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BOLDsωimsuite:一种新的软件套件,用于前向建模BOLD fMRI信号

Jacob Chaussé^1,2, Avery J L Berman^3,4, J Jean Chen^1,5,6

¹Rotman Research Institute, Baycrest, Toronto, Canada.

Imaging neuroscience (Cambridge, Mass.)

|August 13, 2025

概括

本研究介绍了BOLDsωimsuite,这是一个Python工具箱,简化了功能性MRI (fMRI) 的血液氧气水平依赖 (BOLD) 效应前建模. 它降低了BOLD模拟的进入障碍,并促进了可重复的科学.

科学领域:

磁共振成像 (MRI) 是一种磁共振成像技术.
神经成像是一种神经成像.
计算神经科学是一种神经科学.

背景情况:

对血液氧化水平依赖 (BOLD) 效应的前期建模对于理解功能性MRI (fMRI) 机制和定量MRI (qMRI) 是至关重要的.
现有的模拟方法可能很复杂,对研究人员来说是一个很大的进入障碍.
缺乏标准化,可访问的工具阻碍了探索BOLD信号的复杂性和方法选择.

研究的目的:

为了介绍BOLDsωimsuite,一个开源的Python工具箱用于BOLD效应前向建模.
将主要的BOLD模拟方法整合到一个单一,连贯和灵活的包裹中.
减少方法障碍,促进fMRI中的可重复性研究.

主要方法:

开发一个模块化的Python包 (BOLDsωimsuite),集成各种BOLD前建模技术.
实施模拟脚本,允许模块化组件灵活组合.
包括理论解释,样本模拟和计算要求建议.

主要成果:

BOLDsωimsuite为各种不同的BOLD前建模方法提供了一个统一的平台.
该工具箱通过其基于组件的设计,在方法选择方面提供了灵活性.
样本模拟证明了该包对于分析和学习的实用性.

结论:

BOLDsωimsuite显著降低了fMRI中BOLD效应前建模的进入障碍.
该软件是MR物理学的宝贵学习工具,也是可重复的科学研究平台.
该工具箱的开源和可适应性鼓励进一步开发和应用在各种MRI研究领域.

关键词:

大胆的fMRI.蒙特卡罗模拟的蒙特卡罗模拟在这里,Python是Python.生物物理建模生物物理建模校准的fMRI进行校准.扩散扩散是一种扩散.预期建模的前性建模梯度-回声信号的信号.铁制造地图铁制造地图定量的MRI是指MRI的数量.旋回回声信号的回声信号静态脱相处理静态脱相处理血管指纹的指纹检测

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