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Dynamical Characteristics of State Transition Defined by Neural Activity of Phase in Alzheimer’s Disease

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Neural Information Processing (ICONIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1517))

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Abstract

In recent findings of dynamical functional connectivity (dFC) as the degree of variability for functional connectivity, the dFC reflects the abilities and deficits in cognitive functions. Recently, we introduced the instantaneous phase difference between electroencephalography (EEG) signals (called the dynamical phase synchronization (DPS) approach) and succeeded in detecting moment-to-moment dFC dynamics. In this approach, neural interactions in whole-brain activity are decomposed into phase differences of pairwise brain regions. From the viewpoint of “emergence” in complex systems where interactions among several components produce additional functions, an integrated analysis of interactions in a whole-brain network without separating each interaction in pairwise brain regions might lead to new understanding of cognitive functions. Alzheimer’s disease (AD) involves cognitive impairments due to the loss of multiple neural interactions and affects dFC. We hypothesized that instantaneous phase dynamics without decomposing to pairwise instantaneous phase differences would bring another dimension of understanding of alternations of dFC regarding cognitive impairment in AD. To prove this hypothesis, we introduced dynamic states based on instantaneous frequency distribution across the whole brain. Upon applying this method to EEG signals of healthy controls (HC) and subjects with AD, the results showed that the state of the occipital leading phase in the AD group was more difficult to maintain. Moreover, the degree of maintenance of this state has a relatively high correlation with cognitive function in AD. In conclusion, dynamic states based on whole-brain instantaneous frequency distribution might be an additional approach to reveal different aspects of dFC among the other approaches.

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Acknowledgment

This work of Sou Nobukawa was supported by a research grant from the Okawa Foundation and JSPS KAKENHI for Early-Career Scientists under Grant 18K18124.

Author information

Authors and Affiliations

  1. Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275–0016, Japan

    Sou Nobukawa

  2. Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan

    Sou Nobukawa

  3. Research Center for Child Mental Development, Kanazawa University, Ishikawa, Japan

    Takashi Ikeda, Mitsuru Kikuchi & Tetsuya Takahashi

  4. Department of Psychiatry and Behavioral Science, Kanazawa University, Ishikawa, Japan

    Mitsuru Kikuchi

  5. Department of Neuropsychiatry, University of Fukui, Fukui, Japan

    Tetsuya Takahashi

  6. Uozu Shinkei Sanatorium, Toyama, Japan

    Tetsuya Takahashi

Authors
  1. Sou Nobukawa
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  2. Takashi Ikeda
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  3. Mitsuru Kikuchi
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  4. Tetsuya Takahashi
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Corresponding author

Correspondence to Sou Nobukawa .

Editor information

Editors and Affiliations

  1. Sampoerna University, Jakarta, Indonesia

    Teddy Mantoro

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  3. Sampoerna University, Jakarta, Indonesia

    Media Anugerah Ayu

  4. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  5. Universitas Indonesia, Depok, Indonesia

    Achmad Nizar Hidayanto

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Nobukawa, S., Ikeda, T., Kikuchi, M., Takahashi, T. (2021). Dynamical Characteristics of State Transition Defined by Neural Activity of Phase in Alzheimer’s Disease. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-92310-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92309-9

  • Online ISBN: 978-3-030-92310-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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