This study aimed to identify potential markers that can predict Parkinson’s disease with mild cognitive impairment (PDMCI). We retrospectively collected general demographic data, clinically relevant scales, plasma samples, and neuroimaging data (T1-weighted magnetic resonance imaging (MRI) data as well as resting-state functional MRI [Rs-fMRI] data) from 173 individuals. Subsequently, based on the aforementioned multimodal indices, a support vector machine was employed to investigate the machine learning (ML) classification of PD patients with normal cognition (PDNC) and PDMCI. The performance of 29 classifiers was assessed based on various combinations of indicators. Results demonstrated that the optimal classifier in the validation set was composed by clinical + Rs-fMRI+ neurofilament light chain, exhibiting a mean Accuracy of 0.762, a mean area under curve of 0.840, a mean sensitivity of 0.745, along with a mean specificity of 0.783. The ML algorithm based on multimodal data demonstrated enhanced discriminative ability between PDNC and PDMCI patients.

本研究旨在确定可以预测帕金森病伴轻度认知障碍 （PDMCI） 的潜在标志物。我们回顾性收集了 173 例个体的一般人口统计数据、临床相关量表、血浆样本和神经影像学数据 （T1 加权磁共振成像 （MRI） 数据以及静息态功能性 MRI [Rs-fMRI] 数据）。随后，基于上述多模态指数，采用支持向量机研究认知正常 （PDNC） 和 PDMCI 的 PD 患者的机器学习 （ML） 分类。根据各种指标组合评估了 29 个分类器的性能。结果表明，验证集中的最佳分类器由临床 + Rs-fMRI+ 神经丝轻链组成，平均准确率为 0.762，平均曲线下面积为 0.840，平均灵敏度为 0.745，平均特异性为 0.783。基于多模态数据的 ML 算法证明了 PDNC 和 PDMCI 患者之间的区分能力增强。