预测3特斯拉时对静态fMRI功能连接的宏血管贡献:基于模型的方法
Xiaole Z Zhong1,2, Jonathan R Polimeni3,4,5, J Jean Chen1,2,6
1Rotman Research Institute at Baycrest, Toronto, ON, Canada.
Imaging neuroscience (Cambridge, Mass.)
|August 13, 2025
概括
这项研究表明,生物物理模型可以预测宏血管对静止状态fMRI信号的贡献,有助于纠正大血管在脑成像中的偏差.
科学领域:
- 神经成像是一种神经成像.
- 生物物理学的生物物理.
- 医学物理 医学物理
背景情况:
- 大血管信号对功能磁共振成像 (fMRI) 的空间特异性构成挑战.
- 大静脉和动脉对静止状态的BOLD-fMRI信号有很大贡献,即使在高磁场 (3T和7T) 中也是如此.
研究的目的:
- 证明使用生物物理模型来预测静止状态BOLD波动幅度 (RSFA) 和功能连接 (FC) 在3特斯拉的可行性.
- 使用无限圆柱模型和宏血管解剖网络 (宏VAN) 评估宏血管建模的准确性.
主要方法:
- 研究2D和3D无限圆柱模型和从血管图得出的宏VAN.
- 采用生物物理建模来模拟宏血管BOLD信号和功能连接.
- 评估模型在预测RSFA和FC的大静脉和动脉附近的性能.
主要成果:
- 基于宏观VAN的模型和3D无限模型可以模拟宏观的BOLD FC,而宏观VAN的性能更好.
- 生物物理模型准确地预测了靠近静脉 (R2=0.530.93) 的BOLD对对相关性,但不是动脉.
- 在静脉附近建模周脉体BOLD连接是可行的 (R2=0.080.57),精度随距离而下降.
结论:
- 生物物理建模可以模拟静止状态的宏血管BOLD信号和功能连接.
- 该方法显示了fMRI中纠正宏血管偏差的潜力,适用于休息状态和基于任务的范式.
- 精确的预测在静脉附近是可行的,这表明针对偏差校正的有针对性的应用.
关键词:
生物物理建模生物物理建模血液氧化水平依赖的fMRI.功能连接性的功能连接性静止状态波动幅度 (RSFA) 在静止状态波动幅度 (RSFA) 中.血管解剖网络 (VAN) 是一种血管偏差校正 血管偏差校正更多相关视频
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