Identifying mild-moderate Parkinson's disease using whole-brain functional connectivity

Yan Tang, Bailin Liu, Yuan Yang, Chang min Wang, Li Meng, Bei sha Tang, Ji feng Guo

Research output: Contribution to journalArticlepeer-review


Objective: Our study aims to extract significant disorder-associated patterns from whole brain functional connectivity to distinguish mild-moderate Parkinson's disease (PD) patients from controls. Methods: Resting-state fMRI data were measured from thirty-six PD individuals and thirty-five healthy controls. Multivariate pattern analysis was applied to investigate whole-brain functional connectivity patterns in individuals with ‘mild-moderate’ PD. Additionally, the relationship between the asymmetry of functional connectivity and the side of the initial symptoms was also analyzed. Results: In a leave-one-out cross-validation, we got the generalization rate of 80.28% for distinguishing PD patients from controls. The most discriminative functional connectivity was found in cortical networks that included the default mode, sensorimotor and attention networks. Compared to patients with the left side initially affected, an increased abnormal functional connectivity was found in patients in whom the right side was initially affected. Conclusions: Our results indicated that discriminative functional connectivity is likely associated with disturbances of cortical networks involved in sensorimotor control and attention. The spatiotemporal patterns of motor asymmetry may be related to the lateralized dysfunction on the early stages of PD. Significance: This study identifies discriminative functional connectivity that is associated with disturbances of cortical networks. Our results demonstrated new evidence regarding the functional brain changes related to the unilateral motor symptoms of early PD.

Original languageEnglish (US)
Pages (from-to)2507-2516
Number of pages10
JournalClinical Neurophysiology
Issue number12
StatePublished - Dec 2018
Externally publishedYes


  • Locally linear embedding
  • Multivariate pattern analysis
  • Parkinson's disease
  • Resting-state functional MRI
  • Whole-brain patterns

ASJC Scopus subject areas

  • Sensory Systems
  • Neurology
  • Clinical Neurology
  • Physiology (medical)


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