Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks
Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks
复制标题
DOI：
10.1142/s0129065717500514
复制
发表时间：
2017-10
影响因子：
8
通讯作者：
C. Schmidt;D. Piper;Britta Pester;A. Mierau;H. Witte
中科院分区：
文献类型：
--
作者：
C. Schmidt;D. Piper;Britta Pester;A. Mierau;H. Witte
识别脑功能网络中的模块结构是获得神经信息处理的新见解的一种有前途的方法，因为模块对应于相互作用强烈增加的划定的大脑区域。在时变脑功能网络中跟踪网络模块在神经科学中还没有被普遍考虑，尽管它有可能获得对功能相互作用模式的时间演变以及相关的功能分离和整合的变化程度的理解。我们介绍了一个通用的计算框架，用于从定义的时间窗口中提取共识分区的序列加权有向边缘完整的网络，并显示了如何可以跟踪和可视化的模块结构的时间重组。该框架的一部分是一种新的方法，用于计算边缘权重阈值的模块结构质量标准的多目标优化的基础上，以及跨时间步长匹配模块的方法为个别网络。通过使用合成网络序列测试我们的框架，并将其应用于从暴露于主要平衡扰动的健康受试者的脑电图记录计算的脑功能网络，我们证明了该框架以不断发展的网络模块的形式获得对动态脑功能有意义的见解的潜力。我们的框架和它的解释推断的神经处理的精确年表有助于改善目前不完整的理解皮层的贡献，这种平衡扰动的补偿。
Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.