Sanchez-Alonso Sara, Aslin Richard N
Haskins Laboratories, New Haven, CT, USA.
Haskins Laboratories, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Child Study Center, Yale University, New Haven, CT, USA.
Brain Lang. 2022 Jan;224:105047. doi: 10.1016/j.bandl.2021.105047. Epub 2021 Dec 8.
Understanding language neurobiology in early childhood is essential for characterizing the developmental structural and functional changes that lead to the mature adult language network. In the last two decades, the field of language neurodevelopment has received increasing attention, particularly given the rapid advances in the implementation of neuroimaging techniques and analytic approaches that allow detailed investigations into the developing brain across a variety of cognitive domains. These methodological and analytical advances hold the promise of developing early markers of language outcomes that allow diagnosis and clinical interventions at the earliest stages of development. Here, we argue that findings in language neurobiology need to be integrated within an approach that captures the dynamic nature and inherent variability that characterizes the developing brain and the interplay between behavior and (structural and functional) neural patterns. Accordingly, we describe a framework for understanding language neurobiology in early development, which minimally requires an explicit characterization of the following core domains: i) computations underlying language learning mechanisms, ii) developmental patterns of change across neural and behavioral measures, iii) environmental variables that reinforce language learning (e.g., the social context), and iv) brain maturational constraints for optimal neural plasticity, which determine the infant's sensitivity to learning from the environment. We discuss each of these domains in the context of recent behavioral and neuroimaging findings and consider the need for quantitatively modeling two main sources of variation: individual differences or trait-like patterns of variation and within-subject differences or state-like patterns of variation. The goal is to enable models that allow prediction of language outcomes from neural measures that take into account these two types of variation. Finally, we examine how future methodological approaches would benefit from the inclusion of more ecologically valid paradigms that complement and allow generalization of traditional controlled laboratory methods.
了解幼儿期的语言神经生物学对于刻画导致成熟成人语言网络的发育结构和功能变化至关重要。在过去二十年中，语言神经发育领域受到了越来越多的关注，特别是考虑到神经成像技术和分析方法的快速发展，这些技术和方法能够对跨多种认知领域的发育中的大脑进行详细研究。这些方法和分析上的进展有望开发出语言结果的早期标志物，从而在发育的最早阶段进行诊断和临床干预。在此，我们认为语言神经生物学的研究结果需要整合到一种能够捕捉发育中大脑的动态本质和固有变异性以及行为与（结构和功能）神经模式之间相互作用的方法中。因此，我们描述了一个理解早期发育中语言神经生物学的框架，该框架至少需要对以下核心领域进行明确刻画：i）语言学习机制背后的计算，ii）神经和行为测量的发育变化模式，iii）强化语言学习的环境变量（例如社会背景），以及iv）最佳神经可塑性的大脑成熟限制，这决定了婴儿从环境中学习的敏感性。我们结合最近的行为和神经成像研究结果讨论了这些领域中的每一个，并考虑了对两种主要变异来源进行定量建模的必要性：个体差异或类似特质的变异模式以及个体内部差异或类似状态的变异模式。目标是建立能够从考虑这两种变异类型的神经测量中预测语言结果的模型。最后，我们研究了未来的方法如何通过纳入更具生态效度的范式而受益，这些范式可以补充并推广传统的受控实验室方法。
