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Davidowitz, I., Parmet, Y., Frenkel-Toledo, S., Banina, M. C., Soroker, N., Solomon, J. M., et al. (2019). Relationship Between Spasticity and Upper-Limb Movement Disorders in Individuals With Subacute Stroke Using Stochastic Spatiotemporal Modeling. Neurorehabil Neural Repair, 33(2), 141–152.
Abstract: BACKGROUND: Spasticity is common in patients with stroke, yet current quantification methods are insufficient for determining the relationship between spasticity and voluntary movement deficits. This is partly a result of the effects of spasticity on spatiotemporal characteristics of movement and the variability of voluntary movement. These can be captured by Gaussian mixture models (GMMs). OBJECTIVES: To determine the influence of spasticity on upper-limb voluntary motion, as assessed by the bidirectional Kullback-Liebler divergence (BKLD) between motion GMMs. METHODS: A total of 16 individuals with subacute stroke and 13 healthy aged-equivalent controls reached to grasp 4 targets (near-center, contralateral, far-center, and ipsilateral). Two-dimensional GMMs (angle and time) were estimated for elbow extension motion. BKLD was computed for each individual and target, within the control group and between the control and stroke groups. Movement time, final elbow angle, average elbow velocity, and velocity smoothness were computed. RESULTS: Between-group BKLDs were much larger than within control-group BKLDs. Between-group BKLDs for the near-center target were lower than those for the far-center and contralateral targets, but similar to that for the ipsilateral target. For those with stroke, the final angle was lower for the near-center target, and the average velocity was higher. Velocity smoothness was lower for the near-center than for the ipsilateral target. Elbow flexor and extensor passive muscle resistance (Modified Ashworth Scale) strongly explained BKLD values. CONCLUSIONS: Results support the view that individuals with poststroke spasticity have a velocity-dependent reduction in active elbow joint range and that BKLD can be used as an objective measure of the effects of spasticity on reaching kinematics.
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Levin, M. F., Banina, M. C., Frenkel-Toledo, S., Berman, S., Soroker, N., Solomon, J. M., et al. (2018). Personalized upper limb training combined with anodal-tDCS for sensorimotor recovery in spastic hemiparesis: study protocol for a randomized controlled trial. Trials, 19(1), 7.
Abstract: BACKGROUND: Recovery of voluntary movement is a main rehabilitation goal. Efforts to identify effective upper limb (UL) interventions after stroke have been unsatisfactory. This study includes personalized impairment-based UL reaching training in virtual reality (VR) combined with non-invasive brain stimulation to enhance motor learning. The approach is guided by limiting reaching training to the angular zone in which active control is preserved (“active control zone”) after identification of a “spasticity zone”. Anodal transcranial direct current stimulation (a-tDCS) is used to facilitate activation of the affected hemisphere and enhance inter-hemispheric balance. The purpose of the study is to investigate the effectiveness of personalized reaching training, with and without a-tDCS, to increase the range of active elbow control and improve UL function. METHODS: This single-blind randomized controlled trial will take place at four academic rehabilitation centers in Canada, India and Israel. The intervention involves 10 days of personalized VR reaching training with both groups receiving the same intensity of treatment. Participants with sub-acute stroke aged 25 to 80 years with elbow spasticity will be randomized to one of three groups: personalized training (reaching within individually determined active control zones) with a-tDCS (group 1) or sham-tDCS (group 2), or non-personalized training (reaching regardless of active control zones) with a-tDCS (group 3). A baseline assessment will be performed at randomization and two follow-up assessments will occur at the end of the intervention and at 1 month post intervention. Main outcomes are elbow-flexor spatial threshold and ratio of spasticity zone to full elbow-extension range. Secondary outcomes include the Modified Ashworth Scale, Fugl-Meyer Assessment, Streamlined Wolf Motor Function Test and UL kinematics during a standardized reach-to-grasp task. DISCUSSION: This study will provide evidence on the effectiveness of personalized treatment on spasticity and UL motor ability and feasibility of using low-cost interventions in low-to-middle-income countries. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT02725853 . Initially registered on 12 January 2016.
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Liebermann, D. G., Levin, M. F., McIntyre, J., Weiss, P. L., & Berman, S. (2010). Arm path fragmentation and spatiotemporal features of hand reaching in healthy subjects and stroke patients. Conf Proc IEEE Eng Med Biol Soc, 2010, 5242–5245.
Abstract: Arm motion in healthy humans is characterized by smooth and relatively short paths. The current study focused on 3D reaching in stroke patients. Sixteen right-hemiparetic stroke patients and 8 healthy adults performed 42 reaching movements towards 3 visual targets located at an extended arm distance. Performance was assessed in terms of spatial and temporal features of the movement; i.e., hand path, arm posture and smoothness. Differences between groups and within subjects were hypothesized for spatial and temporal aspects of reaching under the assumption that both are independent. As expected, upper limb motion of patients was characterized by longer and jerkier hand paths and slower speeds. Assessment of the number of sub-movements within each movement did not clearly discriminate between groups. Principal component analyses revealed specific clusters of either spatial or temporal measures, which accounted for a large proportion of the variance in patients but not in healthy controls. These findings support the notion of a separation between spatial and temporal features of movement. Stroke patients may fail to integrate the two aspects when executing reaching movements towards visual targets.
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