LAUSR

Differentiating the parkinsonian variant of multiple system atrophy (MSA‐P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA‐P patients and use machine learning methods to explore the diagnostic utility of these features. Resting‐state fMRI data were acquired from 88 healthy controls, 76 MSA‐P patients, and 53 IPD patients using a 3.0 T scanner. The whole‐brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA‐P patients. Regarding graph metrics, early IPD and MSA‐P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum‐basal ganglia‐cortical network, but these disconnections were mainly in the cortico‐thalamo‐cerebellar circuits in MSA‐P patients and the basal ganglia‐thalamo‐cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA‐P and IPD patients show similar whole‐brain network topological alterations. MSA‐P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum‐basal ganglia‐cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA‐P and IPD.