在fMRI分析中寻求对线性解决方案的无意偏见
Jeanette A Mumford1, Michael I Demidenko1, James M Bjork2
1Department of Psychology, Stanford University, Stanford, CA, United States.
Imaging neuroscience (Cambridge, Mass.)
|October 27, 2025
概括
任务功能磁共振成像 (fMRI) 模型可能会因对线性而产生偏差. 我们引入了基于对比的差异膨胀因子 (cVIF) 来量化对线性,并提出了一个和模型来减少fMRI分析中的偏差.
科学领域:
- 神经成像是一种神经成像.
- 认知神经科学 认知神经科学
- 数据分析 数据分析
背景情况:
- 任务功能磁共振成像 (fMRI) 时间序列模型中的对线性可以减少统计功率.
- 研究人员经常在数据收集后调整模型以减轻对线性,但这可能会引入参数估计偏差.
- 青少年大脑认知发展 (ABCD) 研究中的货币激励延迟 (MID) 任务数据说明了这些挑战.
研究的目的:
- 引入一种新的方法,即基于对比的差异膨胀系数 (cVIF),用于量化对线性对任务fMRI的影响.
- 在fMRI建模中评估常见的对线性减少策略的偏差效应.
- 提出一种改进的建模方法,尽量减少偏差,同时管理对线性.
主要方法:
- 开发和应用基于对比的差异膨胀系数 (cVIF) 来评估对线性.
- 模拟和分析任务fMRI数据,特别是使用ABCD研究中的MID任务.
- 将一个包含所有任务组件的"和"模型与采用对线性减少技术的模型进行比较.
主要成果:
- 任务fMRI模型中的对线性可以显著影响统计能力并引入偏差.
- 诸如回归器遗漏或使用冲动函数之类的策略可能会导致偏见的对比估计.
- 拟议的cVIF有效量化了对线性对特定任务相关措施的影响.
- 一个包括响应时间在内的"和"模型,在保持可管理的对线性水平的同时,证明了偏差减少.
结论:
- 在任务fMRI中的标准对线性减少方法可以在参数估计中引入显著的偏差.
- 基于对比度的差异膨胀系数 (cVIF) 提供了一个有价值的工具,用于评估对线性对特定对比度的影响.
- "和"模型方法为任务fMRI分析提供了更强大的方法,减轻了与对线性相关的偏差.
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