Measuring embeddedness: Hierarchical scale-dependent information exchange efficiency of the human brain connectome.
Measuring embeddedness: Hierarchical scale-dependent information exchange efficiency of the human brain connectome.
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DOI：
10.1002/hbm.22869
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发表时间：
2015-09
影响因子：
4.8
通讯作者：
Leow A
中科院分区：
文献类型：
--
作者：
Ye AQ;Zhan L;Conrin S;GadElKarim J;Zhang A;Yang S;Feusner JD;Kumar A;Ajilore O;Leow A
本文提出了一种新的方法来理解信息交换效率和它的衰减跨层次的模块化，从本地到全球，结构人脑连接体。磁共振成像技术使我们能够将人脑的连通性作为一个图形来研究，然后可以使用图形理论方法进行分析。这些复杂的数学技术被统称为大脑连接组学，它们揭示了大脑连接组和许多网络一样是高度模块化的，因此大脑区域可以组织成社区或模块。在这里，使用tractography通知结构连接体从46个正常的健康人类受试者，我们构建了层次模块化的结构连接体使用分叉树状图。从细到粗（即，从局部到全局），我们计算了一个新度量的衰减率，该度量在层次上优先衡量同一模块中两个节点之间的信息交换。通过计算“嵌入性”-节点效率与衰减率之间的比率，人们可以探测人脑的相对尺度不变信息交换效率。结果表明，表现出高嵌入性的区域是那些包括边缘系统，默认模式网络和皮层下核。这支持了在大脑的选择区域中存在整体上接近可分解性但相对嵌入性的存在。我们确定为高度嵌入的区域在功能上各不相同，但可以说它们在记忆，情感和行为中扮演的进化角色中存在联系。
This paper presents a novel approach for understanding information exchange efficiency and its decay across hierarchies of modularity, from local to global, of the structural human brain connectome. Magnetic resonance imaging techniques have allowed us to study the human brain connectivity as a graph, which can then be analyzed using a graph-theoretical approach. Collectively termed brain connectomics, these sophisticated mathematical techniques have revealed that the brain connectome, like many networks, is highly modular and brain regions can thus be organized into communities or modules. Here, using tractography-informed structural connectomes from 46 normal healthy human subjects, we constructed the hierarchical modularity of the structural connectome using bifurcating dendrograms. Moving from fine to coarse (i.e., local to global) up the connectome's hierarchy, we computed the rate of decay of a new metric that hierarchically preferentially weighs the information exchange between two nodes in the same module. By computing “embeddedness”-the ratio between nodal efficiency and this decay rate, one could thus probe the relative scale-invariant information exchange efficiency of the human brain. Results suggest that regions that exhibit high embeddedness are those that comprise the limbic system, the default mode network, and the subcortical nuclei. This supports the presence of near-decomposability overall yet relative embeddedness in select areas of the brain. The areas we identified as highly embedded are varied in function but are arguably linked in the evolutionary role they play in memory, emotion and behavior.