Quantitative Electroencephalography for Objective and Differential Diagnosis of Depression: A Comprehensive Review
Bibliographic record
Abstract
Quantitative electroencephalography (QEEG) has been dramatically developed during recent years in cognitive neurosciences. It has shown significant potential in the diagnosis of cognitive neurological disorders as well as in the evaluation of treatment outcomes and response. Early diagnosis of depression, differential diagnosis, and assessing the treatment outcomes and response are currently the main research fields of QEEG in depression. Identifying reliable disorder-specific EEG-based biomarkers that have strong correlations with the depression specific cognitive functions is one of the major challenges in these fields. Such biomarkers not only allow early and cost-effective diagnosis of depression, but also may have differential diagnostic and predictive values for treatment response of a variety of treatments. This paper aims at a comprehensive review on the main principles of QEEG in developing biomarkers for MDD. The databases of PubMed (1985-2015), Web of Sciences (1985-2015), and Google Scholar (1980-2015) were searched using the set terms. The obtained results were screened for the title and abstract by two authors and they came to consensus whether the studies are related to the review. The main advantages of QEEG for mood disorders are also reviewed. In addition, different QEEG-based measures for objective diagnosis of MDD as well as for distinguishing depressed patients from healthy subjects are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".