A systematic review of quantitative EEG findings in Long COVID, Fibromyalgia and Chronic Fatigue Syndrome
Bibliographic record
Abstract
Abstract Long COVID (LC) is a multisymptom clinical syndrome with similarities to Fibromyalgia Syndrome (FMS) and Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME). All these conditions are believed to be associated with centrally driven mechanisms such as central sensitisation. There is a lack of consensus on quantitative EEG (qEEG) changes observed in these conditions. This review aims to synthesise and appraise the literature on resting-state qEEG in LC, FMS and CFS/ME, to help uncover possible mechanisms of central sensitisation in these similar clinical syndromes. A systematic search of MEDLINE, Embase, CINHAL, PsycINFO and Web of Science databases for articles published between December 1994 and September 2023 was performed. Following screening for predetermined selection criteria and out of the initial 2510 studies identified, 17 articles were retrieved that met all the inclusion criteria, particularly of assessing qEEG changes in one of the three conditions compared to healthy controls. All studies scored moderate to high quality on the Newcastle-Ottawa scale. There was a general trend for decreased low-frequency EEG band activity (delta, theta, and alpha) and increased high-frequency EEG beta activity in FMS, whereas an opposite trend was found in CFS/ME. The limited LC studies included in this review focused mainly on cognitive impairments and showed mixed findings not consistent with patterns seen in FMS and CFS/ME. Further research is required to explore whether there are phenotypes within LC that have EEG signatures similar to FMS or CFS/ME. This could inform identification of reliable diagnostic markers and possible targets for neuromodulation therapies.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".