Evidence that robot-assisted gait training modulates neuroplasticity after stroke: An fMRI pilot study based on graph theory analysis.

Objectives: To investigate alterations of whole-brain network after stroke and therapeutic mechanisms of robot-assisted gait training (RAGT).

Methods: 21 stroke patients and 20 healthy subjects were enrolled, with the stroke patients randomized to either control group (n = 11) or robot group (n = 10), and resting-state functional magnetic resonance imaging data were collected. The global network metrics were obtained using graph theory analysis and compared between stroke patients and healthy subjects, and the effect of the RAGT on the whole-brain networks was explored.

Results: Compared to healthy subjects, area under the curve (AUC) for small-worldness (σ), clustering coefficient (Cp), global efficiency (Eg) and mean local efficiency (Eloc) were significantly lower in stroke patients, whereas AUC for characteristic path length (Lp) were significantly higher. Compared with the control group, patients in robot group showed significant improvement in lower limb motor function, balance function and walking function after intervention, with a significant reduction in the AUC of Cp. Moreover, the improvement of walking function was positively correlated with the changes of AUC of σ and Eg, and negatively correlated with the changes of AUC of Cp.

Conclusions: Small-worldness and network efficiency were significantly reduced after stroke, whereas RAGT decreased characteristic path length and promoted normalization of the whole-brain network, and this change was associated with improvement in walking function. Our findings reveal the mechanism by which RAGT regulates network reorganization and neuroplasticity after stroke.