Clinical Neuroimaging Department, Cuban Neuroscience Center, Cuba.
Neuroimage. 2010 May 1;50(4):1497-510. doi: 10.1016/j.neuroimage.2010.01.028. Epub 2010 Jan 18.
Recently, a related morphometry-based connection concept has been introduced using local mean cortical thickness and volume to study the underlying complex architecture of the brain networks. In this article, the surface area is employed as a morphometric descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in surface area between pair of regions was measured by computing the partial correlation coefficient across 186 normal subjects of the Cuban Human Brain Mapping Project. We demonstrated that connectivity matrices obtained follow a small-world behavior for two different parcellations of the brain gray matter. The properties of the connectivity matrices were compared to the matrices obtained using the mean cortical thickness for the same cortical parcellations. The topology of the cortical thickness and surface area networks were statistically different, demonstrating that both capture distinct properties of the interaction or different aspects of the same interaction (mechanical, anatomical, chemical, etc.) between brain structures. This finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin. To our knowledge, this is the first time that surface area is used to study the morphological connectivity of brain networks.
最近，一种基于相关形态测量的连接概念被引入，使用局部平均皮质厚度和体积来研究大脑网络的潜在复杂结构。在本文中，表面积被用作形态测量描述符，以研究大脑结构之间的并发变化，并构建二进制连接图。通过在 186 名古巴人类大脑映射项目的正常受试者中计算跨区域的偏相关系数，测量了两个区域之间表面积的统计相似性。我们证明，对于大脑灰质的两种不同分割，获得的连接矩阵遵循小世界行为。比较了使用相同皮质分割的皮质厚度获得的连接矩阵的性质。皮质厚度和表面积网络的拓扑结构在统计学上存在差异，这表明它们都捕获了大脑结构之间相互作用的不同特性（机械、解剖、化学等）或同一相互作用的不同方面。这一发现可以解释为，由于每个描述符是由不同的遗传起源导致的不同的细胞机制驱动的。据我们所知，这是首次使用表面积来研究大脑网络的形态连通性。
