Zhang Dai, Xie Yanghui, Wang Longsheng, Zhou Ke
Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Medical Imaging Research Center, Anhui Medical University, Hefei, China.
NPJ Sci Learn. 2024 Sep 30;9(1):58. doi: 10.1038/s41539-024-00270-6.
Arithmetic ability is critical for daily life, academic achievement, career development, and future economic success. Individual differences in arithmetic skills among children and adolescents are related to variations in brain structures. Most existing studies have used hypothesis-driven region of interest analysis. To identify distributed brain regions related to arithmetic ability, we used data-driven cross-validated predictive models to analyze cross-sectional behavioral and structural MRI data in children and adolescents. The gray matter volume (GMV) of widespread brain regions reliably predicted arithmetic abilities measured by the Comprehensive Mathematical Abilities Test. Furthermore, we applied neuroimaging-transcriptome association analysis to explore transcriptional signatures associated with structural patterns of arithmetic ability. Structural patterns of arithmetic ability primarily correlated with transcriptional profiles enriched for genes involved in transmembrane transport and synaptic signaling. Our findings enhance our understanding of the neural and genetic mechanisms underlying children's arithmetic ability and offer a practical predictor for arithmetic skills during development.
算术能力对于日常生活、学业成就、职业发展以及未来的经济成功至关重要。儿童和青少年在算术技能方面的个体差异与脑结构的变化有关。大多数现有研究使用了假设驱动的感兴趣区域分析。为了识别与算术能力相关的分布式脑区,我们使用数据驱动的交叉验证预测模型来分析儿童和青少年的横断面行为和结构MRI数据。广泛脑区的灰质体积(GMV)可靠地预测了通过综合数学能力测试测量的算术能力。此外,我们应用神经影像-转录组关联分析来探索与算术能力结构模式相关的转录特征。算术能力的结构模式主要与参与跨膜运输和突触信号传导的基因丰富的转录谱相关。我们的研究结果增强了我们对儿童算术能力潜在的神经和遗传机制的理解,并为发育过程中的算术技能提供了一个实用的预测指标。