Resting-state EEG activity as a Biomarker and Treatment Target in Depression: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Depression is a highly prevalent and disabling disorder affecting approximately 5% of the adult population worldwide. Despite its impact, the underlying pathophysiology remains insufficiently understood, and current treatments are only partially effective. Brain-based biomarkers offer promise for clarifying mechanisms of depression and guiding novel treatment approaches, including neuromodulation. EEG is particularly attractive for this purpose due to its wide availability, cost-effectiveness, and potential for direct neuromodulatory targeting. We conducted a PROSPERO-registered systematic review in accordance with PRISMA guidelines to assess resting-state EEG biomarkers in adult patients with depression, diagnosed according to DSM-IV/V or ICD-10/11. Included studies reported cross-sectional or correlational data on quantitative EEG measures such as power, cordance, peak frequency, and alpha asymmetry. Semiquantitative analyses using modified albatross plots and meta-analyses were performed. Study quality was assessed with a modified Newcastle-Ottawa Scale. Fifty-two studies met the inclusion criteria. Findings indicated increased low- frequency (delta, theta) and high-frequency (beta, gamma) power, and left frontal alpha asymmetry in depressed patients compared to healthy controls. Meta-analysis confirmed a significant increase in beta power. However, results regarding disease severity correlations and data on peak alpha frequency and cordance were insufficient for interpretation. Risk of bias across studies was high. Our results support increased beta and potentially also theta oscillations and alpha asymmetry as candidate diagnostic EEG biomarkers for depression. These oscillations may reflect disrupted corticolimbic control and reward processing and partially overlap with mechanisms implicated in chronic pain and fatigue. Further investigation is warranted into their potential as diagnostic tools and neuromodulatory treatment targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".