Parkinson's Disease—Related Spatial Covariance Pattern Identified with Resting-State Functional MRI. Issue 11 (November 2015)

• Record Type: Journal Article
• Authors: Wu, Tao; Ma, Yilong; Zheng, Zheng; Peng, Shichun; Wu, Xiaoli; Eidelberg, David; Chan, Piu
• Is Part Of: Journal of cerebral blood flow & metabolism. Volume 35:Issue 11(2015)
• Page Start: 1764
• Page End: 1770
• Publication Date: 2015-11
• Subjects: biomarker -- disease-related spatial covariance pattern -- Parkinson's disease -- resting-state fMRI -- SSMPCA; Cerebral circulation -- Periodicals; Brain -- Metabolism -- Periodicals; Brain -- Blood-vessels -- Periodicals; Cerebrovascular disease -- Periodicals; 612.824
• DOI: 10.1038/jcbfm.2015.118
• Languages: English
• ISSNs: 0271-678X

Abstract

In this study, we sought to identify a disease-related spatial covariance pattern of spontaneous neural activity in Parkinson's disease using resting-state functional magnetic resonance imaging (MRI). Time-series data were acquired in 58 patients with early to moderate stage Parkinson's disease and 54 healthy controls, and analyzed by Scaled Subprofile Model Principal Component Analysis toolbox. A split-sample analysis was also performed in a derivation sample of 28 patients and 28 control subjects and validated in a prospective testing sample of 30 patients and 26 control subjects. The topographic pattern of neural activity in Parkinson's disease was characterized by decreased activity in the striatum, supplementary motor area, middle frontal gyrus, and occipital cortex, and increased activity in the thalamus, cerebellum, precuneus, superior parietal lobule, and temporal cortex. Pattern expression was elevated in the patients compared with the controls, with a high accuracy (90%) to discriminate the patients from the controls. The split-sample analysis produced a similar pattern but with a lower accuracy for group discrimination in both the derivation (80%) and the validation (73%) samples. Our results showed that resting-state functional MRI can be potentially useful for identification of Parkinson's disease–related spatial covariance patterns, and for differentiation of Parkinson's disease patients from healthy controls at an individual level.