Multivariate network-level approach to detect interactions between large-scale functional systems.
Multivariate network-level approach to detect interactions between large-scale functional systems.
复制标题
检测大规模功能系统之间交互的多元网络级方法。
DOI：
10.1007/978-3-642-15745-5_37
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Lin,Weili
中科院分区：
文献类型：
--
作者：
Gao,Wei;Zhu,Hongtu;Giovanello,Kelly;Lin,Weili
考虑到整个大脑组织日益建立的网络结构，大规模系统如何相互作用的问题是有趣的。然而，通常使用的区域互动方法不能解决这个问题。在本文中，我们提出了一个多元网络级框架来直接量化大型功能系统之间的交互模式。该框架在三种不同的大脑状态下进行了测试，包括休息，手指敲击和看电影，使用功能连接MRI。描述了各状态下背侧注意网络（DA）、默认网络（DF）、额顶叶控制网络（FPC）、运动感觉网络（MS）和视觉网络(V)等5个预定义网络的交互模式。结果显示，在不同的状态之间，网络级的相关性发生了戏剧性的和预期的变化，强调了网络级交互对不同状态之间成功过渡的重要性。此外，我们的分析提供了FPC在网络水平上对两个对立系统- da和DF的潜在调节作用的初步证据。
The question of how large-scale systems interact with each other is intriguing given the increasingly established network structures of whole brain organization. Commonly used regional interaction approaches, however, cannot address this question. In this paper, we proposed a multivariate network-level framework to directly quantify the interaction pattern between large-scale functional systems. The proposed framework was tested on three different brain states, including resting, finger tapping and movie watching using functional connectivity MRI. The interaction patterns among five predefined networks including dorsal attention (DA), default (DF), frontal-parietal control (FPC), motor-sensory (MS) and visual (V) were delineated during each state. Results show dramatic and expected network-level correlation changes across different states underscoring the importance of network-level interactions for successful transition between different states. In addition, our analysis provides preliminary evidence of the potential regulating role of FPC on the two opposing systems-DA and DF on the network level.