Neural Circuitries of Motor Learning as a Target to Modulate Sensorimotor Recovery After Stroke
Overview
- Phase
- N/A
- Intervention
- Not specified
- Conditions
- Stroke
- Sponsor
- Medical University of South Carolina
- Enrollment
- 40
- Locations
- 1
- Primary Endpoint
- Learning Rate as Indexed by Change in the Precision of Visuomotor Grip Force Adjustment (i.e., Reduction of Precision Error in Force Adjustment)
- Status
- Completed
- Last Updated
- 6 months ago
Overview
Brief Summary
After a stroke, plasticity occurs in the brain from microscopic to network level with positive but also negative consequences for functional recovery. Why post-stroke plasticity takes a beneficial or a maladaptive direction is still incompletely understood. Because the biological mechanisms underlying sensorimotor learning parallel those observed during recovery, learning mechanisms could be potential modifiers of post-stroke neuroplasticity and have a discrete mal-/adaptive impact on the recovery of sensorimotor function. This project seeks to further the understanding of the link between brain circuits that control the integration of new information during procedural learning in the injured brain and those circuits that are involved in adaptive plastic changes during recovery of sensorimotor function post-stroke. The project's methodological approach will allow the characterization of procedural learning-related neural network dynamics based on functional magnetic resonance imaging (MRI) in human volunteers with and without neurologically impairment post-stroke. Through multivariate integration of behavioral and biological descriptors of sensorimotor recovery, the project will investigate the association between motor learning-related network dynamics and descriptors of recovery.
Investigators
Eligibility Criteria
Inclusion Criteria
- Not provided
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Learning Rate as Indexed by Change in the Precision of Visuomotor Grip Force Adjustment (i.e., Reduction of Precision Error in Force Adjustment)
Time Frame: Pre Learning Session and Post Learning Session (approximately 90 minutes)
Isometric whole-hand grip force is captured continuously with grip-force transducers (at 1000Hz) and adjusted relative to the individual maximum voluntary contraction. Precision of force adjustment is based on the recorded muscle force monitored during task performance and defined as the actual force exerted by the participant relative to the target force (measurement unit: precision in %), with positive values indicating over- and negative values indicating undershoot. Learning rate from before to after learning will be defined as the difference in precision between before as compared to after one single learning session. Precision error was calculated as the percentage deviation of the actual grip force from the target force, averaged across trials before and after the learning task. A reduction in force error indexes a behavioral advantage of force adjustment over time and is thus interpreted as learning.
Change in Blood-oxygen-level-dependent (BOLD) Signal Derived Multi-voxel Brain Activation
Time Frame: Pre Learning Session and Post Learning Session (approximately 90 minutes)
Learning-related BOLD-signal-derived brain activation relative to motor performance.