2020年12月7日
Generalizable brain network markers of major depressive disorder across multiple imaging sites
PLOS Biology
- 巻
- 18
- 号
- 12
- 開始ページ
- e3000966
- 終了ページ
- e3000966
- 記述言語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1371/journal.pbio.3000966
- 出版者・発行元
- Public Library of Science (PLoS)
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.
- リンク情報
- ID情報
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- DOI : 10.1371/journal.pbio.3000966
- eISSN : 1545-7885