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 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.

中文翻译：

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基于连接组的模型预测帕金森病的深部脑刺激结果


丘脑底核深部脑刺激（STN-DBS）是目前治疗晚期帕金森病（PD）的有效侵入性治疗方法。由于手术的侵入性和成本，临床决策过程中需要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可以在不使用药物的情况下预测 DBS 的结果。招募了 50 名患者，提取与 STN-DBS 后 PD 改善率相关的大脑特征，并训练可以预测治疗效果的机器学习模型。功能连接分析表明，GBRT 模型在停药条件下表现最佳，Pearson 相关性为 r = 0.65，p = 2.58E−07。额中回 (MFG) 和颞下回 (ITG) 之间的连接在 GBRT 模型中贡献最大。