基于多模式成像的脑血流预测模型在模拟微重力中的发展.
Linkun Cai1, Yawen Liu2, Kai Li3
1School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.
Cyborg and bionic systems (Washington, D.C.)
|November 26, 2025
概括
机器学习模型在模拟微重力时预测大脑血流 (CBF) 的变化. 这些模型可以帮助在太空飞行期间监测宇航员的大脑健康.
科学领域:
- 神经科学是一个神经科学.
- 太空医学 太空医学
- 生物医学工程 生物医学工程
背景情况:
- 大脑血流 (CBF) 的改变与认知衰退和神经退行有关.
- 保持足够的CBF对于长时间太空飞行期间宇航员大脑健康至关重要.
- 在微重力条件下进行CBF定量评估存在重大挑战.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测模拟微重力下的CBF变化.
- 为了研究内动脉多普勒超声波和脑MRI数据之间的关系,用于CBF映射.
- 为在太空中监测宇航员大脑健康创造一个实用的工具.
主要方法:
- 一个90天的头向下倾斜床休息 (HDTBR) 协议模拟了微重力条件.
- 收集了多式成像数据,包括多普勒超声波和脑MRI.
- 用各种ML算法来构建区域CBF的预测模型.
主要成果:
- 在90天的HDTBR后,在特定的大脑区域观察到显著的区域CBF下降.
- CatBoost ML模型在受影响地区显示了CBF的高预测准确性 (AUC从0.82到0.92).
- 该预测模型已成功部署为交互式Web应用程序,用于潜在的轨道使用.
结论:
- 基于机器学习的CBF预测模型可以使用模拟微重力下的多式成像数据进行可行构建.
- 这些模型显示了在太空任务期间监测宇航员大脑健康的早期预警系统的潜力.
- 开发的应用程序为轨道CBF监控提供了一个实际的解决方案.
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