PROJECT SUMMARY/ABSTRACT
Neuropathic pain impacts the lives of millions of individuals worldwide. Understanding the mechanisms that
drive the development and maintenance of neuropathic pain is critical to the advancement of next generation
therapeutic strategies. In the context of nerve injury, one brain area that may contribute to pain is the central
nucleus of the amygdala (CeA). Evidence from human patients and animal models shows short and long-term
changes in the amygdala may contribute to the overall pathologic state. An explosion of cell-type specific
optogenetic, chemogenetic, and physiological approaches has provided unprecedented cellular access to the
CeA in the context of injury. A major challenge for the field is determining how to integrate data from different
approaches and laboratories to understand the amygdala’s contribution to nociception and pain. We recently
developed the first computational model of the CeA in the context of pain using real-world cell-type data. This
model, built with physiological data and validated against in vivo results, provides a robust framework to study
the interactions between different cell types and their collective contributions to the development of pain. Our
model of the CeA marks a major step forward in the study of neuropathic pain, but at the same time the model
is still primitive in its assumptions. The model represents the CeA in 2-dimensional space and fails to
accurately capture the complex 3-dimensional (3-D) structural properties of the CeA and its subnuclei that
likely drive pronociceptive and antinociceptive outputs from the CeA. The objective of this proposal is to utilize
existing and new wet-lab data to build a proof-of-concept 3-D model of pain-related neurons in the CeA that
accounts for spatial and cell-type heterogeneity and determine the extent to which the model can predict in
vivo results. Our central hypothesis is that the 3-D distribution of two specific cell types in the CeA influences
the appropriate behavioral responses in the context of neuropathic injury and that this distribution can be
modeled to produce novel insights into the CeA’s role in the development of chronic nociception. We will use an
agent-based computational model to describe the physiological and morphological properties of individual
neurons in the CeA and their interactions with one another. This approach will allow us to capture complexity
within the CeA system while remaining accessible to undergraduate students, including those new to
programming. The computational model, which will be publicly available and open source, will include a
graphical user interface that can be accessed by any researcher to complete in silico experiments prior to
devoting the time and resources to costly in vivo experiments. Both computational and wet-lab experiments are
incorporated in this proposal with a focus on involvement of undergraduate students to bolster their
development as rigorous, interdisciplinary, and thoughtful biomedical researchers.
