ArticleNeuroImage. Clinical2018
Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 98 citations in OpenAlex.
- Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis.NeuroImage. Clinical · 2019Pooled it
- Infraslow Closed-Loop Brain Training for Anxiety and Depression (ISAD): A pilot randomised, sham-controlled trial in adult females with internalizing disorders.Cognitive, affective & behavioral neuroscience · 2025Trial
- Magnetoencephalography changes in cervical spondylotic myelopathy: a study of resting-state and somatosensory evoked magnetic fields.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- EEG complexity analysis using enhanced entropy features for depression detection and severity classification.Journal of neuroengineering and rehabilitation · 2026Article
- EEG Unpredictability in the Resting State of Major Depressive Disorder: A Multidomain EEG Analysis.Brain topography · 2026Article
- Resting-state EEG markers associated with violence risk in patients with major depressive disorder.Psychological medicine · 2026Article
- Perspective-taking in video advertising: insights from EEG connectivity and channel-wise spectral analysis.Frontiers in behavioral neuroscience · 2026Article
- Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Support vector machine classification of patients with depression based on resting-state electroencephalography.Asian biomedicine : research, reviews and news · 2024Article
- Article
- Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia.Molecular psychiatry · 2024Article
- Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis.Frontiers in human 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
- Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder.BMC psychiatry · 2023Article
- Functional brain network features specify DBS outcome for patients with treatment resistant depression.Molecular psychiatry · 2023Article
- Do we really measure what we think we are measuring?iScience · 2023Article
- The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research.Biomedicines · 2022Review
- An electroencephalography connectome predictive model of major depressive disorder severity.Scientific reports · 2022Article
- Alteration of cortical functional networks in mood disorders with resting-state electroencephalography.Scientific reports · 2022Article
- Brain Functional Network and Amino Acid Metabolism Association in Females with Subclinical Depression.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD). Method: Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales. Results: At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales. Conclusion: Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD.
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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.