Hierarchical modularity in human brain functional networks.
Hierarchical modularity in human brain functional networks.
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DOI：
10.3389/neuro.11.037.2009
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发表时间：
2009
影响因子：
3.5
通讯作者：
Bullmore ET
中科院分区：
文献类型：
--
作者：
Meunier D;Lambiotte R;Fornito A;Ersche KD;Bullmore ET
复杂系统具有层次模块化组织的想法起源于20世纪60年代初，最近吸引了大规模现实网络定量研究的新支持。在这里，我们调查的层次模块（或“模块中的模块”）分解的人脑功能网络，在无任务或休息条件下使用功能磁共振成像测量18名健康志愿者。我们使用定制的模板来提取具有超过1800个区域节点的网络，并应用快速算法来识别多个层次的嵌套模块结构。我们使用互信息，0 < I < 1，来估计不同主题中网络的社区结构的相似性，并确定最具代表性的个体网络。结果表明，人脑功能网络具有层次模块化组织，受试者之间具有相当程度的相似性，I = 0.63。最大的五个模块在最高层次的内侧枕，外侧枕，中央，顶额和额颞系统;枕模块表现出较少的子模块组织比模块包括多模态关联皮层的区域。连接器节点和枢纽，模块间的连接的关键作用，也集中在关联皮质区。我们的结论是，方法可用于分层模块化分解的大量高分辨率的脑功能网络使用计算方便的算法。这可能使未来的调查西蒙的原始假设，层次或近分解的物理符号系统是一个关键的设计功能，为他们的快速适应不断变化的环境条件。
The idea that complex systems have a hierarchical modular organization originated in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules”) decomposition of human brain functional networks, measured using functional magnetic resonance imaging in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I = 0.63. The largest five modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.