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Objective Pain Assessment: a Key for the Management of Chronic Pain

The individual and social burdens associated with chronic pain have been escalating globally. Accurate pain measurement facilitates early diagnosis, disease progression monitoring and therapeutic efficacy evaluation, thus is a key for the management of chronic pain. Although the “golden standards” o...

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Autores principales: Xu, Xiaohan, Huang, Yuguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979466/
https://www.ncbi.nlm.nih.gov/pubmed/32047606
http://dx.doi.org/10.12688/f1000research.20441.1
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author Xu, Xiaohan
Huang, Yuguang
author_facet Xu, Xiaohan
Huang, Yuguang
author_sort Xu, Xiaohan
collection PubMed
description The individual and social burdens associated with chronic pain have been escalating globally. Accurate pain measurement facilitates early diagnosis, disease progression monitoring and therapeutic efficacy evaluation, thus is a key for the management of chronic pain. Although the “golden standards” of pain measurement are self-reported scales in clinical practice, the reliability of these subjective methods could be easily affected by patients’ physiological and psychological status, as well as the assessors’ predispositions. Therefore, objective pain assessment has attracted substantial attention recently. Previous studies of functional magnetic resonance imaging (fMRI) revealed that certain cortices and subcortical areas are commonly activated in subjects suffering from pain. Dynamic pain connectome analysis also found various alterations of neural network connectivity that are correlated with the severity of clinical pain symptoms. Electroencephalograph (EEG) demonstrated suppressed spontaneous oscillations during pain experience. Spectral power and coherence analysis of EEG also identified signatures of different types of chronic pain. Furthermore, fMRI and EEG can visualize objective brain activities modulated by analgesics in a mechanism-based way, thus bridge the gaps between animal studies and clinical trials. Using fMRI and EEG, researchers are able to predict therapeutic efficacy and identify personalized optimal first-line regimens. In the future, the emergence of magnetic resonance spectroscopy and cell labelling in MRI would encourage the investigation on metabolic and cellular pain biomarkers. The incorporation of machine learning algorithms with neuroimaging or behavior analysis could further enhance the specificity and accuracy of objective pain assessments.
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spelling pubmed-69794662020-02-10 Objective Pain Assessment: a Key for the Management of Chronic Pain Xu, Xiaohan Huang, Yuguang F1000Res Review The individual and social burdens associated with chronic pain have been escalating globally. Accurate pain measurement facilitates early diagnosis, disease progression monitoring and therapeutic efficacy evaluation, thus is a key for the management of chronic pain. Although the “golden standards” of pain measurement are self-reported scales in clinical practice, the reliability of these subjective methods could be easily affected by patients’ physiological and psychological status, as well as the assessors’ predispositions. Therefore, objective pain assessment has attracted substantial attention recently. Previous studies of functional magnetic resonance imaging (fMRI) revealed that certain cortices and subcortical areas are commonly activated in subjects suffering from pain. Dynamic pain connectome analysis also found various alterations of neural network connectivity that are correlated with the severity of clinical pain symptoms. Electroencephalograph (EEG) demonstrated suppressed spontaneous oscillations during pain experience. Spectral power and coherence analysis of EEG also identified signatures of different types of chronic pain. Furthermore, fMRI and EEG can visualize objective brain activities modulated by analgesics in a mechanism-based way, thus bridge the gaps between animal studies and clinical trials. Using fMRI and EEG, researchers are able to predict therapeutic efficacy and identify personalized optimal first-line regimens. In the future, the emergence of magnetic resonance spectroscopy and cell labelling in MRI would encourage the investigation on metabolic and cellular pain biomarkers. The incorporation of machine learning algorithms with neuroimaging or behavior analysis could further enhance the specificity and accuracy of objective pain assessments. F1000 Research Limited 2020-01-23 /pmc/articles/PMC6979466/ /pubmed/32047606 http://dx.doi.org/10.12688/f1000research.20441.1 Text en Copyright: © 2020 Xu X and Huang Y http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Xu, Xiaohan
Huang, Yuguang
Objective Pain Assessment: a Key for the Management of Chronic Pain
title Objective Pain Assessment: a Key for the Management of Chronic Pain
title_full Objective Pain Assessment: a Key for the Management of Chronic Pain
title_fullStr Objective Pain Assessment: a Key for the Management of Chronic Pain
title_full_unstemmed Objective Pain Assessment: a Key for the Management of Chronic Pain
title_short Objective Pain Assessment: a Key for the Management of Chronic Pain
title_sort objective pain assessment: a key for the management of chronic pain
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979466/
https://www.ncbi.nlm.nih.gov/pubmed/32047606
http://dx.doi.org/10.12688/f1000research.20441.1
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