Temporal dynamics of electroencephalographic microstates during sustained pain
Brain dynamics can be modeled by a sequence of transient, nonoverlapping patterns of quasi-stable electrical potentials named "microstates."While electroencephalographic (EEG) microstates among patients with chronic pain remained inconsistent in the literature, this study characterizes the temporal dynamics of EEG microstates among healthy individuals during experimental sustained pain. We applied capsaicin (pain condition) or control (no-pain condition) cream to 58 healthy participants in different sessions and recorded resting-state EEG 15 min after application. We identified 4 canonical microstates (A-D) that are related to auditory, visual, salience, and attentional networks. Microstate C had less occurrence, as were bidirectional transitions between microstate C and microstates A and B during sustained pain. In contrast, sustained pain was associated with more frequent and longer duration of microsite D, as well as more bidirectional transitions between microstate D and microstates A and B. Microstate D duration positively correlated with intensity of ongoing pain. Sustained pain improved global integration within microstate C functional network, but weakened global integration and efficiency within microstate D functional network. These results suggest that sustained pain leads to an imbalance between processes that load on saliency (microstate C) and processes related to switching and reorientation of attention (microstate D).
National Natural Science Foundation of China["32271105","62101236","62276262"] ; Shenzhen Basic Research Project[JCYJ20190 808154413592] ; Beijing Natural Science Foundation
|WOS Research Area|
Neurosciences & Neurology
|WOS Accession No|
|ESI Research Field|
NEUROSCIENCE & BEHAVIOR
Cited Times [WOS]:1
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
1.Laboratory of Brain Atlas and Brain-Inspired Intelligence,State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Science,Beijing,100190,China
2.School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,100049,China
3.School of Psychology,Shenzhen University,Shenzhen,Guangdong,518060,China
4.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,XueyuanAve1088, , Guangdong,518055,China
|Corresponding Author Affilication||Department of Computer Science and Engineering|
Qiu，Shuang,Lyu，Xiaohan,Zheng，Qianqian,et al. Temporal dynamics of electroencephalographic microstates during sustained pain[J]. Cerebral Cortex,2023,33(13):8594-8604.
Qiu，Shuang,Lyu，Xiaohan,Zheng，Qianqian,He，Huiguang,Jin，Richu,&Peng，Weiwei.(2023).Temporal dynamics of electroencephalographic microstates during sustained pain.Cerebral Cortex,33(13),8594-8604.
Qiu，Shuang,et al."Temporal dynamics of electroencephalographic microstates during sustained pain".Cerebral Cortex 33.13(2023):8594-8604.
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