Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI

Takashi Nakano; Masahiro Takamura; Naho Ichikawa; Go Okada; Yasumasa Okamoto; Makiko Yamada; Tetsuya Suhara; Shigeto Yamawaki; Junichiro Yoshimoto

doi:10.3389/fpsyt.2020.00400

Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI

Front Psychiatry. 2020 May 28:11:400. doi: 10.3389/fpsyt.2020.00400. eCollection 2020.

Authors

Takashi Nakano¹, Masahiro Takamura², Naho Ichikawa², Go Okada², Yasumasa Okamoto², Makiko Yamada^{3

4}, Tetsuya Suhara³, Shigeto Yamawaki², Junichiro Yoshimoto¹

Affiliations

¹ Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
² Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan.
³ Institute of Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan.
⁴ Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan.

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.

Keywords: depression; functional connectivity; harmonization; machine learning; multi-center fMRI; resting state fMRI.