ArticlePatterns (New York, N.Y.)2024
An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Structured Prototype Learning with Feature Fusion for Sparse and Asynchronous Audio-Visual Depression Recognition.Sensors (Basel, Switzerland) · 2026Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.PLOS digital health · 2026Article
- Cross-sectional associations between CES-D scores and the salivary metabolome in nonclinical adults.Discover mental health · 2026Article
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- ASYM: multimodal depression recognition via mamba-enhanced attentive feature fusion.Frontiers in psychiatry · 2026Article
- Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers.NPJ digital medicine · 2025Article
- Multimodal graph neural networks in healthcare: a review of fusion strategies across biomedical domains.Frontiers in artificial intelligence · 2025Review
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study developed an artificial intelligence (AI) system using a local-global multimodal fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major depressive disorder (MDD), a complex disease influenced by social, psychological, and biological factors. Utilizing functional MRI, structural MRI, and electronic health records, the system offers an objective diagnostic method by integrating individual brain regions and population data. Tested across cohorts from China, Japan, and Russia with 1,182 healthy controls and 1,260 MDD patients from 24 institutions, it achieved a classification accuracy of 78.75%, an area under the receiver operating characteristic curve (AUROC) of 80.64%, and correctly identified MDD subtypes. The system further discovered distinct brain connectivity patterns in MDD, including reduced functional connectivity between the left gyrus rectus and right cerebellar lobule VIIB, and increased connectivity between the left Rolandic operculum and right hippocampus. Anatomically, MDD is associated with thickness changes of the gray and white matter interface, indicating potential neuropathological conditions or brain injuries.
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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.