensitivity of estimated parameters to measurement errors.

Conclusions/Significance

The results of this study suggest that the usage of stiffness-damping-inertia models to investigate the dynamical properties of the musculoskeletal system under control by the CNS should be reconsidered.

 

 

 

 

 

 

Jeremy Wong has a new paper out based on some of his PhD work:

Spatially selective enhancement of proprioceptive acuity following motor learning
Jeremy D. Wong, Elizabeth T. Wilson, and Paul L. Gribble
J Neurophysiol 2011;105 2512-2521
http://jn.physiology.org/cgi/content/abstract/105/5/2512

Abstract
It is well recognized that the brain uses sensory information to accurately produce motor commands. Indeed, most research into the relationship between sensory and motor systems has focused on how sensory information modulates motor function. In contrast, recent studies have begun to investigate the reverse: how sensory and perceptual systems are tuned based on motor function, and specifically motor learning. In the present study we investigated changes to human proprioceptive acuity following recent motor learning. Sensitivity to small displacements of the hand was measured before and after 10 min of motor learning, during which subjects grasped the handle of a robotic arm and guided a cursor to a series of visual targets randomly located within a small workspace region. We used a novel method of assessing proprioceptive acuity that avoids active movement, interhemispheric transfer, and intermodality coordinate transformations. We found that proprioceptive acuity improved following motor learning, but only in the region of the arm’s workspace explored during learning. No proprioceptive improvement was observed when motor learning was performed in a different location or when subjects passively experienced limb trajectories matched to those of subjects who actively performed motor learning. Our findings support the idea that sensory changes occur in parallel with changes to motor commands during motor learning.

Recent Commentaries

Two commentaries on our recent article* in Journal of Neuroscience have appeared in the past 2 months, one in the Journal Club section of Journal of Neuroscience, and a second in the Neuro Forum section of Journal of Neurophysiology:

Fabrice R. Sarlegna and Pierre-Michel Bernier
On the Link between Sensorimotor Adaptation and Sensory Recalibration
J. Neurosci. 2010 30: 11555-11557; doi:10.1523/JNEUROSCI.3040-10.2010 [Full Text] [PDF]

Daniel J. Goble and Joaquin A. Anguera
Plastic Changes in Hand Proprioception Following Force-Field Motor Learning
J Neurophysiol 104: 1213-1215, 2010. First published July 7, 2010; doi:10.1152/jn.00543.2010 [Abstract] [Full Text] [PDF]

*Ostry D, Darainy M, Mattar A, Wong J, Gribble PL (2010) Somatosensory Plasticity and Motor Learning. J. Neurosci. 30, 5384-93

[pdf]

The Central Nervous System Does Not Minimize Energy Cost in Arm Movements

We have a new publication coming out in Journal of Neurophysiology:

Kistemaker DA, Wong JD, Gribble PL (2010) The Central Nervous System does not minimize energy cost in arm movements. J. Neurophysiol., in press
[pdf-preprint]

Abstract

It has been widely suggested that the many degrees of freedom of the musculoskeletal system may be exploited by the CNS to minimize energy cost. We tested this idea by having subjects making point-to-point movements while grasping a robotic manipulandum. The robot created a force field chosen such that the minimal energy hand path for reaching movements differed substantially from those observed in a null field. The results show that after extended exposure to the force field, subjects continued to move exactly as they did in the null field and hence used substantially more energy than needed. Even after practicing to move along the minimal energy path, subjects did not adapt their freely chosen hand paths to reduce energy expenditure. The results of this study indicate that for point-to-point arm movements minimization of energy cost is not a dominant factor that influences how the CNS arrives at kinematics and associated muscle activation patterns.

Keywords: motor control, motor learning, force field, metabolic energy, muscle activation patterns.

fMRI Activation during Observation of Others’ Reach Errors

Finally our Journal of Cognitive Neuroscience paper has finally appeared in the journal:

fMRI Activation during Observation of Others’ Reach Errors
	Nicole Malfait, Kenneth F. Valyear, Jody C. Culham, Jean-Luc Anton, Liana E. Brown, and Paul L. Gribble
	Journal of Cognitive Neuroscience July 2010, Vol. 22, No. 7: 1493-1503. Abstract | Full Text | PDF (336 KB) | PDF Plus (344 KB)

[local pdf]

Our brain imaging study asks the question, what brain regions are active when we passively observe movement errors made others? The answer is, many of the same sensory/motor brain regions that are activated when we actively move and generate movement errors ourselves. This provides a neural basis for phenomena that we have studied in the past such as motor learning by observing, and suggests that essentially our motor learning brain regions are not only active when we actively engage in movement but also when we observe movements (and movement errors) made by others. Our brains soak up all that we observe and incorporate that information into our own motor repertoire.

Here is the full Abstract:

When exposed to novel dynamical conditions (e.g., externally imposed forces), neurologically intact subjects easily adjust motor commands on the basis of their own reaching errors. Subjects can also benefit from visual observation of others’ kinematic errors. Here, using fMRI, we scanned subjects watching movies depicting another person learning to reach in a novel dynamic environment created by a robotic device. Passive observation of reaching movements (whether or not they were perturbed by the robot) was associated with increased activation in fronto-parietal regions that are normally recruited in active reaching. We found significant clusters in parieto-occipital cortex, intraparietal sulcus, as well as in dorsal premotor cortex. Moreover, it appeared that part of the network that has been shown to be engaged in processing self-generated reach error is also involved in observing reach errors committed by others. Specifically, activity in left intraparietal sulcus and left dorsal premotor cortex, as well as in right cerebellar cortex, was modulated by the amplitude of observed kinematic errors.