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Intrinsic connectivity identifies the sensory-motor network as a main cross-network between remitted late-life depression- and amnestic mild cognitive impairment-targeted networks

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Abstract

Remitted late-life depression (rLLD) and amnestic mild cognitive impairment (aMCI) are both associated with a high risk of developing Alzheimer’s disease (AD). Neurodegeneration is considered to spread within pre-existing networks. To investigate whether, in the healthy brain, there was a pre-existing cross-network between the intrinsic networks that are vulnerable to rLLD and aMCI. We performed functional connectivity analyses based on brain areas with the greatest brain neuronal activity differences in 55 rLLD, 87 aMCI, and 114 healthy controls. Intrinsic networks that were differentially vulnerable to rLLD and aMCI converged onto the sensory-motor network (SMN) in the healthy brain. These regions in the SMN within the aMCI- and rLLD-vulnerable networks played different roles in the cognitive functions. This study identifies the SMN as a cross-network between rLLD- and aMCI-vulnerable networks. The common susceptibility of these diseases to AD is likely due to the breakdown of the cross-network. The results further suggest that interventions targeting the amelioration of sensory-motor deficits in the early course of disease in individuals with AD risk may enhance patient function as AD pathology progresses.

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Acknowledgments

This study was supported by the National Natural Science Foundation of China (Nos. 81420108012, 81671046, 81871438, 81571062 and 81701675), the Disciplinary group of Psychology and Neuroscience, Xinxiang Medical University (No. 2016PNKFKT-01), the Strategic Priority Research Program (B) of Chinese Academy of Sciences (Grant No. XDB32020200), the Key Project supported by Medical Science and technology development Foundation, Nanjing Department of Health (No. JQX18005), the Cooperative Research Project of Southeast University-Nanjing Medical University (No. 2018DN0031), and the Key Research and Development Plan (Social Development) Project of Jiangsu Province (No. BE2018608). The authors thank Xiaofa Huang and Hong Zhu for their help with the acquisition of the behavioral data and for taking care of the clinical data in this study.

Author Contributors

Author JC undertook the data analysis and wrote the manuscript. Authors HS, ZW, DL acquired the data. Authors CJ, YL, and ZJZ designed the study. Authors JC, YZ, and YL supervised the data analysis. Author YL and ZJZ provided infrastructure. All authors contributed to and have approved the final manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (Nos. 81420108012, 81671046, 81871438, 81571062 and 81701675), the Disciplinary group of Psychology and Neuroscience, Xinxiang Medical University (No. 2016PNKFKT-01), the Strategic Priority Research Program (B) of Chinese Academy of Sciences (Grant No. XDB32020200), the Key Project supported by Medical Science and technology development Foundation, Nanjing Department of Health (No. JQX18005), the Cooperative Research Project of Southeast University-Nanjing Medical University (No. 2018DN0031), and the Key Research and Development Plan (Social Development) Project of Jiangsu Province (No. BE2018608).

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Authors and Affiliations

  1. Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, Jiangsu, China

    Jiu Chen, Hao Shu, Zan Wang, Duan Liu & Zhijun Zhang

  2. Institute of neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China

    Jiu Chen

  3. Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yafeng Zhan & Yong Liu

  4. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yong Liu

  5. CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yong Liu

  6. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Yong Liu

  7. Department of Psychology, Xinxiang Medical University, Xinxiang, 453003, Henan, China

    Zhijun Zhang

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Chen, J., Shu, H., Wang, Z. et al. Intrinsic connectivity identifies the sensory-motor network as a main cross-network between remitted late-life depression- and amnestic mild cognitive impairment-targeted networks. Brain Imaging and Behavior 14, 1130–1142 (2020). https://doi.org/10.1007/s11682-019-00098-4

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