Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson’s disease

BackgroundParkinson's disease (PD) is a heterogeneous condition. Cluster analysis based on cortical thickness has been used to define distinct patterns of brain atrophy in PD. However, the potential other neuroimaging modalities, such as white matter (WM) fractional anisotropy (FA), which also demonstrated be altered PD, not investigated.ObjectiveWe aim characterize PD subtypes using multimodal clustering approach and subcortical gray (GM) volumes FA measures.MethodsWe included T1-weighted diffusion-weighted MRI data from 62 patients 33 healthy controls. We extracted mean GM 48 17 regions FSL-VBM, 20 WM tracts Tract-Based Spatial Statistics (TBSS). Hierarchical cluster was performed with sample Ward's linkage method. Whole-brain voxel-wise intergroup comparisons VBM TBSS were FSL. Neuropsychological demographic statistical analyses conducted IBM SPSS 25.0.ResultsWe identified three subtypes, prominent differences little involvement. One group (n = 15) widespread volume reductions pronounced cognitive deficits; second 21) only limited frontal temporal more specific neuropsychological impairment, third 26) without detectable or cognition impairment.ConclusionMultimodal allows classifying into groups according patterns, turn are associated profile.