ArticleHuman brain mapping2024
Classification of MDD using a Transformer classifier with large-scale multisite resting-state fMRI data.
Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 29 citations in OpenAlex.
- HDGNN-Mamba2: Mamba-Based Spatiotemporal Heterogeneous Dynamic Graph Neural Network for Major Depressive Disorder Classification.Brain sciences · 2026Article
- WGTMM: WGAN with Transformer Feature Matching for Generating fMRI Data in MCI Patients.Brain sciences · 2026Article
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- A Framework for Advancing Mechanistic Neurobehavioral Biomarkers in Psychiatry.Biological psychiatry · 2026Review
- 3DViT-GAT: a unified atlas-based 3D vision transformer and graph learning framework for major depressive disorder detection using structural MRI data.Scientific reports · 2026Article
- Ecologically-Valid Emotion Signatures Enhance Mood Disorder Diagnostics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Transformers for fMRI analysis: from spatiotemporal modeling to brain foundation models.Frontiers in neuroinformatics · 2026Review
- Obsessive-compulsive disorder detection using ensemble of scalp EEG-based convolutional neural network.Physical and engineering sciences in medicine · 2025Article
- Random Walk-Based Node Feature Learning for Major Depressive Disorder Identification Through Multi-Site rs-fMRI Data.Human brain mapping · 2025Article
- Detection of Major Depressive Disorder from Functional Magnetic Resonance Imaging Using Regional Homogeneity and Feature/Sample Selective Evolving Voting Ensemble Approaches.Journal of imaging · 2025Article
- Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data.Frontiers in computational neuroscience · 2025Article
- Multiscale Contextual Mamba: Advancing Psychiatric Disorder Detection across Multisite Functional Magnetic Resonance Imaging Datasets via State Space Modeling.Health data science · 2025Article
- Classification of MDD using a Transformer classifier with large-scale multisite resting-state fMRI data.Human brain mapping · 2024Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide with high recurrence rate. Identifying MDD patients, particularly those with recurrent episodes with resting-state fMRI, may reveal the relationship between MDD and brain function. We proposed a Transformer-Encoder model, which utilized functional connectivity extracted from large-scale multisite rs-fMRI datasets to classify MDD and HC. The model discarded the Transformer's Decoder part, reducing the model's complexity and decreasing the number of parameters to adapt to the limited sample size and it does not require a complex feature selection process and achieves end-to-end classification. Additionally, our model is suitable for classifying data combined from multiple brain atlases and has an optional unsupervised pre-training module to acquire optimal initial parameters and speed up the training process. The model's performance was tested on a large-scale multisite dataset and identified brain regions affected by MDD using the Grad-CAM method. After conducting five-fold cross-validation, our model achieved an average classification accuracy of 68.61% on a dataset consisting of 1611 samples. For the selected recurrent MDD dataset, the model reached an average classification accuracy of 78.11%. Abnormalities were detected in the frontal gyri and cerebral cortex of MDD patients in both datasets. Furthermore, the identified brain regions in the recurrent MDD dataset generally exhibited a higher contribution to the model's performance.
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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.