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Developmental Changes in Backbone of Brain Functional Network During the Infancy Period

机译:婴儿期大脑功能网络骨干的发育变化

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In a normal development, structure and functions of the infant’s brain change efficiently to enable one’s communication with the external world. However, it is still a mystery that how dominant connections of the brain network called “backbone” change in the infancy period. In this study developmental changes in the backbone of functional brain network are investigated. Therefore, resting state EEG of 15 infants (7 girls, full-term) with no family health problem were recorded at the ages of 6 and 18 months. Subsequently, the brain functional network was estimated from the cleaned EEG using Weighted Phase Lag Index (WPLI) algorithm. The WPLI network was then explored using the graph theory by Minimum Spanning Tree. Parameters of the MST including diameter, betweenness, leaf number, eccentricity, and hierarchy were calculated. Subsequently, a pairwise t-test was performed based on the MST parameters to compare backbone of the brain network between both age groups. Based on the Power Spectral Analysis, four frequency bands including delta, lower alpha, lower beta, and gamma bands were selected, each of them investigated separately. The results showed an increase of eccentricity and diameter, and a reduction in leaf numbers, betweenness and hierarchy (lower complexity) in the functional networks of the frequencies with enhanced power (eg. gamma). In addition, opposite results were observed in the networks related to the frequencies with declined power (eg. delta). Our findings indicate that backbone of the brain functional network changes in a frequency specific manner, and a reverse order follows the changes in oscillatory pattern.
机译:在正常的发育过程中,婴儿大脑的结构和功能会发生有效变化,从而实现与外界的交流。然而,在婴儿期大脑网络的主导连接(称为“骨干”)如何发生变化仍是一个谜。在这项研究中,研究了功能性大脑网络的骨干的发育变化。因此,在6和18个月的年龄中记录了15例婴儿(7例,足月)的无家庭健康问题的静息状态脑电图。随后,使用加权相滞后指数(WPLI)算法从清洁的脑电图估算大脑功能网络。然后使用最小生成树的图论探索WPLI网络。计算了MST的参数,包括直径,中间度,叶数,偏心率和层次。随后,基于MST参数执行成对t检验,以比较两个年龄组之间的大脑网络主干。基于功率谱分析,选择了四个频带,包括增量,较低的alpha,较低的beta和gamma频带,分别对它们进行了研究。结果表明,在具有增强功率(例如伽马)的频率的功能网络中,偏心率和直径增加,叶数,中间度和层次降低(复杂度降低)。此外,在与功率下降的频率(例如,增量)有关的网络中观察到相反的结果。我们的发现表明,大脑功能网络的骨干以频率特定的方式发生变化,而振荡模式的变化则遵循相反的顺序。

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