Predicting errors from reconfiguration patterns in human brain networks
Predicting errors from reconfiguration patterns in human brain networks
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DOI：
10.1073/pnas.1207523109
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发表时间：
2012-10-09
影响因子：
11.1
通讯作者：
Fiebach, Christian J.
中科院分区：
文献类型：
--
作者：
Ekman, Matthias;Derrfuss, Jan;Fiebach, Christian J.
任务准备是一个复杂的认知过程，它实现了预期的调整，以促进未来的任务表现。人们对控制人类这一过程的定量网络参数知之甚少。通过功能磁共振成像（fMRI）和功能连接测量，我们发现，参与任务准备的大脑网络的大规模拓扑结构显示出一种引导最佳行为的动态重新配置模式。这个网络可以被分解成两个不同的拓扑结构，一个是具有错误弹性的核心，作为一个主要的枢纽，集成了网络的大部分通信，另一个是主要的感官外围，显示出更灵活的网络适应性。在任务准备过程中，核心与外围的相互作用是动态调整的。与任务相关的视觉区域与网络核心的拓扑接近度更高，其局部中心性和互联性增强。未能重新配置网络拓扑可以预测错误，这表明预期的网络重新配置对于成功的任务执行至关重要。在独特的网络解码方法的基础上，我们还开发了一个识别复杂网络特征模式的一般框架，该框架适用于将动态网络属性与行为联系起来的神经科学的其他领域。
Task preparation is a complex cognitive process that implements anticipatory adjustments to facilitate future task performance. Little is known about quantitative network parameters governing this process in humans. Using functional magnetic resonance imaging (fMRI) and functional connectivity measurements, we show that the large-scale topology of the brain network involved in task preparation shows a pattern of dynamic reconfigurations that guides optimal behavior. This network could be decomposed into two distinct topological structures, an error-resilient core acting as a major hub that integrates most of the network's communication and a predominantly sensory periphery showing more flexible network adaptations. During task preparation, core-periphery interactions were dynamically adjusted. Task-relevant visual areas showed a higher topological proximity to the network core and an enhancement in their local centrality and interconnectivity. Failure to reconfigure the network topology was predictive for errors, indicating that anticipatory network reconfigurations are crucial for successful task performance. On the basis of a unique network decoding approach, we also develop a general framework for the identification of characteristic patterns in complex networks, which is applicable to other fields in neuroscience that relate dynamic network properties to behavior.