A biophysically based finite-state machine model for analyzing gastric experimental entrainment and pacing recordings.

Gastrointestinal motility is coordinated by slow waves (SWs) generated by the interstitial cells of Cajal (ICC). Experimental studies have shown that SWs spontaneously activate at different intrinsic frequencies in isolated tissue, whereas in intact tissues they are entrained to a single frequency. Gastric pacing has been used in an attempt to improve motility in disorders such as gastroparesis by modulating entrainment, but the optimal methods of pacing are currently unknown. Computational models can aid in the interpretation of complex in vivo recordings and help to determine optimal pacing strategies. However, previous computational models of SW entrainment are limited to the intrinsic pacing frequency as the primary determinant of the conduction velocity, and are not able to accurately represent the effects of external stimuli and electrical anisotropies. In this paper, we present a novel computationally efficient method for modeling SW propagation through the ICC network while accounting for conductivity parameters and fiber orientations. The method successfully reproduced experimental recordings of entrainment following gastric transection and the effects of gastric pacing on SW activity. It provides a reliable new tool for investigating gastric electrophysiology in normal and diseased states, and to guide and focus future experimental studies.