Abstract
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 language | English (US) |
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Pages (from-to) | 2507-2516 |
Number of pages | 10 |
Journal | Clinical Neurophysiology |
Volume | 129 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2018 |
Funding
This work was supported by grant 81430023 from the National Natural Science Foundation of China, China (to Dr. Bei-sha Tang), grant 2016YFC1306000 from the National Key Plan for Scientific Research and Development of China, China (to Dr. Bei-sha Tang), grants 81371405 and 81571248 from the National Natural Science Foundation of China (to Dr. Ji-feng Guo), grant 2016CX025 from the innovation-driven plan of Central South University, China (to Dr. Ji-feng Guo). This work was also supported by grant 2016JJ4090 from the Natural Science Foundation of Hunan Province and grants 2017T100613 and 2016M592452 from the China Postdoctoral Science Foundation, China (to Dr. Yan Tang). This work was supported by grant 81430023 from the National Natural Science Foundation of China , China (to Dr. Bei-sha Tang), grant 2016YFC1306000 from the National Key Plan for Scientific Research and Development of China , China (to Dr. Bei-sha Tang), grants 81371405 and 81571248 from the National Natural Science Foundation of China (to Dr. Ji-feng Guo), grant 2016CX025 from the innovation-driven plan of Central South University , China (to Dr. Ji-feng Guo). This work was also supported by grant 2016JJ4090 from the Natural Science Foundation of Hunan Province and grants 2017T100613 and 2016M592452 from the China Postdoctoral Science Foundation , China (to Dr. Yan Tang).
Keywords
- Locally linear embedding
- Multivariate pattern analysis
- Parkinson's disease
- Resting-state functional MRI
- Whole-brain patterns
ASJC Scopus subject areas
- Clinical Neurology
- Neurology
- Sensory Systems
- Physiology (medical)