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.
基金：
Applied Basic Research Foundation of Yunnan Province [Grant numbers 202301AS070045, 202101AY070001-115];National Natural Science Foundation of China [Grant number 81960242];The Major Science and Technology Special Project of Yunnan Province[Grant number 202102AA100069]; The Innovative Team of Yunnan Province [202305AS350019]; Shaanxi Provincial Natural Science Basic Research
Program [Grant number 2024JC-YBQN-0822].
