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A systematic review of quantitative EEG findings in Long COVID, Fibromyalgia and Chronic Fatigue Syndrome (preprint)
medrxiv; 2023.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2023.11.06.23298171
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.

Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2023 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2023 Document Type: Preprint