The identification of interacting brain networks during robot-assisted training with multimodal stimulation


 Objective: Robot-assisted rehabilitation training is an effective way to assist rehabilitation therapy. So far, various robotic devices have been developed for automatic training of central nervous system following injury. Multimodal stimulation such as visual and auditory stimulus and even virtual reality (VR) technology were usually introduced in these robotic devices to improve the effect of rehabilitation training. This may need to be explained from a neurological perspective, but there are few relevant studies. Approach: In this study, ten participants performed right arm rehabilitation training tasks using an upper limb rehabilitation robotic device. The tasks were completed under four different feedback conditions including multiple combinations of visual and auditory components: auditory feedback (AF); visual feedback (VF); visual and auditory feedback (VAF); non-feedback (NonF). The functional near-infrared spectroscopy (fNIRS) devices record blood oxygen signals in bilateral motor, visual and auditory areas. Using hemoglobin concentration as an indicator of cortical activation, the effective connectivity of these regions was then calculated through Granger causality. Main results: We found that overall stronger activation and effective connectivity between related brain regions were associated with VAF. When participants completed the training task without visual and auditory feedback, the trends in activation and connectivity were diminished. Significance: This study revealed cerebral cortex activation and interacting networks of brain regions in robot-assisted rehabilitation training with multimodal stimulation, which is expected to provide indicators for further evaluation of the effect of rehabilitation training, and promote further exploration of the interaction network in the brain during a variety of external stimuli, and to explore the best sensory combination.