Evaluating and predicting psychiatric disorders by reduction of dynamic behavioral characteristics using computational models

• Project/Area Number: 18KT0021
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Multi-year Fund
• Section: 特設分野
• Research Field: Complex Systems Disease Theory
• Research Institution: Nagoya University
• Principal Investigator: Katahira Kentaro 名古屋大学, 情報学研究科, 准教授 (60569218)
• Co-Investigator(Kenkyū-buntansha): 国里 愛彦 専修大学, 人間科学部, 准教授 (30613856) ; 山下 祐一 国立研究開発法人国立精神・神経医療研究センター, 神経研究所 疾病研究第七部, 室長 (40584131)
• Project Period (FY): 2018-07-18 – 2021-03-31
• Project Status: Completed (Fiscal Year 2020)
• Budget Amount: ¥18,460,000 (Direct Cost: ¥14,200,000、Indirect Cost: ¥4,260,000); Fiscal Year 2020: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000); Fiscal Year 2019: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000); Fiscal Year 2018: ¥7,150,000 (Direct Cost: ¥5,500,000、Indirect Cost: ¥1,650,000)
• Keywords: 計算論モデリング / 精神疾患 / オンライン実験・調査 / 予測 / 強化学習 / WEB実験・調査

Outline of Final Research Achievements

The characteristics of various psychiatric disorders have been investigated from behavioral data by using computational models that represent the computational processes behind behavior. However, previous studies have only described the correlation between the tendency of the symptoms and the characteristics of the behavior at that time, and have not sufficiently investigated whether the computational models are useful for predicting prognosis and treatment response. In the present study, we examined whether the features captured by computational models directly reflect the current state of the disease and whether they are useful in predicting prognosis. As a result, it was shown that parameters of some computational models were useful for predicting future symptoms of psychiatric disorders.

Academic Significance and Societal Importance of the Research Achievements

うつ病，統合失調症などの精神疾患には，発症を未然に予測することが有効な場合もあると考えられている。計算論的精神医学と呼ばれる新しい分野では，計算論モデルを用いて精神疾患との関連が検討されてきたが，そこでとらえられた行動の特徴が精神疾患の予測に役立つか否かは十分に検討されてこなかった。本研究はそれを検討し，簡便な行動課題のデータから推定した計算論モデルのパラメータが抑うつ傾向の変化の予測に役立つ可能性を示した。本研究の意義はこれまでの計算論的精神医学の研究を精神疾患における予測につなげるという応用の可能性を示したことと，その可能性をさらに検討するための研究の枠組みを提示したという意義がある。

Research Products

• [Journal Article] Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry (2021)
  • Author(s): Katahira Kentaro, Toyama Asako
  • Journal Title: PLOS Computational Biology Volume: 17 Issue: 2 Pages: e1008738-e1008738
  • DOI: 10.1371/journal.pcbi.1008738
  • Peer Reviewed / Open Access
• [Journal Article] Dissociation between asymmetric value updating and perseverance in human reinforcement learning (2021)
  • Author(s): Sugawara Michiyo, Katahira Kentaro
  • Journal Title: Scientific Reports Volume: 11 Issue: 1 Pages: 3574-3574
  • DOI: 10.1038/s41598-020-80593-7
  • Peer Reviewed / Open Access
• [Journal Article] Retrospective surprise: A computational component for active inference (2020)
  • Author(s): Katahira Kentaro、Kunisato Yoshihiko、Okimura Tsukasa、Yamashita Yuichi
  • Journal Title: Journal of Mathematical Psychology Volume: 96 Pages: 102347-102347
  • DOI: 10.1016/j.jmp.2020.102347
  • NAID: 120006884953
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Cognitive biases and perseverance in reinforcement learning: Does your current choice behavior depend on past “choice outcome” or “choice <i>per se</i>” ? (2019)
  • Author(s): 菅原 通代、片平 健太郎
  • Journal Title: The Japanese Journal of Psychonomic Science Volume: 38 Issue: 1 Pages: 48-55
  • DOI: 10.14947/psychono.38.5
  • NAID: 130007760486
  • ISSN: 0287-7651, 2188-7977
  • Year and Date: 2019-09-30
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] The Effect of Reduced Learning Ability on Avoidance in Psychopathy: A Computational Approach (2019)
  • Author(s): Oba Takeyuki、Katahira Kentaro、Ohira Hideki
  • Journal Title: Frontiers in Psychology Volume: 10 Pages: 1-15
  • DOI: 10.3389/fpsyg.2019.02432
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Biases in estimating the balance between model-free and model-based learning systems due to model misspecification (2019)
  • Author(s): Toyama Asako, Katahira Kentaro, Ohira Hideki
  • Journal Title: Journal of Mathematical Psychology Volume: 91 Pages: 88-102
  • DOI: 10.1016/j.jmp.2019.03.007
  • Peer Reviewed / Open Access
• [Journal Article] Reinforcement learning with parsimonious computation and a forgetting process (2019)
  • Author(s): Toyama Asako, Katahira Kentaro, Ohira Hideki
  • Journal Title: Frontiers in Human Neuroscience Volume: 13 Pages: 153-153
  • DOI: 10.3389/fnhum.2019.00153
  • Peer Reviewed / Open Access
• [Journal Article] The statistical structures of reinforcement learning with asymmetric value updates (2018)
  • Author(s): Katahira Kentaro
  • Journal Title: Journal of Mathematical Psychology Volume: 87 Pages: 31-45
  • DOI: 10.1016/j.jmp.2018.09.002
  • Peer Reviewed / Open Access
• [Presentation] Attention bias to affective outcomes (2020)
  • Author(s): Asako Toyama, Kentaro Katahira Hideki Ohira
  • Organizer: The 2020 SAS Annual Conference
  • Int'l Joint Research
• [Presentation] 行動データの計算論モデリングと認知行動療法への貢献の可能性 (2020)
  • Author(s): 片平 健太郎
  • Organizer: 日本認知・行動療法学会第46回大会
• [Presentation] The learning mechanism of shaping risk preference and relations with psychopathic traits (2019)
  • Author(s): Oba, T., Katahira, K., & Ohira, H.
  • Organizer: The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019)
  • Int'l Joint Research
• [Presentation] Forgetting Process in Model-Free and Model-Based Reinforcement Learning (2019)
  • Author(s): Toyama, A., Katahira, K., & Ohira, H.
  • Organizer: The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019)
  • Int'l Joint Research
• [Presentation] Validation of cognitive bias represented by reinforcement learning with asymmetric value updates (2019)
  • Author(s): Sugawara, M. & Katahira, K.
  • Organizer: The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019)
  • Int'l Joint Research
• [Presentation] Pseudo-Learning Rate Modulation by the Forgetting of Action Value when Environmental Volatility Changes (2019)
  • Author(s): Oshima, S. & Katahira, K.
  • Organizer: The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019)
  • Int'l Joint Research
• [Book] 計算論的精神医学 (2019)
  • Author(s): 国里 愛彦、片平 健太郎、沖村 宰、山下 祐一
  • Total Pages: 328
  • Publisher: 勁草書房
  • ISBN: 432625131X
• [Book] 行動データの計算論モデリング (2018)
  • Author(s): 片平健太郎
  • Total Pages: 224
  • Publisher: 株式会社オーム社
  • ISBN: 4274222616