High-resolution (HR) electrical mapping is an important clinical research tool for understanding normal and abnormal gastric electrophysiology. Analyzing velocities of gastric electrical activity in a reliable and accurate manner can provide additional valuable information for quantitatively and qualitatively comparing features across and within subjects, particularly during gastric dysrhythmias. In this study we compared three methods of estimating velocities from HR recordings to determine which method was the most reliable for use with gastric HR electrical mapping. The three methods were i) Simple finite difference ii) Smoothed finite difference and a iii) Polynomial based method. With synthetic data, the accuracy of the simple finite difference method resulted in velocity errors almost twice that of the smoothed finite difference and the polynomial based method, in the presence of activation time error up to 0.5s. With three synthetic cases under various noise types and levels, the smoothed finite difference resulted in average speed error of 3.2% and an average angle error of 2.0° and the polynomial based method had an average speed error of 3.3% and an average angle error of 1.7°. With experimental gastric slow wave recordings performed in pigs, the three methods estimated similar velocities (6.3-7.3 mm/s), but the smoothed finite difference method had a lower standard deviation in its velocity estimate than the simple finite difference and the polynomial based method, leading it to be the method of choice for velocity estimation in gastric slow wave propagation. An improved method for visualizing velocity fields is also presented.

Multi-scale modeling has become a productive strategy for quantifying interstitial cells of Cajal (ICC) network structure-function relationships, but the lack of large-scale ICC network imaging data currently limits modeling progress. The SNESIM (Single Normal Equation Simulation) algorithm was utilized to generate realistic virtual images of small real wild-type (WT) and 5-HT2B-receptor knockout (Htr2b−/−) mice ICC networks. Two metrics were developed to validate the performance of the algorithm: (i) network density, which is the proportion of ICC in the tissue; (ii) connectivity, which reflects the degree of connectivity of the ICC network. Following validation, the SNESIM algorithm was modified to allow variation in the degree of ICC network depletion. ICC networks from a range of depletion severities were generated, and the electrical activity over these networks was simulated. The virtual ICC networks generated by the original SNESIM algorithm were similar to that of their real counterparts. The electrical activity simulations showed that the maximum current density magnitude increased as the network density increased. In conclusion, the SNESIM algorithm is an effective tool for generating realistic virtual ICC networks. The modified SNESIM algorithm can be used with simulation techniques to quantify the physiological consequences of ICC network depletion at various physical scales.

Motility in much of the gastrointestinal (GI) tract is coordinated by an electrical event known as slow waves, and several GI motility disorders are associated with slow wave arrhythmias. The GI smooth muscle cells (SMC) generate contraction, but slow waves are actively regenerated by specialized pacemaker cells called the interstitial cells of Cajal (ICC), which are coupled to the SMC. This unique electrical coupling presents an added layer of complexity to GI electromechanical models, and a major current barrier to further progress is the lack of a framework for ICC-SMC-contraction coupling. In this study, an initial framework for the electromechanical coupling was developed in a 2D model. At each solution step, the slow wave propagation was solved first and the intracellular calcium concentration in the SMC model was related to an adapted tension-extension-calcium relationship to simulate active contraction. With identification of more GI-specific constitutive laws, the ICC-SMC-contraction approach will underpin future GI electromechanical models of health and disease states.

In this study, an automated algorithm was developed to identify the arrhythmic gastric slow wave activity that was recorded using high-resolution mapping technique. The raw signals were processed with a Savitzky-Golay filter, and the slow wave activation times were identified using a threshold varying method and grouped using a region-growing method. Slow wave amplitudes and velocities were calculated for all cycles. Arrhythmic events were identified when the orientation of a slow wave at an electrode exceeded the 95% confidence interval of the averaged orientation of several normal cycles. A second selection criterion was further developed to identify the arrhythmic events by an anisotropy ratio. In both pig and human studies, arrhythmias were associated with the emergence of circumferential velocity components and higher amplitudes.