Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data
Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data
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DOI：
10.1038/s41598-020-72013-7
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发表时间：
2020-09
影响因子：
4.6
通讯作者：
Chenzhong Yin;Xiongye Xiao;Valeriu Balaban;M. Kandel;Y. J. Lee;G. Popescu;P. Bogdan
中科院分区：
文献类型：
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作者：
Chenzhong Yin;Xiongye Xiao;Valeriu Balaban;M. Kandel;Y. J. Lee;G. Popescu;P. Bogdan
了解神经元在脑源性神经元培养中建立或抑制连接以实现交流的机制，可以了解学习、认知和创造性行为是如何出现的。虽然先前的研究表明神经元培养具有自组织临界性，但我们进一步证明了体外脑源性神经元培养表现出自我优化现象。更准确地说，我们分析了从无标记定量显微成像实验中获得的多尺度神经生长数据，并重建了体外神经元培养网络(微观尺度)和神经元培养簇网络(中尺度)。我们通过估计每个网络节点的重要性和它们的信息流来研究神经元文化网络和神经元文化簇网络的结构和演化。通过分析度中心性、贴近度和中间度中心性、节点间度分布(对神经元互联现象的影响)、聚集系数/传递性(评估“小世界”属性)和多重分形谱，我们证明了小鼠神经元随着时间的推移表现出不同于现有复杂网络模型的拓扑特征的自我优化行为。小鼠神经元之间随时间演化的相互联系优化了网络信息流、网络健壮性和自组织程度。这些发现对神经元培养建模有复杂的影响，可能还会对如何设计生物启发的人工智能产生复杂的影响。
Understanding the mechanisms by which neurons create or suppress connections to enable communication in brain-derived neuronal cultures can inform how learning, cognition and creative behavior emerge. While prior studies have shown that neuronal cultures possess self-organizing criticality properties, we further demonstrate that in vitro brain-derived neuronal cultures exhibit a self-optimization phenomenon. More precisely, we analyze the multiscale neural growth data obtained from label-free quantitative microscopic imaging experiments and reconstruct the in vitro neuronal culture networks (microscale) and neuronal culture cluster networks (mesoscale). We investigate the structure and evolution of neuronal culture networks and neuronal culture cluster networks by estimating the importance of each network node and their information flow. By analyzing the degree-, closeness-, and betweenness-centrality, the node-to-node degree distribution (informing on neuronal interconnection phenomena), the clustering coefficient/transitivity (assessing the “small-world” properties), and the multifractal spectrum, we demonstrate that murine neurons exhibit self-optimizing behavior over time with topological characteristics distinct from existing complex network models. The time-evolving interconnection among murine neurons optimizes the network information flow, network robustness, and self-organization degree. These findings have complex implications for modeling neuronal cultures and potentially on how to design biological inspired artificial intelligence.