Motor Network Efficiency in Stroke Patients: Comparing Activation Likelihood Estimation-Based and Whole Brain...

Area of Science:

• Neuroscience
• Neurology
• Medical Imaging

Background:

• Stroke significantly disrupts brain networks, leading to motor function impairments.
• Whole-brain atlases offer network insights but may lack specificity for predicting motor outcomes.
• Accurate prediction of motor recovery is crucial for effective stroke rehabilitation.

Purpose of the Study:

• To compare the predictive performance of a function-specific atlas (ALE) versus a general atlas (Schaefer) for upper limb motor outcomes post-stroke.
• To evaluate network efficiency using Shortest Structural Path Lengths in stroke patients.
• To determine which atlas provides more accurate clinical predictions for stroke-related motor deficits.

Main Methods:

• Compared Schaefer's 100-parcellation atlas with an Activation Likelihood Estimation (ALE)-based atlas for upper limb motor tasks.
• Assessed 142 stroke patients, quantifying network efficiency via Shortest Structural Path Lengths.
• Utilized a k-NN regression model to predict upper limb motor outcome.

Main Results:

• The ALE-based parcellation showed superior predictive performance over the Schaefer parcellation.
• The ALE model exhibited higher predictive power and better model fit.
• ALE demonstrated lower susceptibility to errors in predicting motor outcomes.

Conclusions:

• Function-specific atlases, such as ALE, offer more accurate and clinically relevant insights into post-stroke motor recovery.
• ALE-based parcellations effectively capture function-specific network changes relevant to motor function.
• These findings suggest improved prediction of upper limb motor outcomes in stroke patients using targeted functional atlases.