基于连接组的模型预测帕金森病深部脑刺激的结果。
Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease.
作者信息
Shang Ruihong, He Le, Ma Xiaodong, Ma Yu, Li Xuesong
机构信息
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing, China.
出版信息
Front Comput Neurosci. 2020 Oct 28;14:571527. doi: 10.3389/fncom.2020.571527. eCollection 2020.
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 = 0.65, = 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.
目前，丘脑底核深部脑刺激（STN-DBS）是治疗晚期帕金森病（PD）的一种有效侵入性疗法。由于手术的侵入性和成本，在临床决策过程中需要一种可靠的工具来预测治疗结果。这项工作旨在研究功能连接状态的拓扑网络是否能够在不服药的情况下预测DBS的治疗结果。招募了50名患者，提取与STN-DBS术后PD改善率相关的脑特征，并训练能够预测治疗效果的机器学习模型。功能连接分析表明，在不服药状态下，梯度提升回归树（GBRT）模型表现最佳，Pearson相关系数为 = 0.65， = 2.58E-07。在GBRT模型中，额中回（MFG）和颞下回（ITG）之间的连接贡献最大。
Zotero 插件