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Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain
Neuropathic and nociplastic pain are major causes of pain and involve brain areas such as the central nucleus of the amygdala (CeA). Within the CeA, neurons expressing protein kinase c-delta (PKCδ) or somatostatin (SST) have opposing roles in pain-like modulation. In this manuscript, we describe our...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270735/ https://www.ncbi.nlm.nih.gov/pubmed/37332477 http://dx.doi.org/10.3389/fpain.2023.1183553 |
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author | Miller Neilan, Rachael Reith, Carley Anandan, Iniya Kraeuter, Kayla Allen, Heather N. Kolber, Benedict J. |
author_facet | Miller Neilan, Rachael Reith, Carley Anandan, Iniya Kraeuter, Kayla Allen, Heather N. Kolber, Benedict J. |
author_sort | Miller Neilan, Rachael |
collection | PubMed |
description | Neuropathic and nociplastic pain are major causes of pain and involve brain areas such as the central nucleus of the amygdala (CeA). Within the CeA, neurons expressing protein kinase c-delta (PKCδ) or somatostatin (SST) have opposing roles in pain-like modulation. In this manuscript, we describe our progress towards developing a 3-D computational model of PKCδ and SST neurons in the CeA and the use of this model to explore the pharmacological targeting of these two neural populations in modulating nociception. Our 3-D model expands upon our existing 2-D computational framework by including a realistic 3-D spatial representation of the CeA and its subnuclei and a network of directed links that preserves morphological properties of PKCδ and SST neurons. The model consists of 13,000 neurons with cell-type specific properties and behaviors estimated from laboratory data. During each model time step, neuron firing rates are updated based on an external stimulus, inhibitory signals are transmitted between neurons via the network, and a measure of nociceptive output from the CeA is calculated as the difference in firing rates of pro-nociceptive PKCδ neurons and anti-nociceptive SST neurons. Model simulations were conducted to explore differences in output for three different spatial distributions of PKCδ and SST neurons. Our results show that the localization of these neuron populations within CeA subnuclei is a key parameter in identifying spatial and cell-type pharmacological targets for pain. |
format | Online Article Text |
id | pubmed-10270735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102707352023-06-16 Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain Miller Neilan, Rachael Reith, Carley Anandan, Iniya Kraeuter, Kayla Allen, Heather N. Kolber, Benedict J. Front Pain Res (Lausanne) Pain Research Neuropathic and nociplastic pain are major causes of pain and involve brain areas such as the central nucleus of the amygdala (CeA). Within the CeA, neurons expressing protein kinase c-delta (PKCδ) or somatostatin (SST) have opposing roles in pain-like modulation. In this manuscript, we describe our progress towards developing a 3-D computational model of PKCδ and SST neurons in the CeA and the use of this model to explore the pharmacological targeting of these two neural populations in modulating nociception. Our 3-D model expands upon our existing 2-D computational framework by including a realistic 3-D spatial representation of the CeA and its subnuclei and a network of directed links that preserves morphological properties of PKCδ and SST neurons. The model consists of 13,000 neurons with cell-type specific properties and behaviors estimated from laboratory data. During each model time step, neuron firing rates are updated based on an external stimulus, inhibitory signals are transmitted between neurons via the network, and a measure of nociceptive output from the CeA is calculated as the difference in firing rates of pro-nociceptive PKCδ neurons and anti-nociceptive SST neurons. Model simulations were conducted to explore differences in output for three different spatial distributions of PKCδ and SST neurons. Our results show that the localization of these neuron populations within CeA subnuclei is a key parameter in identifying spatial and cell-type pharmacological targets for pain. Frontiers Media S.A. 2023-05-30 /pmc/articles/PMC10270735/ /pubmed/37332477 http://dx.doi.org/10.3389/fpain.2023.1183553 Text en © 2023 Miller Neilan, Reith, Anandan, Kraeuter, Allen and Kolber. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) 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 | Pain Research Miller Neilan, Rachael Reith, Carley Anandan, Iniya Kraeuter, Kayla Allen, Heather N. Kolber, Benedict J. Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain |
title | Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain |
title_full | Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain |
title_fullStr | Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain |
title_full_unstemmed | Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain |
title_short | Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain |
title_sort | developing a 3-d computational model of neurons in the central amygdala to understand pharmacological targets for pain |
topic | Pain Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270735/ https://www.ncbi.nlm.nih.gov/pubmed/37332477 http://dx.doi.org/10.3389/fpain.2023.1183553 |
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