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Self-supervised learning for accurately modelling hierarchical evolutionary patterns of cerebrovasculature

Author

Listed:
  • Bin Guo

    (West China Xiamen Hospital of Sichuan University
    Beihang University)

  • Ying Chen

    (Beihang University
    Klinikum der Universität München, Ludwig-Maximilians University Munich)

  • Jinping Lin

    (West China Xiamen Hospital of Sichuan University)

  • Bin Huang

    (Affiliated Hospital of Guizhou Medical University)

  • Xiangzhuo Bai

    (Zhongxiang Hospital of Traditional Chinese Medicine)

  • Chuanliang Guo

    (Datian General Hospital)

  • Bo Gao

    (Affiliated Hospital of Guizhou Medical University)

  • Qiyong Gong

    (West China Xiamen Hospital of Sichuan University
    West China Hospital of Sichuan University
    West China Hospital of Sichuan University
    Chinese Academy of Medical Sciences)

  • Xiangzhi Bai

    (Beihang University
    Beihang University
    Beihang University)

Abstract

Cerebrovascular abnormalities are critical indicators of stroke and neurodegenerative diseases like Alzheimer’s disease (AD). Understanding the normal evolution of brain vessels is essential for detecting early deviations and enabling timely interventions. Here, for the first time, we proposed a pipeline exploring the joint evolution of cortical volumes (CVs) and arterial volumes (AVs) in a large cohort of 2841 individuals. Using advanced deep learning for vessel segmentation, we built normative models of CVs and AVs across spatially hierarchical brain regions. We found that while AVs generally decline with age, distinct trends appear in regions like the circle of Willis. Comparing healthy individuals with those affected by AD or stroke, we identified significant reductions in both CVs and AVs, wherein patients with AD showing the most severe impact. Our findings reveal gender-specific effects and provide critical insights into how these conditions alter brain structure, potentially guiding future clinical assessments and interventions.

Suggested Citation

  • Bin Guo & Ying Chen & Jinping Lin & Bin Huang & Xiangzhuo Bai & Chuanliang Guo & Bo Gao & Qiyong Gong & Xiangzhi Bai, 2024. "Self-supervised learning for accurately modelling hierarchical evolutionary patterns of cerebrovasculature," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53550-5
    DOI: 10.1038/s41467-024-53550-5
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    1. Shanshan Wang & Cheng Li & Rongpin Wang & Zaiyi Liu & Meiyun Wang & Hongna Tan & Yaping Wu & Xinfeng Liu & Hui Sun & Rui Yang & Xin Liu & Jie Chen & Huihui Zhou & Ismail Ayed & Hairong Zheng, 2021. "Annotation-efficient deep learning for automatic medical image segmentation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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