精确估计寿命大脑轨迹的方法，区分纵向和队列效应
A recipe for accurate estimation of lifespan brain trajectories, distinguishing longitudinal and cohort effects
摘要：
我们解决的问题是估计大脑的不同部分如何在整个生命周期中发展和变化，以及这些轨迹如何受到遗传和环境因素的影响。这些寿命轨迹的估计在统计学上具有挑战性，因为它们的形状通常是高度非线性的，尽管真正的变化只能通过纵向检查来量化，但由于神经影像学研究中的随访间隔通常覆盖不到寿命的10%，因此必须使用横截面信息。纵向分析中常用的线性混合模型（LLM）和结构方程模型（SEM）依赖于通常不符合寿命数据的假设，特别是当数据由多项研究的观察结果组合而成时。虽然LVMs需要多项式函数形式的先验规范，但SEM不容易处理测量之间具有非结构化时间间隔的数据。广义添加剂混合模型（GAMM）提供了一个有吸引力的替代方案，在本文中，我们提出了各种方式制定GAMM估计寿命轨迹的12个大脑区域，使用大型纵向数据集和现实的模拟实验。我们表明，GAMM能够更准确地拟合寿命轨迹，区分纵向和横截面效应，并估计遗传和环境暴露的影响。最后，我们讨论并对比了与寿命研究相关的问题，这些问题严格要求重复测量数据，并且可以用每个参与者的单个测量来回答问题，并且在后一种情况下，简化了需要做出的假设。这些例子都附有R代码，为有兴趣使用GAMM的研究人员提供了一个教程。
英文摘要：
We address the problem of estimating how different parts of the brain develop and change throughout the lifespan, and how these trajectories are affected by genetic and environmental factors. Estimation of these lifespan trajectories is statistically challenging, since their shapes are typically highly nonlinear, and although true change can only be quantified by longitudinal examinations, as follow-up intervals in neuroimaging studies typically cover less than 10 \% of the lifespan, use of cross-sectional information is necessary. Linear mixed models (LMMs) and structural equation models (SEMs) commonly used in longitudinal analysis rely on assumptions which are typically not met with lifespan data, in particular when the data consist of observations combined from multiple studies. While LMMs require a priori specification of a polynomial functional form, SEMs do not easily handle data with unstructured time intervals between measurements. Generalized additive mixed models (GAMMs) offer an attractive alternative, and in this paper we propose various ways of formulating GAMMs for estimation of lifespan trajectories of 12 brain regions, using a large longitudinal dataset and realistic simulation experiments. We show that GAMMs are able to more accurately fit lifespan trajectories, distinguish longitudinal and cross-sectional effects, and estimate effects of genetic and environmental exposures. Finally, we discuss and contrast questions related to lifespan research which strictly require repeated measures data and questions which can be answered with a single measurement per participant, and in the latter case, which simplifying assumptions that need to be made. The examples are accompanied with R code, providing a tutorial for researchers interested in using GAMMs.
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