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Abstract

Chronic bladder pain evokes asymmetric behavior in neurons across the left and right hemispheres of the amygdala. An agent-based computational model was created to simulate the firing of neurons over time and in response to painful bladder stimulation. Each agent represents one neuron and is characterized by its location in the amygdala and response type (excited or inhibited). At each time step, the firing rates (Hz) of all neurons are stochastically updated from probability distributions estimated from data collected in laboratory experiments. A damage accumulation model tracks the damage accrued by neurons during long-term, painful bladder stimulation. Emergent model output uses neural activity to measure temporal changes in pain attributed to bladder stimulation. Simulations demonstrate the model's ability to capture acute and chronic pain and its potential to predict changes in pain similar to those observed in the lab. Asymmetric neural activity during the progression of chronic pain is examined using model output and a sensitivity analysis.

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