Improved Efficiency and Sensitivity Analysis of 3-D Agent-based Model for Pain-related Neural Activity in the Amygdala

Neuropathic pain is caused by nerve injury and involves brain areas such as the central nucleus of the amygdala (CeA). We developed the first 3-D agent-based model (ABM) of neuropathic pain-related neurons in the CeA using NetLogo3D. The execution time of a single ABM simulation using realistic parameters (e.g., 13,000 neurons and 22,000+ neural connections) is an important factor in the model’s usability. In this paper, we describe our efforts to improve the computational efficiency of our 3-D ABM, which resulted in a 28% reduction in execution time on average for a typical simulation. With this upgraded model, we performed one- and two-parameter sensitivity analyses to study the sensitivity of model output to variability in several key parameters along the anterior to posterior axis of the CeA. These results highlight the importance of computational modeling in exploring spatial and cell-type specific properties of brain regions to inform future wet lab experiments

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 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