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

Authors :
Bin Guo
Ying Chen
Jinping Lin
Bin Huang
Xiangzhuo Bai
Chuanliang Guo
Bo Gao
Qiyong Gong
Xiangzhi Bai
Source :
Nature Communications, Vol 15, Iss 1, Pp 1-17 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723 and 91866448
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.f97334bf8f7b4dfaa918664485d267b5
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-024-53550-5