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DOI 10.1177/15500594231204174

Медицинская библиотека · Clinical EEG and neuroscience · 2023

Resting-state Electroencephalography Microstates Correlate with Pain Intensity in Patients with Complex Regional Pain Syndrome.

Osumi Michihiro, Sumitani Masahiko, Iwatsuki Katsuyuki, Hoshiyama Minoru, Imai Ryota, Morioka Shu, Hirata Hitoshi

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Аннотация

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.

Для цитирования: Osumi Michihiro, Sumitani Masahiko, Iwatsuki Katsuyuki, Hoshiyama Minoru, Imai Ryota, Morioka Shu, Hirata Hitoshi. Resting-state Electroencephalography Microstates Correlate with Pain Intensity in Patients with Complex Regional Pain Syndrome.. Clinical EEG and neuroscience. 2023. DOI: 10.1177/15500594231204174.

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