4.7 Article

The value of arterial spin labelling perfusion MRI in brain age prediction

期刊

HUMAN BRAIN MAPPING
卷 -, 期 -, 页码 -

出版社

WILEY
DOI: 10.1002/hbm.26242

关键词

ageing; ASL; brain age; cerebral perfusion; cerebrovascular health; machine learning

向作者/读者索取更多资源

This study investigated the optimal combination of structural and physiological MRI features and algorithms for brain age prediction. The addition of ASL features to structural brain age, combined with the ElasticNetCV algorithm, improved brain age prediction the most and performed best in a cross-sectional and repeatability comparison.
Current structural MRI-based brain age estimates and their difference from chronological age-the brain age gap (BAG)-are limited to late-stage pathological brain-tissue changes. The addition of physiological MRI features may detect early-stage pathological brain alterations and improve brain age prediction. This study investigated the optimal combination of structural and physiological arterial spin labelling (ASL) image features and algorithms. Healthy participants (n = 341, age 59.7 +/- 14.8 years) were scanned at baseline and after 1.7 +/- 0.5 years follow-up (n = 248, mean age 62.4 +/- 13.3 years). From 3 T MRI, structural (T1w and FLAIR) volumetric ROI and physiological (ASL) cerebral blood flow (CBF) and spatial coefficient of variation ROI features were constructed. Multiple combinations of features and machine learning algorithms were evaluated using the Mean Absolute Error (MAE). From the best model, longitudinal BAG repeatability and feature importance were assessed. The ElasticNetCV algorithm using T1w + FLAIR+ASL performed best (MAE = 5.0 +/- 0.3 years), and better compared with using T1w + FLAIR (MAE = 6.0 +/- 0.4 years, p < .01). The three most important features were, in descending order, GM CBF, GM/ICV, and WM CBF. Average baseline and follow-up BAGs were similar (-1.5 +/- 6.3 and - 1.1 +/- 6.4 years respectively, ICC = 0.85, 95% CI: 0.8-0.9, p = .16). The addition of ASL features to structural brain age, combined with the ElasticNetCV algorithm, improved brain age prediction the most, and performed best in a cross-sectional and repeatability comparison. These findings encourage future studies to explore the value of ASL in brain age in various pathologies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据