Dissociating two sources of variability using a safety-margin model

Area of Science:

• Neuroscience
• Motor Control
• Rehabilitation Science

Background:

• Neurological trauma, such as stroke, significantly impacts daily activities by increasing motor variability.
• Sources of motor variability, neuromotor noise (uncontrollable) and action-tolerance variability (adaptable), are often conflated.
• Distinguishing these variability types is crucial for effective rehabilitation.

Purpose of the Study:

• To develop and validate an adaptive model that disambiguates neuromotor noise and action-tolerance variability.
• To generate distinct "signatures" for each type of variability within a task.
• To compare model predictions with experimental data from stroke survivors and healthy individuals.

Main Methods:

• Development of an adaptive model to simulate motor variability.
• Generation of distinct "signatures" for neuromotor noise and action-tolerance variability.
• Comparison of model outputs with experimental motor data from participants.

Main Results:

• The model successfully produced distinct signatures for neuromotor noise and action-tolerance variability.
• Individuals with higher neuromotor noise showed limited adaptability to the task, aligning with model predictions.
• The findings suggest that not all stroke survivors can adapt equally due to varying levels of neuromotor noise.

Conclusions:

• The developed model effectively differentiates between neuromotor noise and action-tolerance variability.
• This technique can inform personalized rehabilitation strategies for stroke survivors and individuals with neurological injuries.
• The model may aid in understanding the type of brain injury sustained by stroke survivors and guide tailored training interventions.