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Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment
Pain is a complex and multidimensional perception, embodied in our daily experiences through interoceptive appraisal processes. The article reviews the recent literature about interoception along with predictive coding theories and tries to explain a missing link between the sense of the physiologic...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927564/ https://www.ncbi.nlm.nih.gov/pubmed/27445681 http://dx.doi.org/10.3389/fnins.2016.00314 |
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author | Di Lernia, Daniele Serino, Silvia Cipresso, Pietro Riva, Giuseppe |
author_facet | Di Lernia, Daniele Serino, Silvia Cipresso, Pietro Riva, Giuseppe |
author_sort | Di Lernia, Daniele |
collection | PubMed |
description | Pain is a complex and multidimensional perception, embodied in our daily experiences through interoceptive appraisal processes. The article reviews the recent literature about interoception along with predictive coding theories and tries to explain a missing link between the sense of the physiological condition of the entire body and the perception of pain in chronic conditions, which are characterized by interoceptive deficits. Understanding chronic pain from an interoceptive point of view allows us to better comprehend the multidimensional nature of this specific organic information, integrating the input of several sources from Gifford's Mature Organism Model to Melzack's neuromatrix. The article proposes the concept of residual interoceptive images (ghosts), to explain the diffuse multilevel nature of chronic pain perceptions. Lastly, we introduce a treatment concept, forged upon the possibility to modify the interoceptive chronic representation of pain through external input in a process that we call interoceptive modeling, with the ultimate goal of reducing pain in chronic subjects. |
format | Online Article Text |
id | pubmed-4927564 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49275642016-07-21 Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment Di Lernia, Daniele Serino, Silvia Cipresso, Pietro Riva, Giuseppe Front Neurosci Neurology Pain is a complex and multidimensional perception, embodied in our daily experiences through interoceptive appraisal processes. The article reviews the recent literature about interoception along with predictive coding theories and tries to explain a missing link between the sense of the physiological condition of the entire body and the perception of pain in chronic conditions, which are characterized by interoceptive deficits. Understanding chronic pain from an interoceptive point of view allows us to better comprehend the multidimensional nature of this specific organic information, integrating the input of several sources from Gifford's Mature Organism Model to Melzack's neuromatrix. The article proposes the concept of residual interoceptive images (ghosts), to explain the diffuse multilevel nature of chronic pain perceptions. Lastly, we introduce a treatment concept, forged upon the possibility to modify the interoceptive chronic representation of pain through external input in a process that we call interoceptive modeling, with the ultimate goal of reducing pain in chronic subjects. Frontiers Media S.A. 2016-06-30 /pmc/articles/PMC4927564/ /pubmed/27445681 http://dx.doi.org/10.3389/fnins.2016.00314 Text en Copyright © 2016 Di Lernia, Serino, Cipresso and Riva. http://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Neurology Di Lernia, Daniele Serino, Silvia Cipresso, Pietro Riva, Giuseppe Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment |
title | Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment |
title_full | Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment |
title_fullStr | Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment |
title_full_unstemmed | Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment |
title_short | Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment |
title_sort | ghosts in the machine. interoceptive modeling for chronic pain treatment |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927564/ https://www.ncbi.nlm.nih.gov/pubmed/27445681 http://dx.doi.org/10.3389/fnins.2016.00314 |
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