Enhancing Neural Representations of Motor Imagery Through Action-Specific Brain Connectivity Patterns

Motor imagery (MI) is a cognitive process that allows individuals to mentally simulate movements without physical executio n. However, the exploration of functional connectivity (FC) and lateralization mechanisms under different MI actions remains insufficiently understood. In this work, the common orthogonal basis extraction (COBE) algorithm was employed to isolate action-specific components by removing shared background components from the raw FC of the MI process. We demonstrate that action-specific FC effectively captures the hemispheric statistical differences between left- and right-hand MI, outperforming traditional FC and temporal variability measures. And through a comprehensive analysis of network properties at three distinct levels, encompassing the whole-brain network properties, hemispherical properties, and individual nodal strength, complex lateralization patterns associated with diverse types of MI processes were successfully discerned. Furthermore, lateralization indices were further calculated to quantitatively reveal the degree of brain lateralization. Notably, the lateralization performance (LP) derived from action-specific FC exhibited a significant predictive capacity for MI performance, thereby suggesting its potential to evaluate individual MI capability. Collectively, these findings validate the action-specific FC patterns in characterizing neural mechanisms of MI processes and indicate that the LP could potentially be a useful tool to predict the MI performance of MI-based brain-computer inference (BCI), thereby contributing to the formulation of personalized therapeutic strategies for clinical rehabilitation from a new perspective.