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dc.contributor.authorKuo, Po-Chihen_US
dc.contributor.authorChen, Yi-Tien_US
dc.contributor.authorChen, Yong-Shengen_US
dc.contributor.authorChen, Li-Fenen_US
dc.date.accessioned2017-04-21T06:55:28Z-
dc.date.available2017-04-21T06:55:28Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn1053-8119en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.neuroimage.2016.09.040en_US
dc.identifier.urihttp://hdl.handle.net/11536/132956-
dc.description.abstractDecoding the neural representations of pain is essential to obtaining an objective assessment as well as an understanding of its underlying mechanisms. The complexities involved in the subjective experience of pain make it difficult to obtain a quantitative assessment from the induced spatiotemporal patterns of brain activity of high dimensionality. Most previous studies have investigated the perception of pain by analyzing the amplitude or spatial patterns in the response of the brain to external stimulation. This study investigated the decoding of endogenous pain perceptions according to resting-state magnetoencephalographic (MEG) recordings. In our experiments, we applied a beamforming method to calculate the brain activity for every brain region and examined temporal and spectral features of brain activity for predicting the intensity of perceived pain in patients with primary dysmenorrhea undergoing menstrual pain. Our results show that the asymmetric index of sample entropy in the precuneus and the sample entropy in the left posterior cingulate gyrus were the most informative characteristics associated with the perception of menstrual pain. The correlation coefficient (p=0.64, p < 0.001) between the predicted and self-reported pain scores demonstrated the high prediction accuracy. In addition to the estimated brain activity, we were able to predict accurate pain scores directly from MEG channel signals (p=0.65, p < 0.001). These findings suggest the possibility of using the proposed model based on resting-state MEG to predict the perceived intensity of endogenous pain.en_US
dc.language.isoen_USen_US
dc.subjectDecodingen_US
dc.subjectPain perceptionen_US
dc.subjectEndogenous painen_US
dc.subjectResting-state MEGen_US
dc.titleDecoding the perception of endogenous pain from resting-state MEGen_US
dc.identifier.doi10.1016/j.neuroimage.2016.09.040en_US
dc.identifier.journalNEUROIMAGEen_US
dc.citation.volume144en_US
dc.citation.spage1en_US
dc.citation.epage11en_US
dc.contributor.department分子醫學與生物工程研究所zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentInstitute of Molecular Medicine and Bioengineeringen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000390982800001en_US
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