Pineda-Pardo José Ángel, Martínez Kenia, Solana Ana Beatriz, Hernández-Tamames Juan Antonio, Colom Roberto, del Pozo Francisco
Laboratory of Neuroimaging, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223, Pozuelo De Alarcón, Spain,
Brain Topogr. 2015 Mar;28(2):187-96. doi: 10.1007/s10548-014-0393-3. Epub 2014 Sep 7.
Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical connections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 different brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very-large-scale integration circuits analyses, shows that functional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrangements for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal-ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organizations that can only be identified when the physical locations of the nodes are included in the analysis.
宏观脑网络已通过使用图论的多种可用指标得到广泛描述。然而，大多数分析并未纳入有关网络节点物理位置的信息。在此，我们在考虑节点物理位置的同时提供多模态宏观网络特征描述。为此，我们在20名健康受试者的样本中研究了解剖学和功能性宏观脑网络。解剖学网络通过基于图的纤维束成像算法从扩散加权磁共振图像（DW-MRI）中获得。通过DW-MRI识别的解剖学连接为确定90个不同脑区的连通性提供了概率约束。功能网络源自同一脑区的血氧水平依赖信号之间的时间线性相关性。从超大规模集成电路分析改编而来的Rendian标度分析表明，功能网络比解剖学网络更随机且优化程度更低。我们还提供了一种新的指标，可用于量化结构和功能网络的全局连通性排列。虽然功能网络显示半球间连接的贡献更高，但解剖学网络中最高的连接呈背腹排列。这些结果表明，解剖学和功能网络呈现出不同的连通性组织，只有在分析中纳入节点的物理位置时才能识别。
