脑血管大脑年龄的多队列,多序列协调
Mathijs B J Dijsselhof1,2, Candace Moore3, Saba Amiri3
1Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands.
Imaging neuroscience (Cambridge, Mass.)
|October 30, 2025
概括
动脉旋转标记 (ASL) perfusion MRI 的数据协调改善了脑血管大脑年龄的预测. 像AutoComBat这样的ASL特定方法为大脑衰老研究提供了更好的跨研究一致性.
科学领域:
- 神经成像是一种神经成像.
- 放射学 放射学是一门学科.
- 老年学是一门学科.
背景情况:
- 由解剖扫描得出的更高的大脑预测年龄差距 (BAG) 与老年人的认知能力下降相关.
- 整合动脉旋转标记 (ASL) perfusion MRI 增加了脑血管成分,可能提高 BAG 预测和对心血管健康对大脑衰老的影响敏感度.
研究的目的:
- 研究数据协调方法,以提高脑血管大脑年龄估计的概括性,使用ASLMRI.
- 为了比较各种协调技术在多队列,多序列ASL和结构MRI数据上的性能.
主要方法:
- 使用了多项研究数据集 (n=2608) 与健康参与者和多个测试队伍的T1加权 (T1w),FLAIR和ASLMRI数据.
- 提取的特征包括灰色/白质体积 (T1w),白质超强度 (FLAIR) 和ASL脑血流 (CBF) 以及其空间变化系数 (sCoV).
- 在训练基于ExtraTrees的大脑年龄模型 (仅ASL和T1w+FLAIR+ASL) 之前,将几个协调方法 (NeuroComBat,CovBat,NeuroHarmonize,OPNested ComBat,AutoComBat,RELIEF) 应用于ASL特征.
主要成果:
- 调整显著减少了ASL在所有方法中的队列之间的特征差异,特别是在RELIEF.
- 仅使用ASL的模型在协调后显示了改善的平均绝对误差 (MAE) (从11.1±7.5到<8.8±6.2年),AutoComBat表现名义上最好.
- 多模式模型 (T1w+FLAIR+ASL) 显示,与仅ASL协调相比,改进较小,但ASL特定的方法,如AutoComBat,表现更好.
结论:
- 对ASL特征的协调提高了研究的一致性,并改善了大脑年龄的估计,特别是在仅使用ASL数据时.
- 建议采用ASL特定的参数协调方法,考虑获取参数,以便在多队列研究中获得最佳结果.
- 这些发现支持使用ASL参数特定协调来研究心血管健康,大脑衰老和认知衰退之间的联系.
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