STRUCTURAL ORGANIZATION OF FUNCTIONAL NETWORKS FROM EEG SIGNALS DURING MOTOR LEARNING TASKS
STRUCTURAL ORGANIZATION OF FUNCTIONAL NETWORKS FROM EEG SIGNALS DURING MOTOR LEARNING TASKS
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DOI：
10.1142/s0218127410026198
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发表时间：
2010-03-01
影响因子：
2.2
通讯作者：
Babiloni, Fabio
中科院分区：
文献类型：
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作者：
Fallani, Fabrizio De Vico;Baluch, Farhan;Babiloni, Fabio
脑网络拓扑特性的评估是一个新兴的研究课题，因为估计的脑连接模式通常具有相对较大的尺寸和复杂的结构。由于图是网络的数学表示，因此使用理论图方法可以简明地描述从神经成像技术估计的功能网络的拓扑特征。特别是，通过将相干性分析过程应用于高密度脑电图记录，可以开发丰富的可视化效果，为同步大脑活动变化的时空分析提供一种手段。在目前的工作中，我们研究了正常受试者执行具有策略成分的复杂视觉运动任务期间大脑同步网络的变化。特别是，我们通过计算全局和局部效率指数来评估与人类学习相关的功能网络拓扑的差异。我们的结果表明，在学习的初始阶段（这可能与最重要的认知过程有关），α频带（8-12Hz）中功能链接的特定组织往往会提高大脑网络内的通信效率。这些证据可以解释为需要制定新的战略。总的来说，这种方法使我们能够捕捉到学习过程中拓扑结构的变化，从而帮助我们更多地了解策略制定的神经相关性。我们的研究结果为理论图分析研究复杂大脑网络的有效性提供了强有力的支持。
The evaluation of the topological properties of brain networks is an emerging research topic, since the estimated cerebral connectivity patterns often have relatively large size and complex structure. Since a graph is a mathematical representation of a network, the use of a theoretical graph approach would describe concisely the topological features of the functional network estimated from neuroimaging techniques. In particular, by applying the process of coherence analysis to high-density EEG recordings, rich visualizations can be developed that provide a means for spatiotemporal analysis of changes in synchronous brain activity. In the present work, we studied the changes in brain synchronization networks during performance of a complex visuomotor task with strategic components in normal subjects. In particular, we evaluated the differences in the functional network topology associated with human learning by calculating global and local efficiency indexes. Our results suggest that during an initial period of learning, which is probably related to the most significant cognitive processes, the particular organization of functional links in the alpha frequency band (8-12Hz) tends to increase the efficiency of communication within the cerebral network. Such evidence could be interpreted as due to the need for a new strategy formulation. Overall, this approach enabled us to capture a shift in topology made during the process of learning and thus helped us to shed more light on the neural correlates of strategy formulation. Our findings provide strong support for the efficacy of theoretical graph analysis to study complex brain networks.