Identifying And Validating Subtypes Of Parkinson’s Disease Based On Multimodal Mri Data Via Hierarchical Clustering Analysis

Objective: We wished to explore Parkinson’s disease(PD)subtypes by clustering analysis based on the multimodal magnetic resonance imaging(MRI)indices amplitude of lowfrequency fluctuation(ALFF)and gray matter volume(GMV).Then,we analyzed the differences between PD subtypes.Methods: Eighty-six PD patients and 44 healthy controls(HCs)were recruited.We extracted ALFF and GMV according to the Anatomical Automatic Labeling(AAL)partition using Data Processing and Analysis for Brain Imaging(DPABI)software.The Ward linkage method was used for hierarchical clustering analysis.DPABI was employed to compare differences in ALFF and GMV between groups.Results: Two subtypes of PD were identified.The “diffuse malignant subtype” was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function.The UPDRSⅢscore showed a significant negative correlation with GMV in the left thalamus(r =-0.332,p = 0.017),and the HAMD score had a significant negative correlation with GMV in the left temporal lobe(r =-0.312,p = 0.026).The “mild subtype” was characterized by increased ALFF in the frontal lobe,temporal lobe,and sensorimotor cortex,and a slight decrease in GMV with mild impairment of motor function and cognitive function.The UPDRSⅢ score had a negative correlation with GMV in the left thalamus(r =-0.416,p= 0.013),However,the HAMD score showed a significant negative correlation with ALFF in the right temporal lobe(r =-0.500,p = 0.002).Conclusion: Hierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes.These two PD subtypes showed different neurodegenerative patterns upon imaging.Our results provide a new direction for exploring PD subtypes.Study of the pathophysiological mechanism may provide important clues for the prognosis.