The convergence of maturational change and structural covariance in human cortical networks.
The convergence of maturational change and structural covariance in human cortical networks.
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DOI：
10.1523/jneurosci.3554-12.2013
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发表时间：
2013-02-13
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影响因子：
--
通讯作者：
Giedd J
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文献类型：
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作者：
Alexander-Bloch A;Raznahan A;Bullmore E;Giedd J
人类神经影像学研究一直报告在分布的大脑区域中皮质厚度或体积的大规模协方差。这种区域皮质解剖结构的群体协方差机制被假设与解剖学上相连的神经元群体的同步成熟变化有关。在同一个体中多年来以相同速率一起增长（即体积增加或减少）的大脑区域，因此预计在个体之间会表现出强烈的结构协方差或解剖学连接性。为了检验这一预测，我们使用了一个关于健康年轻人（N = 108；入组时年龄为9 - 22岁）的结构磁共振成像数据集，包括对每个参与者在6 - 12年的随访期间进行3 - 6次纵向扫描。在360个区域节点中的每一个节点，以及对于每个参与者，我们估计了i）中间扫描的皮质厚度；以及ii）连续多年扫描中皮质厚度的线性变化率。我们从这些测量值构建了结构和成熟关联矩阵及网络。结构网络和成熟网络都具有相似的全局和节点拓扑性质，以及包括模块化群落结构、数量相对较少的高度连接枢纽区域以及对短距离连接的偏向等介观特征。利用样本的一个子集（N = 32）的静息态功能磁共振成像数据，我们还证明了功能连接性和网络组织在一定程度上可由结构/成熟网络预测，但表现出对短距离连接更强的偏向以及更大的拓扑分离。大脑结构协方差网络很可能反映了分布的皮质区域中的同步发育变化。
Large-scale covariance of cortical thickness or volume in distributed brain regions has been consistently reported by human neuroimaging studies. The mechanism of this population covariance of regional cortical anatomy has been hypothetically related to synchronized maturational changes in anatomically connected neuronal populations. Brain regions that grow together, i.e., increase or decrease in volume at the same rate over the course of years in the same individual, are thus expected to demonstrate strong structural covariance or anatomical connectivity across individuals. To test this prediction, we used a structural MRI dataset on healthy young people (N = 108; aged 9–22 years at enrolment), comprising 3–6 longitudinal scans on each participant over 6–12 years of follow-up. At each of 360 regional nodes, and for each participant, we estimated i) the cortical thickness in the median scan; and ii) the linear rate of change in cortical thickness over years of serial scanning. We constructed structural and maturational association matrices and networks from these measurements. Both structural and maturational networks shared similar global and nodal topological properties, as well as mesoscopic features including a modular community structure, a relatively small number of highly connected hub regions, and a bias towards short distance connections. Using resting-state fMRI data on a subset of the sample (N = 32), we also demonstrated that functional connectivity and network organization was somewhat predictable by structural/maturational networks but demonstrated a stronger bias towards short distance connections and greater topological segregation. Brain structural covariance networks are likely to reflect synchronized developmental change in distributed cortical regions.