ArticleFrontiers in psychiatry2020
Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI.
Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Artificial Intelligence in Multimodal Neuroimaging Research on Depression: A Bibliometric Analysis.Psychiatry investigation · 2026Article
- Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model.Molecular psychiatry · 2026Article
- Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning.World journal of psychiatry · 2025Article
- Aberrant network topological structure of sensorimotor superficial white-matter system in major depressive disorder.Journal of Zhejiang University. Science. B · 2024Article
- Modeling the impact of MRI acquisition bias on structural connectomes: Harmonizing structural connectomes.Network neuroscience (Cambridge, Mass.) · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity.Frontiers in psychiatry · 2023Article
- The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis.Frontiers in neuroscience · 2023Review
- Editorial: The development of biomarkers in psychiatry.Frontiers in psychiatry · 2022Article
- Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder.Communications biology · 2021Article
- Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.Frontiers in psychiatry · 2021Article
- Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis.Frontiers in psychiatry · 2021Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.