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

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number400
JournalFrontiers in Psychiatry
Volume11
DOIs
Publication statusPublished - 28-05-2020

All Science Journal Classification (ASJC) codes

  • Psychiatry and Mental health

Fingerprint Dive into the research topics of 'Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI'. Together they form a unique fingerprint.

  • Cite this

    Nakano, T., Takamura, M., Ichikawa, N., Okada, G., Okamoto, Y., Yamada, M., Suhara, T., Yamawaki, S., & Yoshimoto, J. (2020). Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI. Frontiers in Psychiatry, 11, [400]. https://doi.org/10.3389/fpsyt.2020.00400