Identifying the Neural Bases of Math Competence Based on Structural and Functional Properties of the Human Brain
Identifying the Neural Bases of Math Competence Based on Structural and Functional Properties of the Human Brain
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DOI：
10.1162/jocn_a_02008
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发表时间：
2023-08-01
影响因子：
3.2
通讯作者：
Libertus，Melissa E.
中科院分区：
文献类型：
--
作者：
Ren，Xueying;Libertus，Melissa E.
人类在数学表现和数学学习能力上表现出很大的个体差异。早期数学技能习得对于为更高的定量技能习得和在现代社会取得成功提供基础至关重要。然而，数学能力个体差异背后的神经基础仍不清楚。现代神经成像技术不仅使我们能够识别不同的局部皮层区域，而且还可以从结构和功能上研究数学能力背后的大规模神经网络。为了深入了解数学能力的神经基础，本文综述了典型和非典型儿童和成人数学能力的结构和功能神经标记。虽然包括对儿童算术技能的讨论，但这篇综述主要关注与复杂数学技能相关的神经标记。基本的数字理解和数字比较技巧不在本复习的范围之内。综合目前的研究成果，我们得出结论：与数学能力相关的神经标记并不局限于一个特定的区域；相反，它们的特点是遍布大脑的分布和相互连接的区域网络，主要集中在额叶和顶叶皮层。鉴于人类大脑是一个复杂的网络，其组织的目的是使信息处理成本最小化，高效的大脑能够整合来自不同区域的信息，并协调大脑各区域的活动，以最大限度地提高网络的整体效率来实现目标。我们最后提出，额顶叶网络的效率对数学能力至关重要，它能够以目标导向的方式招募与任务相关的神经资源和参与分布式神经回路。因此，对于未来的研究来说，不仅要检查离散区域的大脑激活模式，还要检查整个大脑结构和功能上的分布式网络模式，这将是很重要的。
Human populations show large individual differences in math performance and math learning abilities. Early math skill acquisition is critical for providing the foundation for higher quantitative skill acquisition and succeeding in modern society. However, the neural bases underlying individual differences in math competence remain unclear. Modern neuroimaging techniques allow us to not only identify distinct local cortical regions but also investigate large-scale neural networks underlying math competence both structurally and functionally. To gain insights into the neural bases of math competence, this review provides an overview of the structural and functional neural markers for math competence in both typical and atypical populations of children and adults. Although including discussion of arithmetic skills in children, this review primarily focuses on the neural markers associated with complex math skills. Basic number comprehension and number comparison skills are outside the scope of this review. By synthesizing current research findings, we conclude that neural markers related to math competence are not confined to one particular region; rather, they are characterized by a distributed and interconnected network of regions across the brain, primarily focused on frontal and parietal cortices. Given that human brain is a complex network organized to minimize the cost of information processing, an efficient brain is capable of integrating information from different regions and coordinating the activity of various brain regions in a manner that maximizes the overall efficiency of the network to achieve the goal. We end by proposing that frontoparietal network efficiency is critical for math competence, which enables the recruitment of task-relevant neural resources and the engagement of distributed neural circuits in a goal-oriented manner. Thus, it will be important for future studies to not only examine brain activation patterns of discrete regions but also examine distributed network patterns across the brain, both structurally and functionally.