Clinically relevant connectivity features define three subtypes of Parkinson's disease patients

Parkinson's disease (PD) is characterized by complex clinical symptoms, including classic motor and nonmotor disturbances. Patients with PD vary in clinical manifestations and prognosis, which point to the existence of subtypes. This study aimed to find the fiber connectivity correlations with...

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Bibliographic Details
Published in:Human brain mapping 2020-10, Vol.41 (14), p.4077-4092
Main Authors: Guo, Tao, Guan, Xiaojun, Zhou, Cheng, Gao, Ting, Wu, Jingjing, Song, Zhe, Xuan, Min, Gu, Quanquan, Huang, Peiyu, Pu, Jiali, Zhang, Baorong, Cui, Feng, Xia, Shunren, Xu, Xiaojun, Zhang, Minming
Format: Article
Language:English
Subjects:
Abnormalities
Brain
Brain damage
Brain injury
Cluster analysis
Clustering
clustering analysis
Connectivity
Correlation analysis
Depression, Mental
Diagnostic imaging
heterogeneity
Impact damage
magnetic resonance imaging
Medical colleges
Medical imaging
Movement disorders
Neural networks
Neurodegenerative diseases
Neuroimaging
Neurophysiology
Parkinson's disease
Signs and symptoms
Structure-function relationships
subtype
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Summary:Parkinson's disease (PD) is characterized by complex clinical symptoms, including classic motor and nonmotor disturbances. Patients with PD vary in clinical manifestations and prognosis, which point to the existence of subtypes. This study aimed to find the fiber connectivity correlations with several crucial clinical symptoms and identify PD subtypes using unsupervised clustering analysis. One hundred and thirty‐four PD patients and 77 normal controls were enrolled. Canonical correlation analysis (CCA) was performed to define the clinically relevant connectivity features, which were then used in the hierarchical clustering analysis to identify the distinct subtypes of PD patients. Multimodal neuroimaging analyses were further used to explore the neurophysiological basis of these subtypes. The methodology was validated in an independent data set. CCA revealed two significant clinically relevant patterns (motor‐related pattern and depression‐related pattern; r = .94, p 
ISSN:1065-9471
1097-0193
DOI:10.1002/hbm.25110