AUTHOR=Nakano Takashi , Takamura Masahiro , Ichikawa Naho , Okada Go , Okamoto Yasumasa , Yamada Makiko , Suhara Tetsuya , Yamawaki Shigeto , Yoshimoto Junichiro TITLE=Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI JOURNAL=Frontiers in Psychiatry VOLUME=11 YEAR=2020 URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2020.00400 DOI=10.3389/fpsyt.2020.00400 ISSN=1664-0640 ABSTRACT=

Resting-state fMRI has the potential to help doctors detect 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 the 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 sites.