Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA.
Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
Sci Adv. 2024 May 31;10(22):eadk7220. doi: 10.1126/sciadv.adk7220.
Foundational mathematical abilities, acquired in early childhood, are essential for success in our technology-driven society. Yet, the neurobiological mechanisms underlying individual differences in children's mathematical abilities and learning outcomes remain largely unexplored. Leveraging one of the largest multicohort datasets from children at a pivotal stage of knowledge acquisition, we first establish a replicable mathematical ability-related imaging phenotype (MAIP). We then show that brain gene expression profiles enriched for candidate math ability-related genes, neuronal signaling, synaptic transmission, and voltage-gated potassium channel activity contributed to the MAIP. Furthermore, the similarity between MAIP gene expression signatures and brain structure, acquired before intervention, predicted learning outcomes in two independent math tutoring cohorts. These findings advance our knowledge of the interplay between neuroanatomical, transcriptomic, and molecular mechanisms underlying mathematical ability and reveal predictive biomarkers of learning. Our findings have implications for the development of personalized education and interventions.
基础数学能力是在儿童早期获得的，对于在我们这个科技驱动的社会中取得成功至关重要。然而，儿童数学能力和学习成果个体差异的神经生物学机制在很大程度上仍未得到探索。利用儿童在知识获取关键阶段的最大型多队列数据集之一，我们首先建立了可重复的与数学能力相关的影像表现型（MAIP）。然后，我们发现候选数学能力相关基因、神经元信号、突触传递和电压门控钾通道活性富集的大脑基因表达谱对 MAIP 有贡献。此外，在干预之前获得的 MAIP 基因表达特征与大脑结构之间的相似性预测了两个独立的数学辅导队列的学习成果。这些发现增进了我们对数学能力背后的神经解剖、转录组和分子机制相互作用的认识，并揭示了学习的预测性生物标志物。我们的研究结果对个性化教育和干预措施的发展具有重要意义。
