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Modeling Dose-Dependent Neural Processing Responses Using Mixed Effects Spline Models: with application to a PET study of ethanol

机译：使用混合效应样条模型对剂量依赖性神经处理反应进行建模：应用于乙醇的PET研究

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摘要

For functional neuroimaging studies that involve experimental stimuli measuring dose levels, e.g. of an anesthetic agent, typical statistical techniques include correlation analysis, analysis of variance or polynomial regression models. These standard approaches have limitations: correlation analysis only provides a crude estimate of the linear relationship between dose levels and brain activity; ANOVA is designed to accommodate a few specified dose levels; polynomial regression models have limited capacity to model varying patterns of association between dose levels and measured activity across the brain. These shortcomings prompt the need to develop methods that more effectively capture dose-dependent neural processing responses. We propose a class of mixed effects spline models that analyze the dose-dependent effect using either regression or smoothing splines. Our method offers flexible accommodation of different response patterns across various brain regions, controls for potential confounding factors, and accounts for subject variability in brain function. The estimates from the mixed effects spline model can be readily incorporated into secondary analyses, for instance, targeting spatial classifications of brain regions according to their modeled response profiles. The proposed spline models are also extended to incorporate interaction effects between the dose-dependent response function and other factors. We illustrate our proposed statistical methodology using data from a PET study of the effect of ethanol on brain function. A simulation study is conducted to compare the performance of the proposed mixed effects spline models and a polynomial regression model. Results show that the proposed spline models more accurately capture varying response patterns across voxels, especially at voxels with complex response shapes. Finally, the proposed spline models can be used in more general settings as a flexible modeling tool for investigating the effects of any continuous covariates on neural processing responses.
机译：对于涉及实验刺激的功能性神经影像学研究，需要测量剂量水平，例如对于麻醉剂，典型的统计技术包括相关性分析，方差分析或多项式回归模型。这些标准方法有局限性：相关分析仅提供剂量水平与大脑活动之间线性关系的粗略估计； ANOVA旨在适应一些指定的剂量水平；多项式回归模型具有有限的能力来建模剂量水平和整个大脑活动之间变化的关联模式。这些缺点促使需要开发更有效地捕获剂量依赖性神经加工反应的方法。我们提出了一类混合效应样条曲线模型，可以使用回归或平滑样条曲线分析剂量依赖性效应。我们的方法可以灵活适应各种大脑区域的不同反应模式，控制潜在的混杂因素，并说明受试者大脑功能的变异性。来自混合效果样条曲线模型的估计值可以轻松地纳入次要分析中，例如，根据大脑区域的建模响应配置文件针对大脑区域的空间分类。拟议的样条模型也得到了扩展，以纳入剂量依赖性响应函数与其他因素之间的相互作用。我们使用乙醇对脑功能的影响的PET研究数据说明了我们提出的统计方法。进行了仿真研究，以比较所提出的混合效果样条模型和多项式回归模型的性能。结果表明，提出的样条曲线模型可以更准确地捕获整个体素的变化响应模式，尤其是在具有复杂响应形状的体素上。最后，所提出的样条模型可以在更通用的设置中用作灵活的建模工具，用于研究任何连续协变量对神经处理响应的影响。

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