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.

翻译：我们处理的问题是估计大脑不同部分在整个寿命期中如何发展和变化,以及这些过程如何受到遗传和环境因素的影响。对这些寿命期轨迹的估算在统计上具有挑战性,因为这些寿命期轨迹在统计上具有挑战性,因为其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加以量化,因为神经成形研究中的后续间隔通常涵盖寿命期不到10 ⁇,使用跨部门信息是必要的。在长期分析中常用的线性混合模型和结构方程模型(SEM)依赖于通常与寿命期计量数据不相符的假设,特别是当数据由多种研究的观测组合在一起时。虽然LMMM的形状通常非常非线性,而真正的变化只能通过纵向检查来量化,但SEM不易处理数据,而测量时间间隔不固定。通用混合模型(GAMMMs)提供了一种有吸引力的替代方法,在估算12个大脑区域的寿命期轨迹轨迹时,我们可以提出各种感兴趣的解释方式,使用大型的纵向数据设置和现实性数据模型,我们需要精确地进行长期的风险评估和模拟。我们用GMMMML的模型来进行精确地讨论。