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Finding influential nodes for integration in brain networks using optimal percolation theory

Author

Listed:
  • Gino Del Ferraro

    (City College of New York)

  • Andrea Moreno

    (CSIC and UMH)

  • Byungjoon Min

    (City College of New York
    Chungbuk National University)

  • Flaviano Morone

    (City College of New York)

  • Úrsula Pérez-Ramírez

    (UPV)

  • Laura Pérez-Cervera

    (CSIC and UMH)

  • Lucas C. Parra

    (City College of New York)

  • Andrei Holodny

    (Memorial Sloan Kettering Cancer Center)

  • Santiago Canals

    (CSIC and UMH)

  • Hernán A. Makse

    (City College of New York)

Abstract

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

Suggested Citation

  • Gino Del Ferraro & Andrea Moreno & Byungjoon Min & Flaviano Morone & Úrsula Pérez-Ramírez & Laura Pérez-Cervera & Lucas C. Parra & Andrei Holodny & Santiago Canals & Hernán A. Makse, 2018. "Finding influential nodes for integration in brain networks using optimal percolation theory," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04718-3
    DOI: 10.1038/s41467-018-04718-3
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    Cited by:

    1. Wang, Dong & Small, Michael & Zhao, Yi, 2021. "Exploring the optimal network topology for spreading dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    2. Bai, Xiwen & Ma, Zhongjun & Zhou, Yaoming, 2023. "Data-driven static and dynamic resilience assessment of the global liner shipping network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    3. Liu, Xiang-Chun & Zhu, Xu-Zhen & Tian, Hui & Zhang, Zeng-Ping & Wang, Wei, 2019. "Identifying localized influential spreaders of information spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 92-97.
    4. Hou, Lei, 2022. "Network versus content: The effectiveness in identifying opinion leaders in an online social network with empirical evaluation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).

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