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Brain Imaging Behav. 2017 Feb;11(1):224-239. doi: 10.1007/s11682-016-9514-9.

Frequency specific brain networks in Parkinson's disease and comorbid depression.

Author information

1
Department of Biomedical Engineering, Peking University, Beijing, 100871, China.
2
School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China. zhangyiuf@gmail.com.
3
Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.
4
Department of Biological Sciences, National University of Singapore, Singapore, 119077, Singapore.
5
Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.
6
Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China.
7
Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China. jgao@pku.edu.cn.
8
Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China. jgao@pku.edu.cn.
9
McGovern Institute for Brain Research, Peking University, Beijing, 100871, China. jgao@pku.edu.cn.

Abstract

The topological organization underlying the human brain was extensively investigated using resting-state functional magnetic resonance imaging, focusing on a low frequency of signal oscillation from 0.01 to 0.1 Hz. However, the frequency specificities with regard to the topological properties of the brain networks have not been fully revealed. In this study, a novel complementary ensemble empirical mode decomposition (CEEMD) method was used to separate the fMRI time series into five characteristic oscillations with distinct frequencies. Then, the small world properties of brain networks were analyzed for each of these five oscillations in patients (n = 67) with depressed Parkinson's disease (DPD, n = 20) , non-depressed Parkinson's disease (NDPD, n = 47) and healthy controls (HC, n = 46). Compared with HC, the results showed decreased network efficiency in characteristic oscillations from 0.05 to 0.12 Hz and from 0.02 to 0.05 Hz for the DPD and NDPD patients, respectively. Furthermore, compared with HC, the most significant inter-group difference across five brain oscillations was found in the basal ganglia (0.01 to 0.05 Hz) and paralimbic-limbic network (0.02 to 0.22 Hz) for the DPD patients, and in the visual cortex (0.02 to 0.05 Hz) for the NDPD patients. Compared with NDPD, the DPD patients showed reduced efficiency of nodes in the basal ganglia network (0.01 to 0.05 Hz). Our results demonstrated that DPD is characterized by a disrupted topological organization in large-scale brain functional networks. Moreover, the CEEMD analysis suggested a prominent dissociation in the topological organization of brain networks between DPD and NDPD in both space and frequency domains. Our findings indicated that these characteristic oscillatory activities in different functional circuits may contribute to distinct motor and non-motor components of clinical impairments in Parkinson's disease.

KEYWORDS:

Brain Network; Complementary Ensemble Empirical Mode Decomposition; Depression; Frequency Specificity; Parkinson’s Disease

PMID:
26849374
PMCID:
PMC5415593
DOI:
10.1007/s11682-016-9514-9
[Indexed for MEDLINE]
Free PMC Article

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