Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
Abstract 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 netwo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2017-08-01
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| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s41109-017-0048-x |
| _version_ | 1811318488945393664 |
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| author | Vaibhav Narula Antonio Giuliano Zippo Alessandro Muscoloni Gabriele Eliseo M. Biella Carlo Vittorio Cannistraci |
| author_facet | Vaibhav Narula Antonio Giuliano Zippo Alessandro Muscoloni Gabriele Eliseo M. Biella Carlo Vittorio Cannistraci |
| author_sort | Vaibhav Narula |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T12:27:03Z |
| format | Article |
| id | doaj.art-7efacdd52bf947c697a8c6ace4c368e4 |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-04-13T12:27:03Z |
| publishDate | 2017-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-7efacdd52bf947c697a8c6ace4c368e42022-12-22T02:46:59ZengSpringerOpenApplied Network Science2364-82282017-08-012112810.1007/s41109-017-0048-xCan local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?Vaibhav Narula0Antonio Giuliano Zippo1Alessandro Muscoloni2Gabriele Eliseo M. Biella3Carlo Vittorio Cannistraci4Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, Technische Universität DresdenInstitute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle RicercheBiomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, Technische Universität DresdenInstitute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle RicercheBiomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, Technische Universität DresdenAbstract 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.http://link.springer.com/article/10.1007/s41109-017-0048-xNetwork topologyTopological measuresLocal-community-paradigmBrain connectivityPain markersComputational neuroscience |
| spellingShingle | Vaibhav Narula Antonio Giuliano Zippo Alessandro Muscoloni Gabriele Eliseo M. Biella Carlo Vittorio Cannistraci Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain? Applied Network Science Network topology Topological measures Local-community-paradigm Brain connectivity Pain markers Computational neuroscience |
| 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 | Network topology Topological measures Local-community-paradigm Brain connectivity Pain markers Computational neuroscience |
| url | http://link.springer.com/article/10.1007/s41109-017-0048-x |
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