Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain

Frontiers in Pain Research

Pain Research

Rachael Miller Neilan¹  Carley Reith¹  Kayla Kraeuter²  Benedict J. Kolber³  Iniya Anandan³  Heather N. Allen⁴ 

[1] Department of Mathematics and Computer Science, Duquesne University, Pittsburgh, PA, United States;Department of Mathematics and Computer Science, Duquesne University, Pittsburgh, PA, United States;Department of Engineering, Duquesne University, Pittsburgh, PA, United States;Department of Neuroscience and Center for Advanced Pain Studies, University of Texas at Dallas, Richardson, TX, United States;Department of Neuroscience and Center for Advanced Pain Studies, University of Texas at Dallas, Richardson, TX, United States;Department of Biological Sciences, Duquesne University, Pittsburgh, PA, United States;

关键词: neuropathic pain;    central nucleus of the amygdala (CeA);    agent-based model (ABM);    computational model;    somatostatin;    protein kinase c delta (PKCδ);    nociplastic pain;   

DOI  :  10.3389/fpain.2023.1183553

 received in 2023-03-10, accepted in 2023-05-05,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
© 2023 Miller Neilan, Reith, Anandan, Kraeuter, Allen and Kolber.