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

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

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 met...

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Bibliographic Details
Main Authors: Takashi Nakano, Masahiro Takamura, Naho Ichikawa, Go Okada, Yasumasa Okamoto, Makiko Yamada, Tetsuya Suhara, Shigeto Yamawaki, Junichiro Yoshimoto
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-05-01
Series:Frontiers in Psychiatry
Subjects:
depression
functional connectivity
machine learning
harmonization
multi-center fMRI
resting state fMRI
Online Access:https://www.frontiersin.org/article/10.3389/fpsyt.2020.00400/full

Internet

https://www.frontiersin.org/article/10.3389/fpsyt.2020.00400/full

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