Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI-Reference-Cited by-同舟云学术

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

Published:2020-05-28 Issue: Volume:11 Page:
ISSN:1664-0640
Container-title:Frontiers in Psychiatry
language:
Short-container-title:Front. Psychiatry

Author:

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

Funder

Japan Agency for Medical Research and Development

Ministry of Health, Labour and Welfare

Publisher

Frontiers Media SA

Subject

Psychiatry and Mental health

Reference41 articles.

1. Resting-state connectivity biomarkers define neurophysiological subtypes of depression;Drysdale;Nat Med,2017

2. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias;Yamashita;PloS Biol,2019

3. Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants;Ichikawa;Sci Rep,2020

4. Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression;Yoshida;PloS One,2017

5. A Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity;Takagi;Sci Rep Nat Publishing Group;,2017

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