AUTHOR=Shang Ruihong , He Le , Ma Xiaodong , Ma Yu , Li Xuesong 

TITLE=Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=14

YEAR=2020

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2020.571527

DOI=10.3389/fncom.2020.571527

ISSN=1662-5188

ABSTRACT=<p>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 the outcome of DBS without medication. Fifty patients were recruited to extract the features of the brain related to the improvement rate of PD after STN-DBS and to train the machine learning model that can predict the therapy's effect. The functional connectivity analyses suggested that the GBRT model performed best with Pearson's correlations of <italic>r</italic> = 0.65, <italic>p</italic> = 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.</p>