Investigation of the effective connectivity of resting state networks in Alzheimer's disease: a functional MRI study combining independent components analysis and multivariate Granger causality analysis

Authors

  • Zhenyu Liu,

    1. Intelligent Medical Research Center, State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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    • These authors contributed equally to this work.
  • Yumei Zhang,

    1. Neurology Department, Beijing Tiantan Hospital, affiliated with Capital Medical University, Beijing, China
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    • These authors contributed equally to this work.
  • Lijun Bai,

    1. Intelligent Medical Research Center, State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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    • These authors contributed equally to this work.
  • Hao Yan,

    1. School of Psychology, Shaanxi Normal University, Xi'an, China
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  • Ruwei Dai,

    1. Intelligent Medical Research Center, State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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  • Chongguang Zhong,

    1. Intelligent Medical Research Center, State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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  • Hu Wang,

    1. Intelligent Medical Research Center, State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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  • Wenjuan Wei,

    1. Intelligent Medical Research Center, State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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  • Ting Xue,

    1. Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
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  • Yuanyuan Feng,

    1. Intelligent Medical Research Center, State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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  • Youbo You,

    1. Intelligent Medical Research Center, State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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  • Jie Tian

    Corresponding author
    1. Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
    • Intelligent Medical Research Center, State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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J. Tian, Institute of Automation, Chinese Academy of Sciences, PO Box 2728, Beijing 100190, China

E-mail: tian@ieee.org

Abstract

Recent neuroimaging studies have shown that the cognitive and memory decline in patients with Alzheimer's disease (AD) is coupled with abnormal functions of focal brain regions and disrupted functional connectivity between distinct brain regions, as well as losses in small-world attributes. However, the causal interactions among the spatially isolated, but functionally related, resting state networks (RSNs) are still largely unexplored. In this study, we first identified eight RSNs by independent components analysis from resting state functional MRI data of 18 patients with AD and 18 age-matched healthy subjects. We then performed a multivariate Granger causality analysis (mGCA) to evaluate the effective connectivity among the RSNs. We found that patients with AD exhibited decreased causal interactions among the RSNs in both intensity and quantity relative to normal controls. Results from mGCA indicated that the causal interactions involving the default mode network and auditory network were weaker in patients with AD, whereas stronger causal connectivity emerged in relation to the memory network and executive control network. Our findings suggest that the default mode network plays a less important role in patients with AD. Increased causal connectivity of the memory network and self-referential network may elucidate the dysfunctional and compensatory processes in the brain networks of patients with AD. These preliminary findings may provide a new pathway towards the determination of the neurophysiological mechanisms of AD. Copyright © 2012 John Wiley & Sons, Ltd.

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