School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China.
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China.
Cereb Cortex. 2024 May 2;34(5). doi: 10.1093/cercor/bhae204.
The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2 years of age. Our findings highlight network-level neural substrates underlying early cognitive development.
功能大脑连接组在时间上高度动态。然而,在妊娠晚期,大脑连接组动态如何演变以及与以后的认知增长有何关联尚不清楚。在这里,我们使用 39 名胎龄为 32 至 42 周的新生儿的静息态功能磁共振成像 (MRI) 数据,研究连接组动态的成熟过程及其在预测 2 岁时神经认知结果中的作用。使用多层网络模型评估新生儿大脑动态。网络动态全局下降,但随着发育,模块性和多样性增加。区域上,随着发育,模块切换主要在前外侧中央回、内侧颞叶和皮质下区域减少,主要区域的生长速度高于关联区域。支持向量回归表明,新生儿连接组动态可预测 2 岁时的个体认知和语言能力。我们的研究结果强调了早期认知发展的网络水平神经基础。