Resting-state EEG and MEG biomarkers of pathological fatigue - A transdiagnostic systematic review

Abstract

Fatigue is a highly prevalent and disabling symptom of many disorders and syndromes, resulting from different pathomechanisms. However, whether and how different mechanisms converge and result in similar symptomatology is only partially understood, and transdiagnostic biomarkers that could further the diagnosis and treatment of fatigue are lacking. We, therefore, performed a transdiagnostic systematic review (PROSPERO: CRD42022330113) of quantitative resting-state electroencephalography (EEG) and magnetoencephalography (MEG) studies in adult patients suffering from pathological fatigue in different disorders. Studies investigating fatigue in healthy participants were excluded. The risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semi-quantitative data synthesis was conducted using modified albatross plots. After searching MEDLINE, Web of Science Core Collection, and EMBASE, 26 studies were included. Cross-sectional studies revealed increased brain activity at theta frequencies and decreased activity at alpha frequencies as potential diagnostic biomarkers. However, the risk of bias was high in many studies and domains. Together, this transdiagnostic systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of pathological fatigue. Beyond, this review might help to guide future M/EEG studies on the development of fatigue biomarkers.

Keywords: Biomarker; EEG; Fatigue; MEG; Systematic review; Transdiagnostic.

Fig. 1

PRISMA Flowchart of study selection. PRISMA, preferred reporting items for systematic reviews and meta-analyses; RCT, randomized controlled trial.

Fig. 2

Conditions and M/EEG parameters. The figure shows counts for the conditions studied in records included (Panel A) and M/EEG parameters assessed (Panel B). CFS, chronic fatigue syndrome; CRF, cancer-related fatigue; FMS, fibromyalgia syndrome; MS, multiple sclerosis; PVFS, post-viral fatigue syndrome; M/EEG, magneto-/electroencephalography.

Fig. 3

Risk of bias assessment for included studies. M/EEG, magneto-/electroencephalography.

Fig. 4

Results of cross-sectional analyses of power for the different frequency bands. Power differences between patients and healthy participants in cross-sectional studies. P values on the x-axis are displayed on a logarithmic scale (log10). Higher values in patients compared to healthy participants are depicted on the right-hand side, non-significant differences in the middle and lower values on the left-hand side of each panel. The total sample size for single studies is depicted on the y-axis. n.s., not significant.

Fig. 5

Results of cross-sectional peak frequency analyses. Differences between patients and healthy participants in cross-sectional studies. P values on the x-axis are displayed on a logarithmic scale (log10). Higher values in patients compared to healthy participants are depicted on the right-hand side, non-significant differences in the middle and lower values on the left-hand side. The total sample size for single studies is depicted on the y-axis. n.s., not significant.

Fig. 6

Results of cross-sectional connectivity analyses for the different frequency bands. Differences between patients and healthy participants in cross-sectional studies. P values on the x-axis are displayed on a logarithmic scale (log10). Higher values in patients compared to healthy participants are depicted on the right-hand side, non-significant differences in the middle and lower values on the left-hand side of each panel. The total sample size for single studies is depicted on the y-axis. n.s., not significant.