Improving health-related quality of life (QoL) is crucial for managing Parkinson’s disease. However, QoL outcomes after deep brain stimulation (DBS) of the subthalamic nucleus (STN) vary considerably. Current approaches lack integration of demographic, patient-reported, neuroimaging, and neurophysiological data to understand this variability. This study used explainable machine learning to analyze multimodal factors affecting QoL changes, measured by the Parkinson’s Disease Questionnaire (PDQ-39) in 63 patients, and quantified each variable’s contribution. Results showed that preoperative PDQ-39 scores and upper beta band activity (>20 Hz) in the left STN were key predictors of QoL changes. Lower initial QoL burden predicted worsening, while improvement was associated with higher beta activity. Additionally, electrode positions along the superior-inferior axis, especially relative to the z = −7 coordinate in standard space, influenced outcomes, with improved and worsened QoL above and below this marker. This study emphasizes a tailored, data-informed approach to optimize DBS treatment and improve patient QoL.

改善与健康相关的生活质量 （QoL） 对于管理帕金森病至关重要。然而，丘脑底核 （STN） 深部脑刺激 （DBS） 后的 QoL 结果差异很大。目前的方法缺乏对人口统计学、患者报告、神经影像学和神经生理学数据的整合，无法理解这种可变性。本研究使用可解释的机器学习来分析影响 QoL 变化的多模式因素，通过帕金森病问卷 （PDQ-39） 在 63 名患者中测量，并量化每个变量的贡献。结果显示，术前 PDQ-39 评分和左侧 STN 的上 β 波段活性 （>20 Hz） 是 QoL 变化的关键预测因子。较低的初始 QoL 负担预示着恶化，而改善与较高的 β 活性相关。此外，沿上下轴的电极位置，尤其是相对于标准空间中的 z = −7 坐标，影响了结果，在该标记之上和以下的 QoL 改善和恶化。本研究强调一种量身定制的、以数据为依据的方法来优化 DBS 治疗并改善患者的生活质量。