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

Bin Guo, Ying Chen, Jinping Lin, Bin Huang, Xiangzhuo Bai, Chuanliang Guo, Bo Gao, Qiyong Gong () and Xiangzhi Bai ()
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Bin Guo: West China Xiamen Hospital of Sichuan University
Ying Chen: Beihang University
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
Xiangzhi Bai: Beihang University

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-53550-5

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