早期灰色物质结构共差预测儿童算术能力的纵向增长
Tian Ren1,2,3, Zheng Li4,5, Chunjie Wang4,5,6
1Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China, 2020111004047@stu.hznu.edu.cn.
Developmental neuroscience
|June 6, 2023
概括
大脑结构,特别是灰质的结构共变性,支持儿童的算术发展. 像突出网络 (SN) 和正面对面网络 (FPN) 等网络内的更强的连接与改善的数学技能相关.
科学领域:
- 神经科学是一个神经科学.
- 发展心理学 发展心理学
- 认知科学 认知科学
背景情况:
- 之前关于算术发育的神经成像研究主要检查了功能性大脑活动和连接性,使大脑结构的作用未得到充分探索.
- 了解大脑结构特征如何影响数学能力的发展,对于识别潜在机制至关重要.
研究的目的:
- 调查早期灰质结构共变对儿童算术能力的后续增长的贡献.
- 探索关键大脑网络中的结构共变性模式与乘法技能的纵向变化之间的关系.
主要方法:
- 使用了63名典型发育的11至13岁儿童的纵向数据集.
- 在11岁时获得的结构磁共振成像 (MRI) 扫描,以评估与突出网络 (SN),前额-双侧网络 (FPN),运动网络 (MN) 和默认模式网络 (DMN) 相关的区域的灰质体积.
- 分析了11岁时结构共变性模式与11至13岁时算术能力增长之间的关联.
主要成果:
- 算术能力的纵向增长与SN和前/对面区域之间,以及FPN和insula之间的结构共变性增加有关.
- 相反,FPN和运动/部区域,MN和前部/运动区域以及DMN和部区域之间的结构共变性较弱与算术收益有关.
- 在初始时间点的算术收益和个别区域灰质量或行为指标之间没有发现显著的相关性.
结论:
- 灰色物质的结构共变性,而不是区域体积,在儿童时期的算术能力的纵向发展中起着特定的作用.
- 这些发现强调了网络层面的结构性组织在支持认知发展,特别是数学技能方面的重要性.
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