Local over-connectivity reduces the complexity of neural activity: Toward a constructive understanding of brain networks in patients with autism spectrum disorder

Koki Ichinose, Jihoon Park, Yuji Kawai, Junichi Suzuki, Minoru Asada, Hiroki Mori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The human brain has a huge number of neurons connected to each other, forming a multitude of networks; notably, such connectivity typically exhibits a small-world structure. However, the brains of persons with autism spectrum disorder (ASD) reportedly have what has been termed as 'local over-connectivity.' The neural activity of the ASD brain is also atypical; resting-state EEG signals in the ASD brain have lower complexity and enhanced power at low and high frequency oscillations. In this study, we used a small-world network model based on the model proposed by Watts and Strogatz to investigate the relationship between the degree of local over-connectivity and neural activity. We controlled the degree of local over-connectivity in the model according to the parameters laid out by Watts and Strogatz. We assessed connectivity using graph-theoretical approaches, and analyzed the complexity and frequency spectrum of the activity. We found that an ASD-like network with local over-connectivity (i.e., a high clustering coefficient and a high degree of centrality) would have excessively high power in the high frequency band, and less complexity than that of a network without local over-connectivity. This result supports the idea that local over-connectivity could underlie the characteristic brain electrical activity in persons with ASD.

Original languageEnglish
Title of host publication7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages233-238
Number of pages6
ISBN (Electronic)9781538637159
DOIs
Publication statusPublished - 2018 Apr 2
Event7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017 - Lisbon, Portugal
Duration: 2017 Sep 182017 Sep 21

Publication series

Name7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017
Volume2018-January

Other

Other7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017
CountryPortugal
CityLisbon
Period17/9/1817/9/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization
  • Developmental Neuroscience

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    Ichinose, K., Park, J., Kawai, Y., Suzuki, J., Asada, M., & Mori, H. (2018). Local over-connectivity reduces the complexity of neural activity: Toward a constructive understanding of brain networks in patients with autism spectrum disorder. In 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017 (pp. 233-238). (7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DEVLRN.2017.8329813