ArticleBMC psychiatry2023
Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder.
Article in BMC psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Resting-state EEG activity in depression: a systematic review and meta-analysis.NeuroImage. Clinical · 2026Pooled it
- A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI).BMC psychiatry · 2025Pooled it
- Beyond the label "major depressive disorder"-detailed characterization of study population matters for EEG-biomarker research.Frontiers in neuroscience · 2025Pooled it
- iTBS on RDLPFC improves performance of motor imagery: a brain-computer interface study combining EEG and fNIRS.Journal of neuroengineering and rehabilitation · 2025Trial
- Computational modeling of prefrontal-amygdala circuits links functional connectivity alterations to circuit-level mechanisms in major depression.Journal of computational neuroscience · 2026Article
- EEG Functional Connectivity Associated With Antidepressant Response to Transcutaneous Electrical Cranial-Auricular Acupoint Stimulation.CNS neuroscience & therapeutics · 2026Article
- Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.BMC medical informatics and decision making · 2026Article
- Mapping the Digital Mind: A Meta-Analysis of EEG Biomarkers in Cognition, Emotion, and Mental Health.Brain sciences · 2026Review
- Toward Multi-Dimensional Depression Assessment: EEG-Based Machine Learning and Neurophysiological Interpretation for Diagnosis, Severity, and Cognitive Decline.Brain sciences · 2026Article
- Interpretable machine-learning for depression classification based on a three-component EEG marker.Frontiers in computational neuroscience · 2026Article
- The Brain-Gut Health Initiative (BIGHI): A Prospective Cohort on Psychiatric Disorders in China.Research (Washington, D.C.) · 2026Review
- Enhancing Diagnostic Accuracy of Neurological Disorders Through Feature-Driven Multi-Class Classification with Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods.Bioengineering (Basel, Switzerland) · 2025Article
- Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness.Frontiers in neuroscience · 2025Article
- Brain neural mechanisms underlying VR enhanced aerobic exercise for mood enhancement in depressed adolescents.Frontiers in human neuroscience · 2025Article
- Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity.PLoS computational biology · 2024Article
- Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.Sensors (Basel, Switzerland) · 2024Review
- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.bioRxiv : the preprint server for biology · 2024Article
- Alpha oscillation mediates the interaction between suicide risk and symptom severity in Major Depressive Disorder.Frontiers in neuroscience · 2024Article
- Resting-state EEG features modulated by depressive state in healthy individuals: insights from theta PSD, theta-beta ratio, frontal-parietal PLV, and sLORETA.Frontiers in human neuroscience · 2024Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMajor depressive disorder (MDD) has a high incidence and an unknown mechanism. There are no objective and sensitive indicators for clinical diagnosis.
objectiveThis study explored specific electrophysiological indicators and their role in the clinical diagnosis of MDD using machine learning.
methodsForty first-episode and drug-naïve patients with MDD and forty healthy controls (HCs) were recruited. EEG data were collected from all subjects in the resting state with eyes closed for 10 min. The severity of MDD was assessed by the Hamilton Depression Rating Scale (HAMD-17). Machine learning analysis was used to identify the patients with MDD.
resultsCompared to the HC group, the relative power of the low delta and theta bands was significantly higher in the right occipital region, and the relative power of the alpha band in the entire posterior occipital region was significantly lower in the MDD group. In the MDD group, the alpha band scalp functional connectivity was overall lower, while the scalp functional connectivity in the gamma band was significantly higher than that in the HC group. In the feature set of the relative power of the ROI in each band, the highest accuracy of 88.2% was achieved using the KNN classifier while using PCA feature selection. In the explanatory model using SHAP values, the top-ranking influence feature is the relative power of the alpha band in the left parietal region.
conclusionsOur findings reveal that the abnormal EEG neural oscillations may reflect an imbalance of excitation, inhibition and hyperactivity in the cerebral cortex in first-episode and drug-naïve patients with MDD. The relative power of the alpha band in the left parietal region is expected to be an objective electrophysiological indicator of MDD.
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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.