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.

中文翻译：

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准确估计大脑寿命轨迹、区分纵向和队列效应的方法

我们解决了估计大脑不同部分在整个生命周期中如何发育和变化的问题，以及这些轨迹如何受到遗传和环境因素的影响。估计这些寿命轨迹在统计上具有挑战性，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量化，因为神经影像学研究中的随访间隔通常覆盖不到 10% 的寿命，使用交叉- 部门信息是必要的。纵向分析中常用的线性混合模型 (LMM) 和结构方程模型 (SEM) 依赖于寿命数据通常无法满足的假设，特别是当数据由多项研究的观察结果组成时。虽然 LMM 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。广义加性混合模型 (GAMM) 提供了一种有吸引力的替代方案，在本文中，我们提出了各种方法来制定 GAMM，以使用大型纵向数据集和真实的模拟实验来估计 12 个大脑区域的寿命轨迹。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，我们讨论和对比与严格要求重复测量数据的寿命研究相关的问题和每个参与者可以通过单个测量回答的问题，在后一种情况下，这简化了需要做出的假设。这些示例随附 R 代码，为对使用 GAMM 感兴趣的研究人员提供了教程。