Short-Term Longitudinal Study on Brain Network Informatics of Stroke Patients Under Acupuncture and Motor Imagery Intervention

Jing Qu, Yijun Du, Jing Jing, Jie Wang, Lingguo Bu, Yonghui Wang

Abstract: Objective: The quest for scientifically effective rehabilitation methods for stroke recovery constitutes an urgent need. However, due to the inadequacies of longitudinal studies and multimodal assessment methods, the rehabilitation mechanisms of methods such as Acupuncture Treatment (AT) and Motor Imagery (MI) remain unclear. Consequently, this study presents both AT and Acupuncture Synchronized with MI (ASMI) therapies, utilizing a combination of subjective and objective approaches to evaluate the long-term impacts of these two treatment modalities. Methods: A longitudinal design was adopted for a duration of two weeks. Clinical improvement in patients was assessed using scale data, while Functional Near-infrared spectroscopy (fNIRS) and Electroencephalogram (EEG) data were collected to analyze changes in brain function. This study proposed the Cluster-Span Threshold for Directed Networks (CSTDN) algorithm for identifying key connections within the brain network and conducted in-depth analysis using graph theory metrics. Results: Scale data indicated improvements in behavioral capabilities in both groups post-treatment. EEG and fNIRS data revealed significant variations in specific frequency bands between the two groups. Conclusion: This study not only validates the efficacy of AT and ASMI in stroke rehabilitation but also unveils the underlying neurobiological mechanisms through multimodal data analysis. The proposed CSTDN algorithm and graph theory analysis offer new perspectives for understanding changes in the brain network. Significance: This research contributes to the optimization of future rehabilitation treatment strategies and the formulation of personalized treatment plans.

External IDs:dblp:journals/titb/QuDJWBW25