このアイテムのアクセス数: 251

このアイテムのファイル:
ファイル 記述 サイズフォーマット 
pcn.13350.pdf492.88 kBAdobe PDF見る/開く
タイトル: Development of a classifier for gambling disorder based on functional connections between brain regions
著者: Takeuchi, Hideaki
Yahata, Noriaki
Lisi, Giuseppe
Tsurumi, Kosuke  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-5051-6978 (unconfirmed)
Yoshihara, Yujiro  kyouindb  KAKEN_id
Kawada, Ryosaku
Murao, Takuro
Mizuta, Hiroto
Yokomoto, Tatsunori
Miyagi, Takashi
Nakagami, Yukako  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-7495-8957 (unconfirmed)
Yoshioka, Toshinori
Yoshimoto, Junichiro
Kawato, Mitsuo
Murai, Toshiya  kyouindb  KAKEN_id
Morimoto, Jun  kyouindb  KAKEN_id
Takahashi, Hidehiko
著者名の別形: 竹内, 秀暁
八幡, 憲明
鶴身, 孝介
吉原, 雄二郎
川田, 良作
村尾, 託朗
水田, 弘人
横本, 竜徳
宮城, 崇史
中神, 由香子
吉岡, 利福
吉本, 潤一郎
川人, 光男
村井, 俊哉
森本, 淳
高橋, 英彦
キーワード: functional connection
gambling disorder
generalizable classifier
machine learning
発行日: Jun-2022
出版者: Wiley
Japanese Society of Psychiatry and Neurology
誌名: Psychiatry and Clinical Neurosciences
巻: 76
号: 6
開始ページ: 260
終了ページ: 267
抄録: [Aim] Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data. [Methods] As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. [Results] The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. [Conclusion] We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state.
記述: 脳機能結合の情報からギャンブル障害の判別器を開発 --人工知能技術の応用により診断や治療に新たな道!--. 京都大学プレスリリース. 2022-04-15.
著作権等: © 2022 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
URI: http://hdl.handle.net/2433/274260
DOI(出版社版): 10.1111/pcn.13350
PubMed ID: 35279904
関連リンク: https://www.kyoto-u.ac.jp/ja/research-news/2022-04-15-2
出現コレクション:学術雑誌掲載論文等

アイテムの詳細レコードを表示する

Export to RefWorks


出力フォーマット 


このアイテムは次のライセンスが設定されています: クリエイティブ・コモンズ・ライセンス Creative Commons