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因果功能调解分析与功能磁共振成像数据的应用

Yi Zhao¹, Xi Luo², Michael E Sobel³

¹Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 West 10th Street, Indianapolis, IN 46204, USA.

Biostatistics (Oxford, England)

|June 29, 2025

概括

这项研究引入了功能数据的因果调解分析,以了解大脑连接动态. 它可以在基于任务的fMRI中估计个体效应曲线,为大脑机制提供新的见解.

科学领域:

神经科学是一个神经科学.
统计统计统计统计
数据科学数据科学数据科学

背景情况:

基于任务的功能磁共振成像 (fMRI) 旨在在刺激呈现期间量化有效的大脑连接.
评估有效连接的动态变化对于理解大脑功能至关重要.
因果调解分析是探索刺激和大脑激活之间的机制的常见工具,但它对连续功能数据的应用有限.

研究的目的:

将因果调解分析扩展到功能数据,特别是在持续治疗,调解者和结果的场景中.
为分析动态大脑连接引入半参数功能线性结构方程模型.
为了能够在功能数据分析中估计单个效果曲线.

主要方法:

开发半参数函数线性结构方程模型.
讨论与功能数据调解分析相关的因果假设.
拟议模型应用于基于任务的fMRI数据.

主要成果:

该研究成功地将因果调解分析应用于功能数据.
开发的模型允许估计单个效果曲线,提供详细的见解.
对fMRI数据的应用为动态大脑连接提供了新的视角.

结论:

拟议的半参数函数线性结构方程模型将因果调解分析扩展到连续函数数据.
这种方法为研究基于任务的fMRI中的动态大脑连接提供了一种新方法.
一个R包 (cfma) 可用于实施这些新的统计模型.

关键词:

有关因果推理的推理.动态大脑连接动态大脑连接功能性数据分析数据分析.调解调解是一种调解方式.结构方程建模结构方程建模时间变化的因果轨迹.

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