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The Journal of Neurophysiology Vol. 88 No. 2 August 2002, pp. 991-1004
Copyright ©2002 by the American Physiological Society
1Kawato Dynamic Brain Project, Japan Science and Technology Corporation and 2ATR Human Information Science Laboratories, Department 3, 2-2-2 Hikaridai, Soraku-gun, Kyoto 619-0288, Japan; 3School of Kinesiology, Simon Fraser University, Burnaby, British V5A 1S6, Canada; 4Intelligent Communication Laboratory, Nippon Telegraph and Telephone Corporation Communication Science Laboratories, 2-4 Hikaridai, Soraku-gun, Kyoto 619-0237, Japan; 5Human and Information Science Laboratory, Nippon Telegraph and Telephone Corporation Communication Science Laboratories and 6Core Research for the Evolutional Science and Technology Program, Japan Science and Technology Corporation 3-1 Wakamiya, Morinosato, Atsugi-city, Kanagawa-prefecture, 243-0198, Japan; and 7Rehabilitation Center, Hyogo College of Medicine, Nishinomiya-city, Hyogo 663-8501, Japan
Osu, Rieko,
David W. Franklin,
Hiroko Kato,
Hiroaki Gomi,
Kazuhisa Domen,
Toshinori Yoshioka, and
Mitsuo Kawato.
Short- and Long-Term Changes in Joint Co-Contraction Associated
With Motor Learning as Revealed From Surface EMG. J. Neurophysiol. 88: 991-1004, 2002. In the field of motor control, two hypotheses have been
controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the
viscoelasticity of musculoskeletal system. Recent observations on
relatively low stiffness during trained movements support the existence
of internal models. However, no study has revealed the decrease in
viscoelasticity associated with learning that would imply improvement
of internal models as well as synergy between the two hypothetical
mechanisms. Previously observed decreases in electromyogram
(EMG) might have other explanations, such as trajectory
modifications that reduce joint torques. To circumvent such
complications, we required strict trajectory control and examined only
successful trials having identical trajectory and torque profiles.
Subjects were asked to perform a hand movement in unison with a target
moving along a specified and unusual trajectory, with shoulder and
elbow in the horizontal plane at the shoulder level. To evaluate joint
viscoelasticity during the learning of this movement, we proposed an
index of muscle co-contraction around the joint (IMCJ). The IMCJ was
defined as the summation of the absolute values of antagonistic muscle
torques around the joint and computed from the linear relation between
surface EMG and joint torque. The IMCJ during isometric contraction, as
well as during movements, was confirmed to correlate well with joint
stiffness estimated using the conventional method, i.e., applying
mechanical perturbations. Accordingly, the IMCJ during the learning of
the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for
each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were
decomposed into long-term components that showed decreases in IMCJ in
accordance with learning with little change in the trajectory and
short-term interactions between the IMCJ and performance error. A
cross-correlation analysis and impulse responses both suggested that
higher IMCJs follow poor performances, and lower IMCJs follow good
performances within a few successive trials. Our results support the
hypothesis that viscoelasticity contributes more when internal models
are inaccurate, while internal models contribute more after the
completion of learning. It is demonstrated that the CNS regulates
viscoelasticity on a short- and long-term basis depending on
performance error and finally acquires smooth and accurate movements
while maintaining stability during the entire learning process.
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