Predicting children’s math skills from task-based and resting-state functional brain connectivity
Predicting children’s math skills from task-based and resting-state functional brain connectivity
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通过基于任务和静息状态的功能性大脑连接来预测儿童的数学技能
DOI：
10.1093/cercor/bhab476
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Price, Gavin R
中科院分区：
文献类型：
--
作者：
Lynn, Andrew;Wilkey, Eric D;Price, Gavin R
认知神经科学的一个重要目标是根据神经结构和功能预测行为，从而为谁可能从临床和/或教育干预中受益提供关键的见解。在整个发育过程中，一组分散的大脑区域之间的功能连接强度与儿童的数学技能有关。因此，在本研究中，我们使用基于连接组的预测模型来研究数字加工过程中和静止时的功能连接性是否可以预测儿童的数学技能(N= 31，MAGE= 9.21岁，14名女性)。总体而言，我们发现，符号数字比较和休息时的功能连接，而不是非符号数字比较时的功能连接，预测了儿童的数学技能。每项任务都揭示了一组基本不同的预测性连接，这些连接分布在典型的大脑网络和主要脑叶上。这些预测连接中的大多数与孩子的数学技能呈负相关，因此连接能力较弱预示着更好的数学技能。值得注意的是，这些预测性联系在很大程度上是跨任务状态不重叠的，这表明儿童的数学能力可能取决于网络隔离和/或区域专业化的状态依赖模式。此外，目前的预测建模方法超越了大脑行为的相关性，转向建立大脑连接的模型，最终可能有助于预测未来的数学技能。
A critical goal of cognitive neuroscience is to predict behavior from neural structure and function, thereby providing crucial insights into who might benefit from clinical and/or educational interventions. Across development, the strength of functional connectivity among a distributed set of brain regions is associated with children’s math skills. Therefore, in the present study we use connectome-based predictive modeling to investigate whether functional connectivity during numerical processing and at rest “predicts” children’s math skills (N= 31,Mage= 9.21 years, 14 Female). Overall, we found that functional connectivity during symbolic number comparison and rest, but not during nonsymbolic number comparison, predicts children’s math skills. Each task revealed a largely distinct set of predictive connections distributed across canonical brain networks and major brain lobes. Most of these predictive connections were negatively correlated with children’s math skills so that weaker connectivity predicted better math skills. Notably, these predictive connections were largely nonoverlapping across task states, suggesting children’s math abilities may depend on state-dependent patterns of network segregation and/or regional specialization. Furthermore, the current predictive modeling approach moves beyond brain–behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.