A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity

  1. Masahiro Yamashita
  2. Yujiro Yoshihara
  3. Ryuichiro Hashimoto
  4. Noriaki Yahata
  5. Naho Ichikawa
  6. Yuki Sakai
  7. Takashi Yamada
  8. Noriko Matsukawa
  9. Go Okada
  10. Saori C Tanaka
  11. Kiyoto Kasai
  12. Nobumasa Kato
  13. Yasumasma Okamoto
  14. Ben Seymour  Is a corresponding author
  15. Hidehiko Takahashi  Is a corresponding author
  16. Mitsuo Kawato  Is a corresponding author
  17. Hiroshi Imamizu  Is a corresponding author
  1. Advanced Telecommunications Research Institute International, Japan
  2. Kyoto University Graduate School of Medicine, Japan
  3. Showa University, Japan
  4. The University of Tokyo, Japan
  5. Hiroshima University Graduate School of Biomedical and Health Sciences, Japan
  6. University of Cambridge, United Kingdom

Abstract

Working memory deficits are present in many neuropsychiatric diseases with diagnosis-related severity. However, it is unknown whether this common behavioral abnormality is a continuum explained by a neural mechanism shared across diseases or a set of discrete dysfunctions. Here, we performed predictive modeling to examine working memory ability (WMA) as a function of normative whole-brain connectivity across psychiatric diseases. We built a quantitative model for letter 3-back task performance in healthy participants, using resting state functional magnetic resonance imaging (rs-fMRI). This normative model was applied to independent participants (N = 965) including four psychiatric diagnoses. Individual's predicted WMA significantly correlated with a measured WMA in both healthy population and schizophrenia. Our predicted effect size estimates on WMA impairment were comparable to previous meta-analysis results. These results suggest a general association between brain connectivity and working memory ability applicable commonly to health and psychiatric diseases.

Data availability

The following dataset was generated: Yamashita, M, Yoshihara, Y, Hashimoto, R, Yahata, N, Ichikawa, N, Sakai, Y, ... Imamizu, H, 2018, Working Memory Prediction Database, https://bicr.atr.jp/dcn/en/download/database-wmp/. A download link for the open access dataset will be sent after the application form for data usage is completed (https://bicr.atr.jp/dcn/wp-content/uploads/Application_Form_for_Data_Usage_WMP-3.pdf). MATLAB code used to build the prediction model are also shared via this download link. You can send the completed application form to dcn_db@atr.jp. The Human Connectome Project 500 Subjects Release Open Access dataset is available from ConnectomeDB (https://db.humanconnectome.org/app/template/Login.vm) after the creation of a free account. Before accessing the dataset, users must agree with the Open Access Data Use Terms from ConnectomeDB (further information can be found here https://www.humanconnectome.org/study/hcp-young-adult/document/500-subjects-data-release and here https://www.humanconnectome.org/study/hcp-young-adult/data-use-terms).

Article and author information

Author details

  1. Masahiro Yamashita

    Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1520-2548
  2. Yujiro Yoshihara

    Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
  3. Ryuichiro Hashimoto

    Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9661-3412
  4. Noriaki Yahata

    Department of Youth Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
    Competing interests
    The authors declare that no competing interests exist.
  5. Naho Ichikawa

    Department of Psychiatry and Neurosciences, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
    Competing interests
    The authors declare that no competing interests exist.
  6. Yuki Sakai

    Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
  7. Takashi Yamada

    Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
  8. Noriko Matsukawa

    Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
  9. Go Okada

    Department of Psychiatry and Neurosciences, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
    Competing interests
    The authors declare that no competing interests exist.
  10. Saori C Tanaka

    Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
  11. Kiyoto Kasai

    Department of Youth Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
    Competing interests
    The authors declare that no competing interests exist.
  12. Nobumasa Kato

    Medical Institute of Developmental Disabilities Research,, Showa University, Tokyo, Japan
    Competing interests
    The authors declare that no competing interests exist.
  13. Yasumasma Okamoto

    Department of Psychiatry and Neurosciences, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
    Competing interests
    The authors declare that no competing interests exist.
  14. Ben Seymour

    Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    bjs49@cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1724-5832
  15. Hidehiko Takahashi

    Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
    For correspondence
    hidehiko@kuhp.kyoto-u.ac.jp
    Competing interests
    The authors declare that no competing interests exist.
  16. Mitsuo Kawato

    Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
    For correspondence
    kawato@atr.jp
    Competing interests
    The authors declare that no competing interests exist.
  17. Hiroshi Imamizu

    Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
    For correspondence
    imamizu@atr.jp
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1024-0051

Funding

Ministry of Education, Culture, Sports, Science, and Technology (JP18dm0307008)

  • Mitsuo Kawato

Wellcome

  • Ben Seymour

ImPACT Program of Council for Science, Technology and Innovation

  • Masahiro Yamashita
  • Mitsuo Kawato
  • Hiroshi Imamizu

JSPS KAKENHI (26120002)

  • Hiroshi Imamizu

Arthritis Research UK (21357)

  • Ben Seymour

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Michael Breakspear, QIMR Berghofer Medical Research Institute, Australia

Ethics

Human subjects: ATR dataset was acquired using protocol (#12-101) according to the Declaration of Helsinki and approved by the Ethics Committee at Advanced Telecommunication Research Institute International. All participants gave written informed consent.Data from SCZ group was acquired by study design that was approved by the Committee on Medical Ethics (#R0027) of Kyoto University and was conducted in accordance with the Code of Ethics of the World Medical Association. All participants gave written informed consent.Data from MDD group was acquired by study protocol (#E-38) that was approved by the Ethics Committee of Hiroshima University. All participants gave written informed consent.Data from OCD group was acquired by study protocol (#RBMR-C-1098-5) that was approved by the Medical Committee on Human Studies at the Kyoto Prefectural University of Medicine. All participants gave written informed consent.Data from ASD group at the University of Tokyo was acquired by study protocol (#3048 and #3150) approved by the Ethics Committee of the Graduate School of Medicine and Faculty of Medicine at the University of Tokyo. All participants gave written informed consent.Data from ASD group at Showa University was aqcuired by study protocol (#893) that was approved by Ethics Committee of the Faculty of Medicine of Showa University. All participants gave written informed consent.

Version history

  1. Received: June 1, 2018
  2. Accepted: December 8, 2018
  3. Accepted Manuscript published: December 10, 2018 (version 1)
  4. Version of Record published: January 8, 2019 (version 2)

Copyright

© 2018, Yamashita et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Masahiro Yamashita
  2. Yujiro Yoshihara
  3. Ryuichiro Hashimoto
  4. Noriaki Yahata
  5. Naho Ichikawa
  6. Yuki Sakai
  7. Takashi Yamada
  8. Noriko Matsukawa
  9. Go Okada
  10. Saori C Tanaka
  11. Kiyoto Kasai
  12. Nobumasa Kato
  13. Yasumasma Okamoto
  14. Ben Seymour
  15. Hidehiko Takahashi
  16. Mitsuo Kawato
  17. Hiroshi Imamizu
(2018)
A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity
eLife 7:e38844.
https://doi.org/10.7554/eLife.38844

Share this article

https://doi.org/10.7554/eLife.38844

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