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Article: Decoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities

TitleDecoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities
Authors
Issue Date2016
PublisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/computational_neuroscience
Citation
Frontiers in Computational Neuroscience, 2016, v. 10, p. Article 32 How to Cite?
AbstractPain is a highly subjective experience. Self-report is the gold standard for pain assessment in clinical practice, but it may not be available or reliable in some populations. Neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have the potential to be used to provide physiology-based and quantitative nociceptive pain assessment tools that complements self-report. However, existing neuroimaging-based nociceptive pain assessments only rely on the information in pain-evoked brain activities, but neglect the fact that the perceived intensity of pain is also encoded by ongoing brain activities prior to painful stimulation. Here, we proposed to use machine learning algorithms to decode pain intensity from both pre-stimulus ongoing and post-stimulus evoked brain activities. Neural features that were correlated with intensity of laser-evoked nociceptive pain were extracted from high-dimensional pre- and post-stimulus EEG and fMRI activities using partial least-squares regression (PLSR). Further, we used support vector machine (SVM) to predict the intensity of pain from pain-related time-frequency EEG patterns and BOLD-fMRI patterns. Results showed that combining predictive information in pre- and post-stimulus brain activities can achieve significantly better performance in classifying high-pain and low-pain and in predicting the rating of perceived pain than only using post-stimulus brain activities. Therefore, the proposed pain prediction method holds great potential in basic research and clinical applications.
Persistent Identifierhttp://hdl.handle.net/10722/234594
ISSN
2015 Impact Factor: 2.653
2015 SCImago Journal Rankings: 1.355

 

DC FieldValueLanguage
dc.contributor.authorTU, Y-
dc.contributor.authorTAN, A-
dc.contributor.authorBai, Y-
dc.contributor.authorHung, YS-
dc.contributor.authorZhang, Z-
dc.date.accessioned2016-10-14T13:47:55Z-
dc.date.available2016-10-14T13:47:55Z-
dc.date.issued2016-
dc.identifier.citationFrontiers in Computational Neuroscience, 2016, v. 10, p. Article 32-
dc.identifier.issn1662-5188-
dc.identifier.urihttp://hdl.handle.net/10722/234594-
dc.description.abstractPain is a highly subjective experience. Self-report is the gold standard for pain assessment in clinical practice, but it may not be available or reliable in some populations. Neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have the potential to be used to provide physiology-based and quantitative nociceptive pain assessment tools that complements self-report. However, existing neuroimaging-based nociceptive pain assessments only rely on the information in pain-evoked brain activities, but neglect the fact that the perceived intensity of pain is also encoded by ongoing brain activities prior to painful stimulation. Here, we proposed to use machine learning algorithms to decode pain intensity from both pre-stimulus ongoing and post-stimulus evoked brain activities. Neural features that were correlated with intensity of laser-evoked nociceptive pain were extracted from high-dimensional pre- and post-stimulus EEG and fMRI activities using partial least-squares regression (PLSR). Further, we used support vector machine (SVM) to predict the intensity of pain from pain-related time-frequency EEG patterns and BOLD-fMRI patterns. Results showed that combining predictive information in pre- and post-stimulus brain activities can achieve significantly better performance in classifying high-pain and low-pain and in predicting the rating of perceived pain than only using post-stimulus brain activities. Therefore, the proposed pain prediction method holds great potential in basic research and clinical applications.-
dc.languageeng-
dc.publisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/computational_neuroscience-
dc.relation.ispartofFrontiers in Computational Neuroscience-
dc.rightsThis Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleDecoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities-
dc.typeArticle-
dc.identifier.emailBai, Y: tsdwx56@hku.hk-
dc.identifier.emailHung, YS: yshung@eee.hku.hk-
dc.identifier.authorityHung, YS=rp00220-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3389/fncom.2016.00032-
dc.identifier.hkuros270129-
dc.identifier.volume10-
dc.identifier.spageArticle 32-
dc.identifier.epageArticle 32-
dc.publisher.placeSwitzerland-

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