Anti-Fragmentation of Resting-State Functional Magnetic Resonance Imaging Connectivity Networks with Node-Wise Thresholding.
Anti-Fragmentation of Resting-State Functional Magnetic Resonance Imaging Connectivity Networks with Node-Wise Thresholding.
复制标题
具有节点阈值的静息态功能磁共振成像连接网络的抗碎片化。
DOI：
10.1089/brain.2017.0523
复制
发表时间：
2017
影响因子：
3.4
通讯作者：
Hayasaka,Satoru
中科院分区：
文献类型：
--
作者：
Hayasaka,Satoru
基于功能磁共振成像（fMRI）的功能连接网络通常通过对节点时间过程的相关矩阵进行阈值化来构建。在被称为硬阈值化的典型阈值化方法中，将单个阈值应用于整个相关矩阵以识别表示超阈值相关性的边缘。然而，已知硬阈值产生具有不均匀边缘分配的网络，导致具有大量断开连接的节点的碎片化网络。有人建议，另一种网络阈值的方法，逐节点阈值，是能够克服这些问题。为了检验这一点，各种网络特性进行了比较网络之间的硬阈值和节点的阈值，从123名健康的年轻受试者公开的静息状态fMRI数据。结果发现，用硬阈值构造的网络包括大量断开连接的节点，而在用逐节点阈值形成的网络中没有观察到这种网络碎片。此外，在硬阈值网络中，观察到碎片化的模块组织，其特征在于大量的小模块。相反，这种模块化的碎片没有观察到节点的阈值网络，产生模块，在任何阈值和高度一致的主题。这些结果表明，逐节点阈值可能会导致更少的碎片网络。此外，逐节点阈值化使得能够鲁棒地表征网络特性，而不受阈值的选择的太大影响。
Functional magnetic resonance imaging (fMRI)-based functional connectivity networks are often constructed by thresholding a correlation matrix of nodal time courses. In a typical thresholding approach known as hard thresholding, a single threshold is applied to the entire correlation matrix to identify edges representing superthreshold correlations. However, hard thresholding is known to produce a network with uneven allocation of edges, resulting in a fragmented network with a large number of disconnected nodes. It is suggested that an alternative network thresholding approach, node-wise thresholding, is able to overcome these problems. To examine this, various network characteristics were compared between networks constructed by hard thresholding and node-wise thresholding, with publicly available resting-state fMRI data from 123 healthy young subjects. It was found that networks constructed with hard thresholding included a large number of disconnected nodes, while such network fragmentation was not observed in networks formed with node-wise thresholding. Moreover, in hard thresholding networks, fragmentized modular organization was observed, characterized by a large number of small modules. On the contrary, such modular fragmentation was not observed in node-wise thresholding networks, producing modules that were robust at any threshold and highly consistent across subjects. These results indicate that node-wise thresholding may lead to less fragmented networks. Moreover, node-wise thresholding enables robust characterization of network properties without much influence by the selection of a threshold.