Department of Statistics, Iowa State University, Ames, IA, 50011, USA.
Department of Statistics, University of California Davis, Davis, CA, 95616, USA.
Brain Struct Funct. 2019 Mar;224(2):535-551. doi: 10.1007/s00429-018-1785-z. Epub 2018 Nov 3.
The maturation of the myelinated white matter throughout childhood is a critical developmental process that underlies emerging connectivity and brain function. In response to genetic influences and neuronal activities, myelination helps establish the mature neural networks that support cognitive and behavioral skills. The emergence and refinement of brain networks, traditionally investigated using functional imaging data, can also be interrogated using longitudinal structural imaging data. However, few studies of structural network development throughout infancy and early childhood have been presented, likely owing to the sparse and irregular nature of most longitudinal neuroimaging data, which complicates dynamic analysis. Here, we overcome this limitation and investigate through concurrent correlation the co-development of white matter myelination and volume, and structural network development of white matter myelination between brain regions as a function of age, using statistically well-supported methods. We show that the concurrent correlation of white matter myelination and volume is overall positive and reaches a peak at 580 days. Brain regions are found to differ in overall magnitudes and patterns of time-varying association throughout early childhood. We introduce time-dynamic developmental networks based on temporal similarity of association patterns in the levels of myelination across brain regions. These networks reflect groups of brain regions that share similar patterns of evolving intra-regional connectivity, as evidenced by levels of myelination, are biologically interpretable and provide novel visualizations of brain development. Comparing the constructed networks between different maternal education groups, we found that children with higher and lower maternal education differ significantly in the overall magnitude of the time-dynamic correlations.
儿童时期髓鞘白质的成熟是一个关键的发育过程,它为新兴的连接和大脑功能奠定了基础。髓鞘形成响应遗传影响和神经元活动,有助于建立支持认知和行为技能的成熟神经网络。大脑网络的出现和细化,传统上使用功能成像数据进行研究,也可以使用纵向结构成像数据进行研究。然而,很少有研究报告在婴儿期和幼儿期整个期间的结构网络发育情况,这可能是由于大多数纵向神经影像学数据稀疏且不规则,这使得动态分析变得复杂。在这里,我们克服了这一限制,并使用统计学上支持的方法,通过并发相关性研究,调查了大脑区域之间的白质髓鞘和体积的共同发育,以及白质髓鞘的结构网络发育如何随年龄变化。我们发现白质髓鞘和体积的并发相关性总体上是正相关的,并在 580 天达到峰值。研究发现,在整个幼儿期,大脑区域在整体幅度和随时间变化的关联模式上存在差异。我们引入了基于大脑区域之间髓鞘水平关联模式时间相似性的时间动态发育网络。这些网络反映了具有相似的区域内连接演变模式的大脑区域组,这由髓鞘水平来证明,它们具有生物学可解释性,并提供了大脑发育的新可视化。在比较不同母亲教育群体之间构建的网络时,我们发现具有较高和较低母亲教育的儿童在时间动态相关性的整体幅度上存在显著差异。