Resting-state Electroencephalography Microstates Correlate with Pain Intensity in Patients with Complex Regional Pain Syndrome.
Michihiro OsumiMasahiko SumitaniKatsuyuki IwatsukiMinoru HoshiyamaRyota ImaiShu MoriokaHitoshi HirataPublished in: Clinical EEG and neuroscience (2023)
Objective : Severe pain and other symptoms in complex regional pain syndrome (CRPS), such as allodynia and hyperalgesia, are associated with abnormal resting-state brain network activity. No studies to date have examined resting-state brain networks in CRPS patients using electroencephalography (EEG), which can clarify the temporal dynamics of brain networks. Methods : We conducted microstate analysis using resting-state EEG signals to prospectively reveal direct correlations with pain intensity in CRPS patients (n = 17). Five microstate topographies were fitted back to individual CRPS patients' EEG data, and temporal microstate measures were subsequently calculated. Results : Our results revealed five distinct microstates, termed microstates A to E, from resting EEG data in patients with CRPS. Microstates C, D and E were significantly correlated with pain intensity before pain treatment. Particularly, microstates D and E were significantly improved together with pain alleviation after pain treatment. As microstates D and E in the present study have previously been related to attentional networks and the default mode network, improvement in these networks might be related to pain relief in CRPS patients. Conclusions : The functional alterations of these brain networks affected the pain intensity of CRPS patients. Therefore, EEG microstate analyses may be used to identify surrogate markers for pain intensity.
Keyphrases
- resting state
- functional connectivity
- chronic pain
- end stage renal disease
- neuropathic pain
- pain management
- ejection fraction
- chronic kidney disease
- newly diagnosed
- peritoneal dialysis
- gene expression
- working memory
- prognostic factors
- high intensity
- spinal cord injury
- blood brain barrier
- physical activity
- heart rate
- big data
- patient reported outcomes