@article{oai:repo.qst.go.jp:00079999,
 author = {Nakano, Takashi and Takamura, Masahiro and Ichikawa, Naho and Okada, Go and Okamoto, Yasumasa and Yamada, Makiko and Suhara, Tetsuya and Yamawaki, Shigeto and Yoshimoto, Junichiro and Yamada, Makiko and Suhara, Tetsuya},
 journal = {Front. Psychiatry},
 month = {May},
 note = {esting-state fMRI has the potential to find abnormal behavior in brain activity and to diagnose patients with depression. However, resting-state fMRI has a bias depending on the scanner site, which makes it difficult to diagnose depression at a new site. In this paper, we propose methods to improve the performance of diagnosis of major depressive disorder (MDD) at an independent site by reducing the site bias effects using regression. For this, we used a subgroup of healthy subjects of the independent site to regress out site bias. We further improved the classification performance of patients with depression by focusing on melancholic depressive disorder. Our proposed methods would be useful to apply depression classifiers to subjects at completely new site.},
 title = {Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI},
 volume = {11},
 year = {2020}
}