Predicting Pain Using a Data-Driven Agent-Based Model of the Bilateral Central Amygdala.

Sensory processing in the amygdala is a complex, dynamic process. Decades of surgical, electrical, pharmacological, optogenetic, and chemogenetic in vivo manipulations have revealed the nociceptive functions of anatomically- and genetically-restricted neuronal populations. In parallel, molecular and electrophysiological approaches have allowed for high-resolution, temporal examination of nociceptive-induced alterations in amygdala plasticity. Computational integration of this data is critical for future therapeutic development; in practice, these models would allow for in silico prediction of amygdala activity following injury, and in a reciprocal fashion, changes in pain-like behaviors following manipulation of discrete amygdala neuronal populations. To this end, we developed a three-dimensional computer model of the bilateral central nucleus of the amygdala (CeA). We employed agent-based modelling to integrate wet-lab data from two CeA cell populations: Calcitonin Gene-Related Peptide Receptor (CGRPR; Calcrl) expressing cells and Protein Kinase C delta (PKCδ; Prkcd) expressing cells. We integrated the spatial location, connectivity, neuronal activity, and electrophysiological properties of these neurons in our realistic bilateral model architecture. Our model captures properties of the amygdala that drive pain modulation, including hemisphere-specific physiological differences, and generates predictions of nociception related to bladder injury. Predictions from the model were compared retrospectively to pain outcomes during manipulation of CGRPR-expressing neurons in whole mice. These comparisons show strong alignment between our model and in vivo outcomes.