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Machine-learning-based investigation of the effect of psychotropic agents on resting-state functional connectivity

Research Project

Project/Area Number 16K10233
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Research Field Psychiatric science
Research InstitutionNational Institutes for Quantum and Radiological Science and Technology

Principal Investigator

Yahata Noriaki  国立研究開発法人量子科学技術研究開発機構, 量子生命科学領域, グループリーダー(定常) (70409150)

Project Period (FY) 2016-04-01 – 2020-03-31
Project Status Completed (Fiscal Year 2019)
Budget Amount *help
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Keywords脳・神経 / 精神疾患 / 安静時脳機能顔図 / 機能的結合 / 薬理学 / 向精神薬 / 安静時脳機能画像 / 脳機能結合
Outline of Final Research Achievements

The present study aimed at establishing a machine-learning-based model that could quantitatively evaluate the effect of psychotropic agents on neuroimaging-based metrics such as interregional functional connectivity. The main results included that the established machine-learning model could successfully classify a group of mice (C57Bl/6) that had received chronic administration of antidepressant (selective serotonin reuptake inhibitor; SSRI) for four weeks from a control group (AUC~0.9). For the reliable classification, the machine learning algorithm selected a set of functional connectivity formed by the nodes in both cortical and subcortical areas such as cingulate, striatum, and association area. The similar algorithm was applied to a human data set of mood disorder patients. The derived model could classify two groups of patients with and without administration of SSRI at a level of AUC~0.7, indicating utility of the established methodology in the future biomarker development.

Academic Significance and Societal Importance of the Research Achievements

精神疾患を脳部位間の機能的な連係異常と捉え、その時空間的特徴を元に疾患のバイオマーカーを確立し、診断や治療に供する可能性に関心が集まっている。特に近年、安静状態にある脳領域間の同期状態(機能的結合)に、正常/疾患を区別する指標を見出す試みが進められている。一方、患者が治療中に服用する薬物が安静時機能的結合に影響を及ぼすことも知られており、機能的結合解析において疾患と薬物の影響を適切に切り分ける必要性があった。本研究を通し、長期薬物投与を受けるマウスの機能的結合の経時的変容について理解が深まった。今後ヒト研究へのフィードバックを通し、画像ベースの疾患バイオマーカーの精度向上への寄与が期待される。

Report

(5 results)
  • 2019 Annual Research Report   Final Research Report ( PDF )
  • 2018 Research-status Report
  • 2017 Research-status Report
  • 2016 Research-status Report
  • Research Products

    (6 results)

All 2020 2019 2018 2017 2016 Other

All Int'l Joint Research (1 results) Journal Article (1 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 1 results,  Open Access: 1 results) Presentation (4 results) (of which Int'l Joint Research: 2 results)

  • [Int'l Joint Research] Singapore Bioimaging Consortium(シンガポール)

    • Related Report
      2019 Annual Research Report
  • [Journal Article] Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis2020

    • Author(s)
      Grandjean Joanes, Chika Sato, et al.
    • Journal Title

      NeuroImage

      Volume: 205 Pages: 116278-116278

    • DOI

      10.1016/j.neuroimage.2019.116278

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Presentation] Resting-state functional-connectivity investigation of the neural substrates of psychiatric disorders2019

    • Author(s)
      Noriaki Yahata
    • Organizer
      第42回日本神経科学大会(NEURO2019)
    • Related Report
      2019 Annual Research Report
  • [Presentation] Resting-state functional-connectivity-based investigation of the neural substrates of autism spectrum disorder2018

    • Author(s)
      八幡憲明
    • Organizer
      WFSBP Asia Pacific Regional Congress of Biological Psychiatry
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research
  • [Presentation] Identification of antidepressant dose-related, resting-state functional connectivity as a novel therapeutic target in neurofeedback: a machine learning-based fMRI study2017

    • Author(s)
      Yahata N, Ichikawa N, Lisi G, Morimoto J, Okamoto Y, Kawato M.
    • Organizer
      Real-time Functional Imaging and Neurofeedback Conference 2017 (rtFIN 2017)
    • Related Report
      2017 Research-status Report
    • Int'l Joint Research
  • [Presentation] 安静時脳機能結合を通じた自閉スペクトラム症の神経基盤理解と臨床応用の可能性2016

    • Author(s)
      八幡憲明
    • Organizer
      第112回日本精神神経学会学術総会
    • Place of Presentation
      東京ベイ幕張(千葉県千葉市)
    • Related Report
      2016 Research-status Report

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Published: 2016-04-21   Modified: 2021-02-19  

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