  The brain continually reorganizes its functional network to adapt to
post-stroke functional impairments. Previous studies using static modularity
analysis have presented global-level behavior patterns of this network
reorganization. However, it is far from understood how the brain reconfigures
its functional network dynamically following a stroke. This study collected
resting-state functional MRI data from 15 stroke patients, with mild (n = 6)
and severe (n = 9) two subgroups based on their clinical symptoms.
Additionally, 15 age-matched healthy subjects were considered as controls. By
applying a multilayer network method, a dynamic modular structure was
recognized based on a time-resolved function network. Then dynamic network
measurements (recruitment, integration, and flexibility) were calculated to
characterize the dynamic reconfiguration of post-stroke brain functional
networks, hence, to reveal the neural functional rebuilding process. It was
found from this investigation that severe patients tended to have reduced
recruitment and increased between-network integration, while mild patients
exhibited low network flexibility and less network integration. It is also
noted that this severity-dependent alteration in network interaction was not
able to be revealed by previous studies using static methods. Clinically, the
obtained knowledge of the diverse patterns of dynamic adjustment in brain
functional networks observed from the brain signal could help understand the
underlying mechanism of the motor, speech, and cognitive functional impairments
caused by stroke attacks. The proposed method not only could be used to
evaluate patients' current brain status but also has the potential to provide
insights into prognosis analysis and prediction.
