While understanding other’s action intention, mirror and mentalizing systems of human brain are successively activated in action perception and intention inference processes.
Methods:
To reveal the relationship between mirror and mentalizing systems during the two stages, this electroencephalogram study adopted the method of time-varying orthogonalized partial directed coherence (OPDC) to assess causal interaction between mirror and mentalizing networks during a “hand-cup interaction” action intention understanding task.
Results:
Task-related causal connectivity was found in gamma frequency band (30–45 Hz), primarily manifested as directed edges from sensorimotor to frontal areas in poststimulus 400–600 ms interval and directed links from frontal to parietal and temporal regions in 600–800 ms period. The analysis of event-related potential and source currents suggests that the change of inter-regional causality is related with functional transition of the brain from mirror matching to intention inference. The OPDC network modeling further finds that frontal area contains more inflow nodes in mirror network, whereas more outflow nodes in mentalizing network, with high betweenness centrality in temporally changing functional communities. Compared with intention-oriented actions, identification of unintelligible action intention particularly induces stronger OPDC from right superior frontal to inferior frontal gyrus and from sensorimotor to right frontotemporal regions during mentalizing inference process.
Conclusion:
These findings collectively suggest that, in the time ordering of information transfer within the directed networks, frontal area plays an important role of bridging hub between mirror and mentalizing systems, from maintaining and supervising perceptual information for mirror matching to controlling the mentalizing process for decoding other’s action intention.
Impact Statement
From the perspective of neural mechanism of action intention understanding, this study extends previous research to decoding dynamic fluctuations in the brain network structure related to continuous cognitive subprocesses. In the field of human–machine interaction, the electroencephalogram (EEG) features extracted from this study for recognizing different types of action intention have potential application value for medical rehabilitation, such as motor dysfunction caused by stroke, spinal cord injury, and so on, by monitoring and analyzing patients’ EEG signals. In addition, the EEG features can also be applied to controlling prosthetic limbs, exoskeletons, and other assistive devices to help patients restore or improve movement.
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