Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs.
Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs.
复制标题
应用图论评估静态和动态大脑连接性：构建大脑图的方法
DOI：
10.1109/jproc.2018.2825200
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发表时间：
2018-05
期刊：
影响因子：
--
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Yu Q;Du Y;Chen J;Sui J;Adali T;Pearlson G;Calhoun VD
人类大脑的连接是复杂的。基于图论的分析已经成为分析脑成像数据的强大和流行的方法，主要是因为它有可能定量地阐明网络、结构和功能的静态架构、随时间推移的动态行为的组织以及与疾病相关的脑变化。创建脑图的第一步是定义连接它们的节点和边。我们回顾了定义大脑节点的多种方法，包括固定节点和数据驱动节点。扩展大多数研究的狭隘观点，专注于静态和/或单一模态的大脑连接，我们还调查了先进的方法和他们的表现，在建立动态和多模态脑图。我们展示了来自健康对照组和精神疾病患者的模拟和真实的数据的结果。我们概述了这些不同技术的优势和挑战。本文通过总结和考察近年来基于图论的脑成像数据分析研究，为开发新的强大工具来探索复杂的脑网络提供了指导。
Human brain connectivity is complex. Graph-theorybased analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multimodal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesses. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this paper provides a guide for developing new powerful tools to explore complex brain networks.
影响因子：
2.9
作者：
He Y;Evans A
通讯作者：
Evans A
影响因子：
3.7
作者：
Hayasaka S;Hugenschmidt CE;Laurienti PJ
通讯作者：
Laurienti PJ