ArticleScientific reports2022
An electroencephalography connectome predictive model of major depressive disorder severity.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Beyond the label "major depressive disorder"-detailed characterization of study population matters for EEG-biomarker research.Frontiers in neuroscience · 2025Pooled it
- The application of electroencephalogram in depression research: bibliometric and technological application analysis from 2005 to 2025.Frontiers in neuroscience · 2025Pooled it
- Identification of Longitudinal Associations Between Depressive Symptoms and EEG Functional Connectivity in Adolescents Receiving Antidepressant Treatment.Research square · 2026Article
- Cognition-Modulated EEG Signatures and Clinical Features in Major Depressive Disorder: A Machine Learning-Based Exploratory Study.Alpha psychiatry · 2026Article
- From EEG signals to quantitative assessment: predicting depression severity using a novel deep learning framework.Scientific reports · 2026Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation.Brain and behavior · 2025Article
- Study on the effects of exercise intervention combined with virtual reality technology on emotions and brain networks in secondary school students with depressive symptoms.Frontiers in psychiatry · 2025Article
- Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review.PloS one · 2025Article
- Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction.Brain informatics · 2024Article
- Individual deviations from normative electroencephalographic connectivity predict antidepressant response.Journal of affective disorders · 2024Article
- Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder.Npj mental health research · 2023Review
- Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine.Frontiers in neuroscience · 2023Article
- An electroencephalography connectome predictive model of craving for methamphetamine.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
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Authors and funding
6 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10
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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.