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1.
Brain Res ; 1419: 19-33, 2011 Oct 24.
Article in English | MEDLINE | ID: mdl-21920508

ABSTRACT

This study investigated trajectory timing variability in right and left stroke survivors and healthy controls when reaching to a centrally located target under a fixed target condition or when the target could suddenly change position after reach onset. Trajectory timing variability was investigated with a novel method based on dynamic programming that identifies the steps required to time warp one trial's acceleration time series to match that of a reference trial. Greater trajectory timing variability of both hand and joint motions was found for the paretic arm of stroke survivors compared to their non-paretic arm or either arm of controls. Overall, the non-paretic left arm of the LCVA group and the left arm of controls had higher timing variability than the non-paretic right arm of the RCVA group and right arm of controls. The shoulder and elbow joint warping costs were consistent predictors of the hand's warping cost for both left and right arms only in the LCVA group, whereas the relationship between joint and hand warping costs was relatively weak in control subjects and less consistent across arms in the RCVA group. These results suggest that the left hemisphere may be more involved in trajectory timing, although the results may be confounded by skill differences between the arms in these right hand dominant participants. On the other hand, arm differences did not appear to be related to differences in targeting error. The paretic left arm of the RCVA exhibited greater trajectory timing variability than the paretic right arm of the LCVA group. This difference was highly correlated with the level of impairment of the arms. Generally, the effect of target uncertainty resulted in slightly greater trajectory timing variability for all participants. The results are discussed in light of previous studies of hemispheric differences in the control of reaching, in particular, left hemisphere specialization for temporal control of reaching movements.


Subject(s)
Arm/physiopathology , Cerebral Infarction/physiopathology , Movement Disorders/physiopathology , Paresis/physiopathology , Adult , Aged , Aged, 80 and over , Arm/innervation , Cerebral Infarction/pathology , Female , Humans , Male , Middle Aged , Movement Disorders/pathology , Paresis/pathology
2.
J Neurosci Methods ; 188(1): 89-96, 2010 Apr 30.
Article in English | MEDLINE | ID: mdl-20105441

ABSTRACT

This article investigates two methodological issues resulting from a recent study of center of mass positional stability during performance of whole-body targeting tasks (Freitas et al., 2006): (1) Can identical results be obtained with uncontrolled manifold (UCM) variance analysis when it is based on estimating the Jacobian using multiple linear regression (MLR) analysis compared to that using typical analytic formal geometric model? (2) Are kinematic synergies more related to stabilization of the instantaneous anterior-posterior position of the center of mass (COM(AP)) or the center of pressure (COP(AP))? UCM analysis was used to partition the variance of the joint configuration into 'bad' variance, leading to COM(AP) or COP(AP) variability, and 'good' variance, reflecting the use of motor abundance. Findings indicated (1) nearly identical UCM results for both methods of Jacobian estimation; and (2) more 'good' and less 'bad' joint variance related to stability of COP(AP) than to COM(AP) position. The first result requires further investigation with more degrees of freedom, but suggests that when a formal geometric model is unavailable or overly complex, UCM analysis may be possible by estimating the Jacobian using MLR. Correct interpretation of the second result requires analysis of the singular values of the Jacobian for different performance variables, which indicates how certain amount of joint variance affects each performance variable. Thus, caution is required when interpreting differences in joint variance structure among various performance variables obtained by UCM analysis without first investigating how the different relationships captured by the Jacobian translate those variances into performance-level variance.


Subject(s)
Joints/physiology , Movement/physiology , Posture/physiology , Adult , Analysis of Variance , Biomechanical Phenomena , Computer Simulation , Electromyography , Humans , Models, Biological , Psychomotor Performance , Range of Motion, Articular , Regression Analysis
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