Cargando…

Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a...

Descripción completa

Detalles Bibliográficos
Autores principales: Narula, Vaibhav, Zippo, Antonio Giuliano, Muscoloni, Alessandro, Biella, Gabriele Eliseo M., Cannistraci, Carlo Vittorio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214247/
https://www.ncbi.nlm.nih.gov/pubmed/30443582
http://dx.doi.org/10.1007/s41109-017-0048-x
_version_ 1783367948084183040
author Narula, Vaibhav
Zippo, Antonio Giuliano
Muscoloni, Alessandro
Biella, Gabriele Eliseo M.
Cannistraci, Carlo Vittorio
author_facet Narula, Vaibhav
Zippo, Antonio Giuliano
Muscoloni, Alessandro
Biella, Gabriele Eliseo M.
Cannistraci, Carlo Vittorio
author_sort Narula, Vaibhav
collection PubMed
description The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s41109-017-0048-x) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6214247
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-62142472018-11-13 Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain? Narula, Vaibhav Zippo, Antonio Giuliano Muscoloni, Alessandro Biella, Gabriele Eliseo M. Cannistraci, Carlo Vittorio Appl Netw Sci Research The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s41109-017-0048-x) contains supplementary material, which is available to authorized users. Springer International Publishing 2017-08-30 2017 /pmc/articles/PMC6214247/ /pubmed/30443582 http://dx.doi.org/10.1007/s41109-017-0048-x Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Narula, Vaibhav
Zippo, Antonio Giuliano
Muscoloni, Alessandro
Biella, Gabriele Eliseo M.
Cannistraci, Carlo Vittorio
Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
title Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
title_full Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
title_fullStr Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
title_full_unstemmed Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
title_short Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
title_sort can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214247/
https://www.ncbi.nlm.nih.gov/pubmed/30443582
http://dx.doi.org/10.1007/s41109-017-0048-x
work_keys_str_mv AT narulavaibhav canlocalcommunityparadigmandepitopologicallearningenhanceourunderstandingofhowlocalbrainconnectivityisabletoprocesslearnandmemorizechronicpain
AT zippoantoniogiuliano canlocalcommunityparadigmandepitopologicallearningenhanceourunderstandingofhowlocalbrainconnectivityisabletoprocesslearnandmemorizechronicpain
AT muscolonialessandro canlocalcommunityparadigmandepitopologicallearningenhanceourunderstandingofhowlocalbrainconnectivityisabletoprocesslearnandmemorizechronicpain
AT biellagabrieleeliseom canlocalcommunityparadigmandepitopologicallearningenhanceourunderstandingofhowlocalbrainconnectivityisabletoprocesslearnandmemorizechronicpain
AT cannistracicarlovittorio canlocalcommunityparadigmandepitopologicallearningenhanceourunderstandingofhowlocalbrainconnectivityisabletoprocesslearnandmemorizechronicpain