A Cerebellar Partitioning Method Using Spectral Clustering With Optimized Nonlinear Functional Connectivity

Cerebellum has a stronger individual specificity of functional signals than the brain and is associated with a variety of neuropsychiatric disorders, and increasing attention is being paid to neuropsychiatric symptoms caused by cerebellar dysfunction. However, there is a lack of a suitable cerebellar partition utilizing researchers to fully understand the functional and structural organization of the cerebellum, reduce data dimensionality, and improve the applicability of various types of models to cerebellar functional imaging data, impeding progress in cerebellum-related research. In this study, we use order-preserving variations with spatial constraints to optimize functional connectivity matrices and employ a spectral clustering algorithm combined with a clustering ensemble technique to construct a cerebellar partitioning algorithm with a variable number of partitions. Our method was initially validated by using two separate sets of functional magnetic resonance data (fMRI), demonstrating high reproducibility across individuals. Comparative analysis revealed that our partitions exhibited enhanced signal coherence and greater spatial congruence with established cerebellar structural templates compared to four publicly available cerebellar atlases. Furthermore, preliminarily applying these partitions to Parkinson's disease (PD) data, we extracted cerebellar connectivity network features and constructed a classification model using a logistic regression model with L2 regularization. The connectivity features derived from our newly constructed cerebellar partitions substantially improved the usability of the Parkinson's classification model, with the classification of PD optimized at a number of partitions equal to 185, suggesting that the optimal number of cerebellar partitions may also vary based on the problem under study. Notably, cerebellar regions implicated in motor execution were identified to exhibit higher feature importance in the Parkinson's classification model, offering an important direction for feature selection in the multimodal classification models of PD.