Modeling dose-dependent neural processing responses using mixed effects spline models: with application to a PET study of ethanol.
Modeling dose-dependent neural processing responses using mixed effects spline models: with application to a PET study of ethanol.
复制标题
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。
DOI：
10.1016/j.neuroimage.2007.09.074
复制
发表时间：
2008
期刊：
影响因子：
5.7
通讯作者：
Bowman,FDuBois
中科院分区：
文献类型：
--
作者：
Guo,Ying;Bowman,FDuBois
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.
