Dynamic reconfiguration of human brain networks during learning
Dynamic reconfiguration of human brain networks during learning
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DOI：
10.1073/pnas.1018985108
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发表时间：
2011-05-03
影响因子：
11.1
通讯作者：
Grafton, Scott T.
中科院分区：
文献类型：
--
作者：
Bassett, Danielle S.;Wymbs, Nicholas F.;Grafton, Scott T.
人类学习是一种复杂的现象，需要灵活性来适应现有的大脑功能，并精确地选择新的神经生理活动来驱动所需的行为。这两个属性灵活性和选择性必须在多个时间尺度上运作，因为技能的表现从缓慢和具有挑战性变为快速和自动。这种选择性的适应性自然是由模块化结构提供的，模块化结构在进化、发展和优化网络功能中起着关键作用。使用功能连接测量获得的大脑活动从最初的培训，通过掌握一个简单的运动技能，我们调查模块化在人类学习的作用，通过识别模块化组织跨越多个时间尺度的动态变化。我们的研究结果表明，灵活性，我们衡量的忠诚节点模块，在一个实验会话预测的相对量的学习在未来的会议。我们还开发了一个通用统计框架，用于识别不断发展的系统中的模块化架构，该框架广泛适用于网络适应性对于理解系统性能至关重要的学科。
Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes-flexibility and selection-must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.