Paper
9 March 2011 Functional connectivity comparison of the default mode network in non-depressed Parkinson disease and depressed Parkinson disease
Yuan Han, Rui Li, Jiangtao Liu, Li Yao, Xia Wu
Author Affiliations +
Abstract
Examining the spontaneous activity to understand the neural mechanism of brain disorders and establish neuroimaging-based disease-related biomarkers is a focus in recent resting-state functional MRI (fMRI) studies. The present study hypothesized that resting activity in the default mode network (DMN), which was used for characterizing the resting-state human brain might be different in patients with depressed Parkinson disease (dPD) compared with non-depressed Parkinson disease (ndPD) patients. To test the hypothesis, we firstly employed the Group independent component analysis (ICA) approach to isolate the DMN for the two groups by analyzing the resting-state fMRI data from a group of 12 patients with dPD and a group of 12 age-matched ndPD subjects. Between-group comparison of the functional connectivity in the DMN was then performed to examine the impact of depression on the intrinsic activity in PD. We found 1) the core region from the network the medial prefrontal cortex (MPFC) show significant decreased activity in dPD group compared with ndPD group; 2) the activity in MPFC has significant negative correlation with behavioral measure; 3) the resting activity intensity of MPFC is suggested to be a promising biomarker for distinguishing dPD from ndPD.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuan Han, Rui Li, Jiangtao Liu, Li Yao, and Xia Wu "Functional connectivity comparison of the default mode network in non-depressed Parkinson disease and depressed Parkinson disease", Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 79650S (9 March 2011); https://doi.org/10.1117/12.877748
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KEYWORDS
Brain

Independent component analysis

Functional magnetic resonance imaging

Prefrontal cortex

Principal component analysis

Brain mapping

Neuroimaging

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