ArticleMolecular psychiatry2025
Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression.
Article in Molecular psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Insular spontaneous activity changes after dialectical behavior therapy skills training for depressed patients with non-suicidal self-injury: a randomized controlled trial.BMC psychiatry · 2026Trial
- Transient frontal spectral events from EEG predict antidepressant response to sertraline in depression.Journal of psychiatric research · 2026Trial
- BriGHT: transcriptome-regularized multimodal neuroimaging for brain disorder prediction.Bioinformatics (Oxford, England) · 2026Article
- Personality Heterogeneity Shapes the Neural Organization of Conditioned Threat Learning.Research square · 2026Article
- Urinary copper is linked to regional cortical volume reductions in adolescents with major depressive disorder.Translational psychiatry · 2026Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Whole-brain mapping of TMS-induced discomfort with large-scale concurrent TMS-fMRI.bioRxiv : the preprint server for biology · 2026Article
- Alpha-band fronto-limbic hyperconnectivity in adolescents with major depression and suicidal ideation.BMC psychiatry · 2026Article
- Multimodal Graphical Network Analysis of Small-for-Gestational-Age in Preterm Infants: Integrating Neonatal Brain Volume, Structural Connectivity, and Early Neurodevelopmental Outcome.Annals of biomedical engineering · 2026Article
- An EEG-based digital biomarker for personalizing transcranial magnetic stimulation in major depressive disorder.NPJ digital medicine · 2026Article
- Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Transient frontal spectral events from EEG predict antidepressant response to sertraline in depression.medRxiv : the preprint server for health sciences · 2026Article
- From symptom-based heterogeneity to mechanism-based profiling in youth ADHD: the promise of computational psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- Hippocampal NLRP3 Inflammasome Drives Anxiety- and Depressive-Like Behaviors Following Drug-Induced Liver Injury.International journal of biological sciences · 2026Article
- From synapse to system: mechanistic pathways of neural signaling dysfunction in psychiatric disorders.Frontiers in cell and developmental biology · 2026Review
- Dual-Stream deep learning for multimodal feature fusion and classification of balance control in elite freestyle aerial skiers.PloS one · 2026Article
- Review
- Botulinum Toxin: An Unconventional Tool for the Treatment of Depression?Brain sciences · 2025Review
- Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD's complex and varied neuropathology. Identifying biomarkers for antidepressant treatment requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined with advanced data-driven methods, can enhance our understanding of the neurobiological processes influencing treatment outcomes. To address this, we analyzed resting-state fMRI and EEG connectivity data from 130 patients treated with sertraline and 135 patients with placebo from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks to integrate data-augmented connectivity and cross-modality correlation, aiming to predict individual symptom changes by revealing multimodal brain network signatures. The results showed that our model demonstrated promising prediction accuracy, with an R
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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.