ArticleScientific reports2020
Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Article
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- Effective methods and framework for energy-based local learning of deep neural networks.Frontiers in artificial intelligence · 2025Article
- Combined topological and spatial constraints are required to capture the structure of neural connectomes.Network neuroscience (Cambridge, Mass.) · 2025Article
- Target analysis and identification of curcumin against vascular calcification.Scientific reports · 2024Article
- Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review-part I.Journal of biomedical optics · 2024Review
- From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation.Communications biology · 2024Review
- Roadmap on Label-Free Super-Resolution Imaging.Laser & photonics reviews · 2023Article
- A unified approach of detecting phase transition in time-varying complex networks.Scientific reports · 2023Article
- Network bypasses sustain complexity.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Decoding the underlying mechanisms of Di-Tan-Decoction in treating intracerebral hemorrhage based on network pharmacology.BMC complementary medicine and therapies · 2023Article
- Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity.PloS one · 2023Article
- Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.Molecules (Basel, Switzerland) · 2022Article
- 3D Reconstruction of cellular images from microfabricated imagers using fully-adaptive deep neural networks.Scientific reports · 2022Article
- Adding links on minimum degree and longest distance strategies for improving network robustness and efficiency.PloS one · 2022Article
- Deciphering the generating rules and functionalities of complex networks.Scientific reports · 2021Article
- Principled network extraction from images.Royal Society open science · 2021Article
- Spatial light interference microscopy: principle and applications to biomedicine.Advances in optics and photonics · 2021Article
- Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena.Scientific reports · 2021Article
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7 authors.
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Abstract
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.
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