Reaching is IMPOSSIBLE UNTIL SPASTICITY IS CURED! Solve the correct problem. These were obviously high functioning survivors.
Compensatory Proximal Adjustments Characterize Effective Reaching Movements After Stroke
Silke Wolf, Dr. rer. biol. hum. https://orcid.org/0000-0001-8344-2872 si.wolf@uke.de, Winter, MS https://orcid.org/0000-0003-1457-610X, Elangovan, PhD https://orcid.org/0000-0002-1330-7058, Hanna Braaß, MD https://orcid.org/0000-0002-1400-8404, Jan Feldheim, Dipl.-Inf., José A. Graterol Pérez, MD https://orcid.org/0000-0002-8094-4380, Fanny Quandt, MD https://orcid.org/0000-0001-7042-6785, Robert Schulz, MD https://orcid.org/0000-0003-0913-899X, Jürgen Konczak, PhD https://orcid.org/0000-0002-4422-5610, and Christian Gerloff, MD https://orcid.org/0000-0002-6484-8882
Abstract
BACKGROUND:
Understanding of sensorimotor reorganization following a stroke is still incomplete. This study examined how the neuromotor system of well-recovered patients with stroke achieves stable control of the redundant degrees of freedom in the upper limb through goal-directed reaching movements.
METHODS:
Thirteen right-handed individuals with left-hemispheric stroke and 13 age-, sex- and handedness-matched healthy controls participated in this cross-sectional study. Each participant performed 80 unconstrained reach-to-grasp movements with either arm while kinematic data were recorded at 200 Hz using an optoelectronic motion capture system. Two types of outcome measures were examined, contrasting healthy individuals with those who have had strokes: end point and proximal kinematics. End point kinematics analyzed spatiotemporal hand movement characteristics, including movement time, time-to-peak velocity (TTPVHD), time-to-peak acceleration (TTPAHD), and velocity peaks for trajectory smoothness. Proximal kinematics focused on interjoint and intrajoint coordination of the elbow and shoulder, examining angular velocities and their timing differences. Stroke effects were analyzed using linear mixed-effects models.
RESULTS:
No significant differences were observed in distal end point kinematics between groups (n=13 each) for movement time (control versus stroke, 0.92 versus 0.96 s; P=0.944), TTPVHD (40% versus 42% of movement time, P=0.358), TTPAHD (22% versus 21%; P=0.583), or smoothness (1.02 versus 1.15; P=0.057). However, stroke significantly affected proximal kinematics, altering interjoint coordination with differences in timing between elbow flexion and shoulder rotation (18% versus 14%; P=0.019) and intrajoint coordination with differences in timing between shoulder flexion and abduction (18% versus 11%; P=0.008) and between flexion and rotation (1% versus 10%; P=0.001).
CONCLUSIONS:
Arm motor control in this cohort of well-recovered patients with stroke showed near-complete restoration of distal end point kinematics but significant differences in the timing of proximal intrajoint and interjoint coordination. These findings suggest compensatory adjustments in shoulder and elbow movements to achieve functional goals like reaching. An enhanced understanding of these strategies can inform targeted interventions to improve upper limb capability poststroke.
Graphical Abstract
The temporal sequence of stroke recovery is well known. Within hours after the stroke, a cascade of repair mechanisms triggers dendritic growth, axonal sprouting, and new synapse formation.5,6 Significant sensorimotor improvements typically occur within the first 3 months, resulting in a chronic state after approximately 6 months.7 However, there is substantial evidence that targeted motor function training during the chronic phase can further enhance recovery.8
To enhance UL sensorimotor recovery and to develop targeted therapies, it is vital to understand 2 key aspects: how the healthy nervous system maintains stable control over a musculoskeletal system with redundant degrees of freedom (DoF), and how these stable movement patterns differ from those observed after stroke. Motor cortical efferent signals modulate muscle tone and are essential for phasic innervation underlying voluntary behavior. In goal-directed tasks like reaching, neurocomputational models suggest that motor networks involving the sensorimotor cortices, the cerebellum, and the basal ganglia must process and retain information about the underlying dynamics (eg, joint torques).9 Thus, force feedback and implicit biomechanical knowledge are essential for intact control.
The question remains how movement is organized and controlled in a poststroke motor system. Neuronal loss affects cortical representations while disrupting essential movement-relevant feedback and the transmission of voluntary motor commands. A fundamental first step for answering this question is to understand the altered overt behavior by identifying stroke-typical movement patterns, as opposed to healthy movement patterns, of interjoint and intrajoint coordination that reflect the available central control of the musculoskeletal system.
To address this question, this study systematically evaluated the biomechanics of volitional reach-to-grasp movements in individuals who have experienced a stroke and compared them to those of individuals without a neurological deficit. In this research, system redundancy refers to the arm’s available joint DoF, where the major joints (wrist, ulnar-radial, elbow, and shoulder) collectively offer 7 DoFs to control the hand’s movement to a 6-DoF target in space. Specifically, we investigated the extent of hand end point control and how it is achieved through proximal patterns of (1) elbow-shoulder interjoint coordination and (2) intrajoint coordination of the 3-DoF shoulder joint. Although progress has been made in analyzing poststroke UL kinematics, the interdependencies between these proximal joint kinematics and distal end point kinematics remain unclear.10 Subsequently, the study examined how kinematic measures relate to standard clinical assessments.
Human reaching movements show kinematic invariants that develop within months after birth and remain consistent throughout a person’s workspace.11 One invariant maintains constant movement time by adjusting hand velocity for different distances, while another ensures the time-to-peak velocity (TTPV) of the hand at mid-reach (≈50%).12 Thus, our analysis focused on whether and to what extent these temporal kinematic features are restored after a stroke, delineated from features that exhibit any losses or adaptations. By linking end point kinematics with proximal kinematics, this study examined how the redundant joint DoFs of the arm are coordinated during reaching poststroke.
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