Neural function, injury, and stroke subtype predict treatment gains after stroke

Area of Science:

• Neuroscience
• Rehabilitation Medicine
• Medical Imaging

Background:

• Stroke recovery shows high variability in response to restorative therapies.
• Preclinical data suggest neural injury, function, and clinical status impact treatment gains.
• A multivariate approach may improve prediction of rehabilitation outcomes.

Purpose of the Study:

• To identify predictors of treatment gains in post-stroke patients receiving robotic therapy.
• To investigate the combined predictive value of neural injury, neural function, and clinical status.
• To explore stroke subtype-specific predictors of rehabilitation response.

Main Methods:

• 29 patients 3-6 months post-stroke received 3 weeks of upper extremity robotic therapy.
• Assessments included brain injury (gray/white matter), neural function (cortical activity/connectivity), and clinical status.
• Multivariate modeling and Lasso regression analyzed predictor variables.

Main Results:

• Smaller corticospinal tract (CST) injury, greater ipsilesional motor cortex (M1) activation, and stronger interhemispheric M1-M1 connectivity predicted gains.
• A model combining CST injury and M1-M1 connectivity showed significant predictive value (r(2) = 0.44, p = 0.002).
• A CST injury threshold (>63%) indicated no significant gains; lacunar stroke patients' gains were predicted by intrahemispheric connectivity.

Conclusions:

• Post-stroke restorative therapy response is best predicted by neural injury and function measures.
• Neuroimaging (CST injury, M1 connectivity) offers superior prediction over behavioral assessments.
• Lesion-specific strategies may enhance rehabilitation, considering differing predictors across stroke subtypes.