Integrative analysis of brain functional networks and EEG microstates to elucidate the neural mechanisms of motor imagery and motor execution

Background Motor execution (ME) and motor imagery (MI) share partially overlapping neural mechanisms, yet differ in their patterns of network coordination and temporal dynamics. However, studies integrating functional connectivity and microstate analysis to systematically compare ME and MI remain limited.Objective This study aims to characterize differences in brain region activation, network topology, and whole-brain dynamic organization between ME and MI, providing a theoretical basis for their application in motor rehabilitation.Methods EEG data were collected from 28 healthy participants during ME and MI tasks. Brain functional networks were constructed using phase-locking value (PLV), phase lag index (PLI), Pearson correlation coefficient (PCC), and mutual information, and graph-theoretical measures were applied to evaluate connectivity strength and global network efficiency. In parallel, task-related microstate templates were extracted, and temporal parameters were computed to reveal rapid whole-brain dynamics. Integrating spatial activation and temporal microstate features enabled a comprehensive comparison between ME and MI.Results ME elicited markedly stronger sensorimotor synchronization, higher global efficiency, and more optimized information transmission compared with MI. ME networks predominantly engaged primary motor and somatosensory cortices, whereas MI relied more on premotor regions. Microstate analysis revealed that ME featured more deterministic transitions and prolonged sensory-integration states, while MI exhibited more flexible transitions and extended engagement of working-memory–related states. These findings highlight distinct patterns of neural coordination and temporal organization between the two motor states.Conclusions Although ME and MI share common motor-related network components, they exhibit divergent connectivity strength and dynamic reconfiguration patterns. These differences enhance the understanding of neural mechanisms underlying motor control and support their complementary use in rehabilitation strategies for motor dysfunction.