Hu Derek K, Goetz Parker W, To Phuc D, Garner Cristal, Magers Amber L, Skora Clare, Tran Nhi, Yuen Tammy, Hussain Shaun A, Shrey Daniel W, Lopour Beth A
Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.
Division of Neurology, Children's Hospital Orange County, Orange, CA, United States.
Front Netw Physiol. 2022 Jun 15;2:893826. doi: 10.3389/fnetp.2022.893826. eCollection 2022.
During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.
在正常儿童发育过程中，功能性脑网络会随着时间的推移与神经元振荡的变化同步发展。先前的研究已经证明了网络拓扑结构随年龄的差异，特别是在新生儿以及从出生到成年早期的队列中。在此，我们评估脑电图（EEG）功能连接的发育变化，特别关注生命的头两年。使用基于互相关的测量方法和加权相位滞后指数，从240名0至2岁健康婴儿清醒和睡眠期间的脑电图中计算功能连接网络（FCN）。评估了拓扑特征，包括网络强度、全局聚类系数、特征路径长度和小世界度量。我们发现互相关FCN保持一致的小世界结构，并且在婴儿出生后的前3个月后连接强度增加。这些网络中最强的连接在各年龄组中始终位于额叶和枕叶区域。在δ和θ波段，加权相位滞后指数网络在清醒和睡眠状态下，在出生后的前3个月后强度下降，并且在清醒状态下的α和β波段也发现了类似结果。然而，在睡眠状态下的α波段，FCN强度随年龄显著增加，特别是在21至24个月龄组。在此期间，网络中大多数最强的连接位于额中央区域，并且在3个月以上的受试者睡眠期间的β波段也观察到定性相似的分布。图论分析表明加权相位滞后指数网络具有小世界结构，但程度低于使用互相关计算的网络。总体而言，图论指标随时间变化不大，聚类系数（清醒和睡眠）、特征路径长度（睡眠）和小世界度量（睡眠）在年龄组之间没有显著差异。这些结果表明，婴儿FCN在头两年中不断发展，网络强度的变化比网络结构特征更为显著。本研究量化了婴儿发育期间的正常脑网络，可为未来健康和神经疾病研究提供基线。
