Motor imagery and motor execution both cause similar changes in brain oscillations over spatially almost identical cortical areas. Because of this finding, motor imagery has been suggested as a backdoor to the motor system in stroke rehabilitation. Yet, neuroimaging and lesion studies suggest that the networks involved in motor imagery and execution are at least partially distinct. Similar spatial patterns of power modulation can be caused by different brain networks. This conflict between EEG and fMRI puts the evidence for shared networks somewhat in doubt – and therefore the rationale to use motor imagery neurofeedback as treatment.
In this study, we disentangled this relationship by studying the role of brain-robot interfaces in the context of motor imagery and motor execution networks. We let healthy right-handed subjects perform several behavioral tasks involving imagery and execution of movements of the left hand. These were kinesthetic imagery (KIS), visual imagery (VIS), tonic muscle contraction (EMG) and visuomotor integration (VMI). In addition, subjects used a brain-robot-interface, i.e. performed motor imagery supported by haptic/proprioceptive feedback. We used a principal component analysis to assess the relationship of these indicators and found two main components. We investigated cortical resting state networks in the α-range using the phase slope index (similar, but slightly more robust than Nolte). We detected that two distinct cortical networks were underlying motor control: a motor imagery network connecting the left parietal and motor areas with the right prefrontal cortex and a motor execution network characterized by transmission from the left to right motor areas. Even more important: A brain robotic interface was able to recruit both abilities and networks.
Put shortly: We found strong empirical evidence that a brain-robot-interface recruits the abilities for motor imagery and execution. It offers therefore a way to bridge the gap between these networks, opening a backdoor to the motor execution system.
You can find the full paper published in Neuroimage: Bauer, Robert, Meike Fels, Mathias Vukelić, Ulf Ziemann, and Alireza Gharabaghi. “Bridging the Gap between Motor Imagery and Motor Execution with a Brain-Robot Interface.” NeuroImage, 2014. doi:10.1016/j.neuroimage.2014.12.026.