Andrew et al. found that by using support vector machine (SVM) to classify clinical data, T1-weighted magnetic resonance imaging (MRI), resting-state functional MRI (Rs-fMRI), and plasma nerve fiber light chain (NfL) and glial fibrillary acidic protein (GFAP) level data, the results showed that The classifier combining clinical data, Rs-fMRI and NfL biomarkers performed the best, highlighting the importance of multimodal data fusion in improving the predictive ability of PD-MCI (65). Here, GFAP is linked to Parkinson disease.