Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold.
Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold.
复制标题
DOI：
10.3389/fnins.2017.00441
复制
发表时间：
2017
影响因子：
4.3
通讯作者：
Bifone A
中科院分区：
文献类型：
--
作者：
Bordier C;Nicolini C;Bifone A
神经影像数据可以表示为节点和边缘的网络，捕获大脑连接的拓扑组织。图论提供了一个通用且强大的框架来研究这些网络及其在不同尺度上的结构。例如，社区检测方法已广泛应用于研究许多自然网络的模块化结构，包括大脑功能连接网络。稀疏化过程通常用于去除最弱的边缘（受实验噪声影响最大），并降低图的密度，从而使其在理论上和计算上更容易处理。然而，薄弱环节也可能包含重要的结构信息，确定最佳权衡的程序是积极研究的主题。在这里，我们探索使用渗透分析（一种基于统计物理学的方法）来确定大脑连接网络中社区检测的最佳稀疏阈值。通过使用具有真实模块化结构和人脑功能连接网络典型的真实拓扑特征的合成网络，我们表明渗透分析可用于确定最佳稀疏阈值，从而最大化网络社区结构的信息。我们使用广泛应用于大脑连接网络分析的三种不同的社区检测方法来验证这种方法：纽曼的模块化、InfoMap 和渐近惊喜。重要的是，我们测试了噪声和数据变异性的影响，这是确定最佳阈值的关键因素。这种数据驱动的方法在分析具有不同连接强度的人群（例如患者和对照）的大脑网络群落结构时应该特别有用。
Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks' community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman's modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.
登录
查看更多内容
DOI：
10.1523/jneurosci.1929-08.2008
发表时间：
2008-09-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Bassett DS;Bullmore E;Verchinski BA;Mattay VS;Weinberger DR;Meyer-Lindenberg A
通讯作者：
Meyer-Lindenberg A
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
DOI：
10.1088/1742-5468/2005/09/p09008
发表时间：
2005-09-01
影响因子：
2.4
作者：
Danon, L;Díaz-Guilera, A;Arenas, A
通讯作者：
Arenas, A
DOI：
10.1073/pnas.1220826110
发表时间：
2013-07-09
影响因子：
11.1
作者：
Crossley, Nicolas A.;Mechelli, Andrea;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
影响因子：
3.3
作者：
Krüger, G;Glover, GH
通讯作者：
Glover, GH