Multimodal imaging and machine learning for diagnosis of Parkinson’s disease with cognitive impairment: ASL and QSM as potential biomarkers

<b>Objectives:</b> This study aimed to investigate differences in brain imaging characteristics among patients with Parkinson's disease with cognitive impairment (PDCI), Parkinson's disease without cognitive impairment (PDNCI), and healthy controls (HC), and to develop machine learning-based models for the early diagnosis of PDCI. A total of 48 patients with PDCI, 50 patients with PDNCI, and 47 age- and sex-matched healthy controls were enrolled, all of whom underwent magnetic resonance imaging using a 3.0 T MRI scanner. Arterial spin labeling (ASL) was applied to quantify cerebral blood flow (CBF), and quantitative susceptibility mapping (QSM) was used to assess magnetic susceptibility, while cognitive function was evaluated using standardized neuropsychological scales. Group differences were examined using one-way analysis of variance (ANOVA), and seven machine learning classifiers, including random forest (RF), K-nearest neighbors (KNN), and Extreme Gradient Boosting (XGB), were constructed to discriminate among the PDCI, PDNCI, and HC groups. The ANOVA results revealed significant differences in both CBF and magnetic susceptibility between the HC group and the two PD groups, whereas no significant differences were observed between the PDCI and PDNCI groups. Compared with normative data, patients with PDCI exhibited cognitive impairments exceeding 2 standard deviations in the domains of language, attention, and working memory, as well as impairments exceeding 1 standard deviation in visuospatial function, memory, and executive function. Among the machine learning models, RF, KNN, and XGB achieved perfect classification performance, with all evaluation metrics reaching 1.000, indicating excellent discriminative capability. Feature importance analysis identified increased CBF and magnetic susceptibility in regions such as the left precuneus (Precuneus_L) and left postcentral gyrus (Postcentral_L) as key imaging features distinguishing PDCI, and correlation analyses further demonstrated significant associations between cognitive deficits and alterations in CBF and magnetic susceptibility. These findings suggest that ASL- and QSM-derived imaging features have substantial potential as non-invasive biomarkers for the early diagnosis of PDCI, that patients with PDCI exhibit widespread impairments across multiple cognitive domains-particularly in language, attention, and working memory-and that machine learning models integrating multimodal imaging features provide a reliable and effective approach for early diagnosis and may facilitate personalized treatment strategies in Parkinson's disease, although future studies with larger sample sizes and independent validation cohorts are warranted to enhance the robustness and generalizability of these models.