Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease
Ruihong Shang, Le He, Xiaodong Ma, Yu Ma, Xuesong Li
Abstract
Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective invasive treatment for advanced Parkinson’s disease (PD) at present. Due to the invasiveness and cost of operations, a reliable tool is required to predict the outcome of therapy in the clinical decision-making process. This work aims to investigate whether the topological network of functional connectivity states can predict outcome of DBS without medication. 50 patients were recruited to extract the features of brain related to improvement rate of PD after STN-DBS and to train the machine learning model that can predict therapy’s effect. The functional connectivity analyses suggested the GBRT model performed best with Pearson’s correlations of r = 0.65, p = 2.58E-07 in medication-off condition. The connections between Middle Frontal Gyrus (MFG) and Inferior Temporal Gyrus (ITG) contribute most in the GBRT model.