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Evolution of brain network dynamics in early Parkinson’s disease with mild cognitive impairment

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Abstract

How mild cognitive impairment (MCI) is instantiated in dynamically interacting and spatially distributed functional brain networks remains an unexplored mystery in early Parkinson’s disease (PD). We applied a machine-learning technology based on personalized sliding-window algorithm to track continuously time-varying and overlapping subnetworks under the functional brain networks calculated form resting state electroencephalogram data within a sample of 33 early PD patients (13 early PD patients with MCI and 20 early PD patients without MCI). We decoded a set of subnetworks that captured surprisingly dynamically varying and integrated interactions among certain brain lobes. We observed that the master expressed subnetworks were particularly transient, and flexibly switching between high and low expression during integration into a dynamic brain network. This transience was particularly salient in a subnetwork predominantly linking temporal-parietal-occipital lobes, which decreases in both expression and flexibility in early PD patients with MCI and expresses their degree of cognitive impairment. Moreover, MCI induced a regularly interrupted, slow evolution of subnetworks in functional brain network dynamics in early PD at the individual level, and the dynamic expression characteristics of subnetworks also reflected the degree of cognitive impairment in patients with early PD. Collectively, these results provide novel and deeper insights regarding MCI-induced abnormal dynamical interaction and large-scale changes in functional brain network of early PD.

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Data available statement

The datasets generated for this study are available on request to the corresponding author.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 62173241, in part by the Natural Science Foundation of Tianjin, China (Grant No. 20JCQNJC01160), in part by Ministry of Science and Technology of China (Grant Nos. 2016YFC1306500, 2016YFC1306504), in part by National Key R&D Program of China (Grant No. 2016YFC1306501). The authors also gratefully acknowledge the financial support provided by Opening Fundation of Key Laboratory of Opto-technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of Education (KFKT2020-01).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Chunguang Chu, Zhen Zhang, Jiang Wang, Liufang Wang, Chen Liu & Guosheng Yi

  2. Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China

    Xiao Shen, Lipeng Bai, Zhuo Li, Mengmeng Dong & Xiaodong Zhu

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  1. Chunguang Chu
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Correspondence to Chen Liu, Guosheng Yi or Xiaodong Zhu.

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The studies involving human participants were reviewed and approved by Medical Ethics Committee of Tianjin Medical University General Hospital. All subjects understood the purpose of collecting the data and the significance of the study, and signed the written informed consent.

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Chu, C., Zhang, Z., Wang, J. et al. Evolution of brain network dynamics in early Parkinson’s disease with mild cognitive impairment. Cogn Neurodyn 17, 681–694 (2023). https://doi.org/10.1007/s11571-022-09868-1

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