A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.

人类神经发育研究的一个关键目标是绘制健康和疾病的神经和行为轨迹。越来越多的开发联盟已经开始通过提供对横截面和纵向“大数据”存储库的开放访问来解决这一差距。然而，开发能够预测受试者内和受试者间神经发育变化的模型仍然具有挑战性。在这里，我们提出了绘制神经发育轨迹的两个基本要素的概念和分析视角：方差的状态和特征成分。我们专注于绘制一系列神经和行为测量的变化，并考虑发育过程中状态和性状变化的并发变化。我们提出了一个定量框架，用于结合发育过程中神经行为变异的状态和特征特定来源。具体来说，我们认为利用方差的状态和特征成分并考虑环境因素的非线性混合增长模型对于全面绘制大脑行为关系是必要的。我们在绘制幼儿期语言神经发育变化的背景下讨论这个框架，重点是功能连接的测量及其建立稳健的神经行为关系的可靠性。最终目标是通过统计数据揭示涉及个体差异和年龄相关变化的神经行为关系的发展轨迹。