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Dynamic Generative Model of the Human Brain in Resting-State

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Book cover Complex Networks & Their Applications VI (COMPLEX NETWORKS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 689))

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Abstract

The human connectome is constructed by translating neuron activities into a complex network with nodes and edges. Although the topology of such networks is well studied, the formation rules and dynamics are not fully understood. It is challenging to develop a generative model for large scale dynamic complex networks due to the computational obstacle and nontrivial network structure. To study the node-based and subject-based network dynamics in resting-state brain, we divide the brain scan session into several sliding windows and generate a network for each segment. Then, an action-based model generator, which generates edges according to a topology manipulating actions, is fitted to the network series. This model, presumably as the first applicable approach for synthesizing the brain network dynamics, is shown capable of synthesizing a network series of the action-based model. Also, the estimated parameters shows that the actions of brain regions are related to their functionality.

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Author information

Authors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Dali Guo, Viplove Arora, Enrico Amico, Joaquín Goñi & Mario Ventresca

Authors
  1. Dali Guo
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  2. Viplove Arora
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  3. Enrico Amico
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  4. Joaquín Goñi
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  5. Mario Ventresca
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Corresponding author

Correspondence to Mario Ventresca .

Editor information

Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

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Guo, D., Arora, V., Amico, E., Goñi, J., Ventresca, M. (2018). Dynamic Generative Model of the Human Brain in Resting-State. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_103

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  • DOI: https://doi.org/10.1007/978-3-319-72150-7_103

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72149-1

  • Online ISBN: 978-3-319-72150-7

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