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Spatial–rhythmic network as a biomarker of familial risk for psychotic bipolar disorder

Nature Mental Health volume 1, pages 887–899 (2023)Cite this article

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Abstract

Neuronal rhythms with different temporospatial dynamics are prominent signatures of brain operation. Yet, the synchronous coupling across multiple rhythms and spatially distributed subsystems, as well as its role in brain cognition and disease, remains mysterious. Here we proposed a conceptually new framework to construct the large-scale spatial–rhythmic network (SRN) and apply it to case–control P300 electroencephalogram datasets. Results show that SRN configurations are essential substrates of attentional allocation and immediate memory for healthy controls (N = 235), yielding prominent inter-rhythmic interactions between the δ-frontoparietal/δ-limbic network and other rhythmic subnetworks during P300 generation. Importantly, SRN deviances shared by patients with bipolar disorder (N = 188) and their first-degree relatives (N = 201) might be putative electrophysiological biomarkers for clinical screening of individuals at high familial risk of disease onset. The findings emphasize that configurations of SRNs have a previously unrecognized role in cognitive (dys)functions.

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Fig. 1: Schematic illustration of SRN construction.
Fig. 2: Differences in ERP, SRN topologies and network properties between the target (n = 200) and standard tones (n = 200).
Fig. 3: Relationship between large-scale SRN and P300 amplitudes of healthy people (n = 200).
Fig. 4: Disease-specific changes of the large-scale SRN.

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Data availability

The datasets used in this work can be found at the National Institute of Mental Health Data Archive (https://nda.nih.gov/). The data are available on NDA through collection ID 2274.

Code availability

The MEMD algorithm that performed on Python 3.7 (https://www.python.org/) is available on the mariogrune GitHub (https://github.com/mariogrune). The EEG pre-processing toolboxes are freely available (EEGLAB v2020.0, https://sccn.ucsd.edu/eeglab/index.php, and REST_v1.2_20200818, https://github.com/webrain2018/REST). Large-scale SRN properties were computed by the Brain Connectivity Toolbox (www.nitrc.org/projects/bct/). The S estimator and statistical analyses including correlation analysis and t-test were conducted using MATLAB R2018b (https://ww2.mathworks.cn/products/matlab.html). The visualization of subnetwork distribution images was conducted using BrainNetViewer 1.7 (https://www.nitrc.org/projects/bnv/). The statistical visualization of P300 amplitude and network properties was conducted using GraphPad Prism 8.3 (https://www.graphpad.com/).

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Acknowledgements

This work was supported by the STI 2030-Major Projects (grant no. 2022ZD0211400 to F.L.; no. 2022ZD0208500 to D.Y.; no. 2022ZD0208900 to Y.L.), the National Natural Science Foundation of China (no. 62103085 to F.L.; no. U19A2082 to P.X.), the Key R&D projects of Science & Technology Department of Sichuan Province (no. 23ZDYF0961 to L.Y.) and the Scientific Research Foundation of Sichuan Provincial People’s Hospital (no. 2021LY21 to L.Y.).

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Author notes

  1. These authors contributed equally: Lin Jiang, Yi Liang.

Authors and Affiliations

  1. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China

    Lin Jiang, Runyang He, Qingqing Yang, Chanlin Yi, Dezhong Yao, Fali Li & Peng Xu

  2. School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China

    Lin Jiang, Runyang He, Qingqing Yang, Chanlin Yi, Dezhong Yao, Fali Li & Peng Xu

  3. Department of Neurology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China

    Yi Liang & Liang Yu

  4. Department of Neurology, Affiliated Hospital of University of Electronic Science and Technology of China, Chengdu, China

    Yi Liang & Liang Yu

  5. Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany

    Sarah Genon, Simon B. Eickhoff & Debo Dong

  6. Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany

    Sarah Genon & Simon B. Eickhoff

  7. School of Electrical Engineering, Zhengzhou University, Zhengzhou, China

    Dezhong Yao

  8. Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China

    Dezhong Yao, Fali Li & Peng Xu

  9. Faculty of Psychology, Southwest University, Chongqing, China

    Debo Dong

  10. Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China

    Fali Li

  11. Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China

    Peng Xu

  12. Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, China

    Peng Xu

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Contributions

P.X. conceived and designed the study. P.X., F.L., D.D. and D.Y. supervised the work. S.G., S.B.E. and D.D. contributed to the data acquisition. L.J., Y.L. and R.H. analysed and interpreted the data. L.J. and C.Y. performed the statistical analyses. Q.Y. created the figures. L.J. wrote the manuscript. P.X., F.L., D.Y., S.G., S.B.E., D.D. and L.Y. critically revised the paper for important intellectual content.

Corresponding authors

Correspondence to Dezhong Yao, Debo Dong, Fali Li or Peng Xu.

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Nature Mental Health thanks Fleur Howells, Xiao Hu and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Supplementary Results 1–3, Figs. 1–4 and Tables 1–4.

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Jiang, L., Liang, Y., Genon, S. et al. Spatial–rhythmic network as a biomarker of familial risk for psychotic bipolar disorder. Nat. Mental Health 1, 887–899 (2023). https://doi.org/10.1038/s44220-023-00143-8

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