Individualized cortico-basal ganglia network effective connectivity predicts outcomes of STN-DBS in patients with Parkinson's disease

Background Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease (PD) patients. However, postoperative outcomes vary with no reliable predictive method. Methods Our study involves 43 PD patients undergoing STN-DBS. Preoperative resting-state functional magnetic resonance imagings (rs-fMRI) were collected. The volume of tissue activated (VTA) was defined based on contact points and stimulation parameters. A model of the cortico-basal ganglia network was established using dynamic causal modeling. The correlation between the UPDRS-III and the network edges was determined through Pearson correlation analysis. Furthermore, a generalized linear model was employed to predict the post-DBS motor improvement. Results Individual STN-VTA intersections were found to be important to UPDRS-III improvement induced by DBS (R = 0.59, P = 0.001). STN-VTA intersections were related to the thalamic-primary motor cortex (M1) (R = 0.47, P = 0.005), and M1-STN (R = 0.40, P = 0.006) coupling strength. The coupling strength of Thal-M1 (R = 0.442, P = 0.009) and M1-STN (R = 0.481 P = 0.004) resulted in DBS-induced movement enhancement, particularly rigidity. The strength of effective connections within the STN-Thal-M1 pathway was found to predict improvements in UPDRS-III scores ( P = 0.003). Conclusion Our study confirmed the relationship between clinical improvements in STN-DBS and target location as well as the stimulation parameters. By constructing personalized cortical-basal ganglia network models based on target location as well as the stimulation parameters, we discovered that the effective connection strength in STN-THA-M1 can predict motor improvement in PD patients undergoing STN-DBS.