Predicting upper extremity motor improvement following therapy using EEG-based connectivity in chronic stroke

Area of Science:

• Neuroscience
• Rehabilitation Medicine
• Biomedical Engineering

Background:

• Prognosis uncertainty complicates tailored rehabilitation for stroke patients.
• Sensorimotor network connectivity shows potential for predicting upper extremity motor recovery.
• Network integrity is crucial for neuroplasticity and functional restoration post-stroke.

Purpose of the Study:

• To determine if pre-therapy sensorimotor network connectivity predicts motor improvement in the upper extremity after stroke.
• Investigate the prognostic value of brain connectivity for stroke rehabilitation outcomes.

Main Methods:

• Secondary analysis of a pilot randomized controlled trial involving 12 chronic stroke survivors.
• Electroencephalography (EEG) assessed sensorimotor connectivity before a 2-week task-practice therapy.
• Motor improvement measured by the Box and Block Test; regression analysis examined associations.

Main Results:

• Increased ipsilesional (same side) sensorimotor alpha connectivity correlated with greater upper extremity motor gains.
• This association was significant (p < 0.05) and observed in both therapy and control groups.
• Other connectivity measures and conventional predictors were not significant.

Conclusions:

• EEG-assessed sensorimotor connectivity may offer prognostic value for individual stroke patients.
• This could aid in optimizing rehabilitation planning for upper extremity function.
• Further research can validate connectivity metrics for personalized stroke therapy.