Structural covariance networks across the life span, from 6 to 94 years of age.
Structural covariance networks across the life span, from 6 to 94 years of age.
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DOI：
10.1162/netn_a_00016
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发表时间：
2017-10-01
期刊：
影响因子：
--
通讯作者：
Spreng RN
中科院分区：
文献类型：
--
作者：
DuPre E;Spreng RN
结构协方差检验脑区之间和个体之间灰质形态的协变。尽管年龄对结构协方差模式的影响有很大的兴趣，但迄今为止还没有研究提供一个完整的寿命视角，即从儿童期到成年早期、中期和晚期，对结构协方差网络的发展进行研究。在这里，我们调查的寿命轨迹的结构协方差在六个典型的神经认知网络：默认，背注意，额顶控制，躯体运动，腹侧注意，视觉。通过结合来自五个开放获取数据源的数据，我们在1,580名参与者的样本中研究了这些网络从6岁到94岁的结构协方差轨迹。使用偏最小二乘法，我们表明，结构协方差模式在整个寿命表现出两个显着的，年龄依赖性的趋势。第一个趋势是一个稳定的模式，其完整性在整个生命周期中下降。第二个趋势是一个倒U型，将年轻人与其他年龄组区分开来。枢纽地区，包括后扣带皮层和前扣带皮层，出现特别有影响力的表达，这第二个年龄依赖性的趋势。总的来说，我们的研究结果表明，结构协方差提供了一个可靠的定义，神经认知网络的整个生命周期，并揭示了共享和网络特定的轨迹。在理解大规模神经认知网络的规范性相互作用时，寿命观点的重要性越来越明显。虽然最近的工作已经取得了重大进展，了解这些网络的功能和结构的连接性，有相对较少的关注结构协方差网络的寿命轨迹。在这项研究中，我们研究了六个神经认知网络在整个生命周期中的结构协方差模式。我们的研究结果表明，网络既表现出网络特定的稳定模式的结构协方差，以及共享的年龄相关的趋势。以前确定的枢纽地区似乎表现出强烈的影响，这些年龄相关的轨迹的表达。这些结果提供了初步的证据，多模态理解的结构协方差网络结构功能相互作用的整个生命过程。
Structural covariance examines covariation of gray matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete life span perspective—bridging childhood with early, middle, and late adulthood—on the development of structural covariance networks. Here, we investigate the life span trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open-access data sources, we examine the structural covariance trajectories of these networks from 6 to 94 years of age in a sample of 1,580 participants. Using partial least squares, we show that structural covariance patterns across the life span exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the life span. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the life span and reveal both shared and network-specific trajectories. The importance of life span perspectives is increasingly apparent in understanding normative interactions of large-scale neurocognitive networks. Although recent work has made significant strides in understanding the functional and structural connectivity of these networks, there has been comparatively little attention to life span trajectories of structural covariance networks. In this study we examine patterns of structural covariance across the life span for six neurocognitive networks. Our results suggest that networks exhibit both network-specific stable patterns of structural covariance as well as shared age-dependent trends. Previously identified hub regions seem to show a strong influence on the expression of these age-related trajectories. These results provide initial evidence for a multimodal understanding of structural covariance in network structure-function interaction across the life course.