ArticleMedical image analysis2023
Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Source-free unsupervised domain adaptation: A survey.Neural networks : the official journal of the International Neural Network Society · 2024Pooled it
- Article
- Multi-Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Uncertainty aware domain incremental learning for cross domain depression detection.Scientific reports · 2025Article
- Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.Medical image analysis · 2025Article
- ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.NeuroImage · 2025Article
- Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis.Pattern recognition · 2025Article
- Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI.IEEE transactions on bio-medical engineering · 2024Article
- Dynamic Weighting Translation Transfer Learning for Imbalanced Medical Image Classification.Entropy (Basel, Switzerland) · 2024Article
- Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection.Human brain mapping · 2023Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- Dynamic Graph Clustering Learning for Unsupervised Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols. To address this issue, we develop a novel discrepancy-based unsupervised cross-domain fMRI adaptation framework (called UFA-Net) for automated MDD identification. The proposed UFA-Net is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation for cross-site MDD identification. Extensive evaluation on 681 subjects from two imaging sites shows that the proposed method outperforms several state-of-the-art methods. Our method helps localize disease-associated functional connectivity abnormalities and is therefore well interpretable and can facilitate fMRI-based analysis of MDD in clinical practice.
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Registered trials
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.