Data_Sheet_1_Early identification of Parkinson’s disease with anxiety based on combined clinical and MRI features.docx

ObjectiveTo identify cortical and subcortical volume, thickness and cortical area features and the networks they constituted related to anxiety in Parkinson’s disease (PD) using structural magnetic resonance imaging (sMRI), and to integrate multimodal features based on machine learning to identify PD-related anxiety.MethodsA total of 219 patients with PD were retrospectively enrolled in the study. 291 sMRI features including cortical volume, subcortical volume, cortical thickness, and cortical area, as well as 17 clinical features, were extracted. Graph theory analysis was used to explore structural networks. A support vector machine (SVM) combination model, which used both sMRI and clinical features to identify participants with PD-related anxiety, was developed and evaluated. The performance of SVM models were evaluated. The mean impact value (MIV) of the feature importance evaluation algorithm was used to rank the relative importance of sMRI features and clinical features within the model.Results17 significant sMRI variables associated with PD-related anxiety was used to build a brain structural network. And seven sMRI and 5 clinical features with statistically significant differences were incorporated into the SVM model. The comprehensive model achieved higher performance than clinical features or sMRI features did alone, with an accuracy of 0.88, a precision of 0.86, a sensitivity of 0.81, an F1-Score of 0.83, a macro-average of 0.85, a weighted-average of 0.92, an AUC of 0.88, and a result of 10-fold cross-validation of 0.91 in test set. The sMRI feature right medialorbitofrontal thickness had the highest impact on the prediction model.ConclusionWe identified the brain structural features and networks related to anxiety in PD, and developed and internally validated a comprehensive model with multimodal features in identifying.

研究目的在于利用结构磁共振成像（sMRI）技术，识别帕金森病（PD）患者皮质和皮质下体积、厚度及皮质面积等特征及其构成的神经网络，并基于机器学习整合多模态特征，以识别与PD相关的焦虑症状。研究方法包括：回顾性纳入219名PD患者，提取包括皮质体积、皮质下体积、皮质厚度和皮质面积在内的291个sMRI特征，以及17个临床特征。运用图论分析探索结构网络，开发并评估了一种支持向量机（SVM）组合模型，该模型结合sMRI和临床特征以识别具有PD相关焦虑的参与者。SVM模型的性能通过特征重要性评估算法的平均影响值（MIV）进行评估。结果显示，与PD相关焦虑相关的17个显著sMRI变量被用于构建脑结构网络，其中7个sMRI特征和5个临床特征在统计上具有显著差异，并被纳入SVM模型。综合模型在单独的临床特征或sMRI特征的基础上，实现了更高的性能，准确率达到0.88，精确率为0.86，敏感度为0.81，F1分数为0.83，宏观平均值为0.85，加权平均值为0.92，AUC值为0.88，测试集10折交叉验证结果为0.91。sMRI特征右侧内侧额叶厚度对预测模型的贡献最大。研究结论为：我们识别了与PD相关焦虑的脑结构特征和网络，并开发了一个综合模型，通过多模态特征在识别PD相关焦虑方面进行了内部验证。