Mean Motor Disconnectivity Predicts Hand Function Impairment After Stroke: Beyond Motor Cortex Output

Area of Science:

• Neuroscience
• Neurology
• Medical Imaging

Background:

• Persistent hand dysfunction is a common stroke deficit, linked to primary motor cortex (M1) and corticospinal tract (CST) damage.
• Current assessment methods like CST lesion load and motor evoked potential (MEP) status show significant inter-individual variability.
• The need for improved measures to predict hand function recovery after stroke is critical.

Purpose of the Study:

• To investigate if mean motor disconnectivity (MMD), a novel measure, explains additional variance in hand function among subacute stroke patients.
• To compare the predictive power of MMD against established measures of stroke-related motor pathway damage.

Main Methods:

• Thirty-two subacute ischemic stroke patients with upper extremity weakness were studied.
• Skilled hand function was assessed using the Jebsen-Taylor test.
• Mean motor disconnectivity (MMD) was calculated from structural MRI-based lesion masks within a 20-region motor network; MEP status and CST lesion load were also measured.

Main Results:

• Motor evoked potential (MEP) status, CST lesion load, and MMD were significantly correlated with preserved hand function.
• Lesion volume alone was not a significant predictor.
• Including MMD in a multivariable model significantly improved its fit compared to a model with MEP status alone.

Conclusions:

• Mean motor disconnectivity (MMD) explains additional variability in skilled hand function post-stroke, beyond MEP status.
• Disconnectivity measures capture stroke impact on motor areas more comprehensively than traditional metrics.
• MMD shows promise as a more sensitive biomarker for predicting motor recovery after stroke.