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Generalizable brain network markers of major depressive disorder across multiple imaging sites.
https://repo.qst.go.jp/records/81690
https://repo.qst.go.jp/records/81690d564b8a4-ee6f-4833-a967-e0744641c7e1
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2021-01-18 | |||||
| タイトル | ||||||
| タイトル | Generalizable brain network markers of major depressive disorder across multiple imaging sites. | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Yamashita, Ayumu
× Yamashita, Ayumu× Sakai, Yuki× Yamada, Takashi× Yahata, Noriaki× Kunimatsu, Akira× Okada, Naohiro× Itahashi, Takashi× Hashimoto, Ryuichiro× Mizuta, Hiroto× Ichikawa, Naho× Takamura, Masahiro× Okada, Go× Yamagata, Hirotaka× Harada, Kenichiro× Matsuo, Koji× C Tanaka, Saori× Kawato, Mitsuo× Kasai, Kiyoto× Kato, Nobumasa× Takahashi, Hidehiko× Okamoto, Yasumasa× Yamashita, Okito× Imamizu, Hiroshi× Noriaki, Yahata |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability. | |||||
| 書誌情報 |
PLoS biology 巻 18, 号 12, p. e3000966, 発行日 2020-12 |
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| 出版者 | ||||||
| 出版者 | Public Library of Science | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1544-9173 | |||||
| PubMed番号 | ||||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 33284797 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1371/journal.pbio.3000966 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000966 | |||||
