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Title: Decoding subjective intensity of nociceptive pain from pre-stimulus and post-stimulus brain activities
Authors: Tu, Yiheng
Tan, Ao
Bai, Yanru
Hung, Yeung Sam
Zhang, Zhiguo
Keywords: EEG
Pre-stimulus Brain Activity
DRNTU::Engineering::Chemical engineering
Issue Date: 2016
Source: Tu, Y., Tan, A., Bai, Y., Hung, Y. S., & Zhang, Z. (2016). Decoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities. Frontiers in Computational Neuroscience, 10, 32-. doi:10.3389/fncom.2016.00032
Series/Report no.: Frontiers in Computational Neuroscience
Abstract: Pain 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.
DOI: 10.3389/fncom.2016.00032
Schools: School of Chemical and Biomedical Engineering 
Rights: © 2016 Tu, Tan, Bai, Hung and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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