Motor Impairment in Stroke Patients Is Associated With Network Properties During Consecutive Motor Imagery

Objective: Our study aimed to predict the Fugl-Meyer assessment (FMA) upper limb using network properties during motor imagery using electroencephalography (EEG) signals. Methods: The subjects performed a finger tapping imagery task according to consecutive cues. We measured the weighted phase lag index (wPLI) as functional connectivity and directed transfer function (DTF) as causal connectivity in healthy controls and stroke patients. The network properties based on the wPLI and DTF were calculated. We predicted the FMA upper limb using partial least squares regression. Results: A higher DTF in the mu band was observed in stroke patients than in healthy controls. Notably, the difference in local properties at node F3 was negatively correlated with motor impairment in stroke patients. Finally, using significant network properties based on the wPLI and DTF, we predicted motor impairments using the FMA upper limb with a root-mean-square error of 1.68 (R-2 = 0.97). This outperformed the state-of-the-art predictors. Conclusion: These findings demonstrate that network properties based on functional and causal connectivity were highly associated with motor function in stroke patients. Significance: Our network properties can help calculate the predictor of motor impairments in stroke rehabilitation and provide insight into the neural correlates related to motor function based on EEG after reorganization induced by stroke.

Automated summary

This study aimed to predict Fugl-Meyer assessment (FMA) upper limb using network properties derived from functional and causal connectivity measured by electroencephalography (EEG) signals. Results showed that stroke patients exhibited higher directed transfer function (DTF) in the mu band compared to healthy controls, and there was a negative correlation between local properties at node F3 and motor impairment in stroke patients. By utilizing significant network properties based on weighted phase lag index (wPLI) and DTF, the FMA upper limb could be predicted with high accuracy. These findings provide important insights into the neural correlates of motor function in stroke patients and contribute to the development of motor impairment predictors in stroke rehabilitation.