Faculty of Applied Science, Vavuniya Campus of the University of Jaffna, Vavuniya, Sri Lanka.
Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka.
Brain Connect. 2020 Aug;10(6):316-327. doi: 10.1089/brain.2020.0747. Epub 2020 Jul 10.
The nodal brain network measures (e.g., centrality measures) are defined for a single node and the global network measures (e.g., global efficiency) are defined for the whole brain in the literature. But a meaningful group of nodes will be benefited from a formulation that applies to a group of nodes rather than a single node or the whole brain. The question such as "which brain lobe is more structurally central in the older-adult brain?" could be answered to some extent by the application of a centrality measure that applied to the group of nodes from each lobe. In the brain asymmetric studies, path-based global measures were applied to the left and right hemispherical networks separately, considering only intrahemispheric edges. However, for a valid comparison, such global measures should include the interhemispheric edges as well. This problem can be solved by considering both hemispherical nodes as two groups in one network. Novel definitions for group nodes network measures are presented in this study, to solve a number of such group-context problems in the brain networks analysis. We apply the group measures to the structural connectomes of older adults and Alzheimer's disease (AD) subjects based on the brain lobes and hemispherical groups to demonstrate the effectiveness of the proposed measures. The temporal and parietal lobes are the most central lobes in older adults and AD, but the strength of these lobes has been heavily affected in AD. However, the rewiring of the AD brain preserves the paths for communication between other regions through these lobes. Leftward efficiency revealed in older adults and the asymmetry disappeared in the rewired AD. We prove that the concepts of group network measures have the potential to solve a number of such group-context problems in the brain networks analysis and the group network measures change the way of analyzing brain networks.
节点脑网络度量（例如，中心性度量）是针对单个节点定义的，而全局网络度量（例如，全局效率）是针对整个大脑定义的。但是，对于一组有意义的节点，将适用于一组节点而不是单个节点或整个大脑的公式将受益。通过应用适用于每个脑叶节点组的中心性度量，可以在某种程度上回答“在老年人大脑中哪个脑叶在结构上更具中心性？”等问题。在大脑不对称性研究中，路径为基础的全局度量分别应用于左半球和右半球网络，仅考虑半球内的边缘。然而，为了进行有效的比较，这些全局度量应该包括半球间的边缘。这个问题可以通过将两个半球的节点视为一个网络中的两个组来解决。本研究提出了用于组节点网络度量的新定义，以解决大脑网络分析中许多此类组上下文问题。我们根据大脑叶和半球组将组度量应用于老年人和阿尔茨海默病（AD）患者的结构连接组，以证明所提出度量的有效性。在老年人和 AD 中，颞叶和顶叶是最具中心性的脑叶，但这些脑叶的强度在 AD 中受到了严重影响。然而，AD 大脑的重新布线通过这些脑叶保留了区域之间的通信路径。在老年人中发现的左向效率和在重新布线的 AD 中消失的不对称性。我们证明，组网络度量的概念具有解决大脑网络分析中许多此类组上下文问题的潜力，并且组网络度量改变了分析大脑网络的方式。
