Weill Cornell Medical College, New York, New York, United States of America.
PLoS One. 2012;7(6):e35029. doi: 10.1371/journal.pone.0035029. Epub 2012 Jun 22.
Whole brain weighted connectivity networks were extracted from high resolution diffusion MRI data of 14 healthy volunteers. A statistically robust technique was proposed for the removal of questionable connections. Unlike most previous studies our methods are completely adapted for networks with arbitrary weights. Conventional statistics of these weighted networks were computed and found to be comparable to existing reports. After a robust fitting procedure using multiple parametric distributions it was found that the weighted node degree of our networks is best described by the normal distribution, in contrast to previous reports which have proposed heavy tailed distributions. We show that post-processing of the connectivity weights, such as thresholding, can influence the weighted degree asymptotics. The clustering coefficients were found to be distributed either as gamma or power-law distribution, depending on the formula used. We proposed a new hierarchical graph clustering approach, which revealed that the brain network is divided into a regular base-2 hierarchical tree. Connections within and across this hierarchy were found to be uncommonly ordered. The combined weight of our results supports a hierarchically ordered view of the brain, whose connections have heavy tails, but whose weighted node degrees are comparable.
从 14 名健康志愿者的高分辨率弥散 MRI 数据中提取了全脑加权连通网络。提出了一种统计上可靠的技术来去除可疑的连接。与大多数先前的研究不同，我们的方法完全适用于具有任意权重的网络。对这些加权网络进行了常规统计，发现与现有报告相当。经过使用多个参数分布的稳健拟合程序，发现我们网络的加权节点度最好由正态分布描述，与之前提出重尾分布的报告相反。我们表明，连接权重的后处理，如阈值处理，可以影响加权度渐近线。聚类系数分布要么为伽马分布，要么为幂律分布，具体取决于所使用的公式。我们提出了一种新的层次图聚类方法，该方法表明大脑网络分为一个规则的基 2 层次树。发现该层次内和层次间的连接是不同寻常的有序的。我们的结果的综合权重支持大脑的分层有序视图，其连接具有重尾，但加权节点度相当。
