Detailed measurements of gastric electrical activity and their implications on inverse solutions

Polygonally Meshed Dipole Model Simulation of the Electrical Field Produced by the Stomach and Intestines

Cutaneous electrogastrography (EGG) is used in clinical and physiological fields to noninvasively measure the electrical activity of the stomach and intestines. Dipole models that mathematically express the electrical field characteristics generated by the stomach and intestines have been developed to investigate the relationship between the electrical control activity (ECA) (slow waves) shown in EGG and the internal gastric electrical activity. However, these models require a mathematical description of the movement of an annular band of dipoles, which limits the shape that can be modeled. In this study, we propose a novel polygonally meshed dipole model to conveniently reproduce ECA based on the movement of the annular band in complex shapes, such as the shape of the stomach and intestines, constructed in three-dimensional (3D) space. We show that the proposed model can reproduce ECA simulation results similar to those obtained using conventional models. Moreover, we show that the proposed model can reproduce the ECA produced by a complex geometrical shape, such as the shape of the intestines. The study results indicate that ECA simulations can be conducted based on structures that more closely resemble real organs than those used in conventional dipole models, with which, because of their intrinsic construction, it would be difficult to include realistic complex shapes, using the mathematical description of the movement of an annular band of dipoles. Our findings provide a powerful new approach for computer simulations based on the electric dipole model.

Bayesian inverse methods for spatiotemporal characterization of gastric electrical activity from cutaneous multi-electrode recordings

Gastrointestinal (GI) problems give rise to 10 percent of initial patient visits to their physician. Although blockages and infections are easy to diagnose, more than half of GI disorders involve abnormal functioning of the GI tract, where diagnosis entails subjective symptom-based questionnaires or objective but invasive, intermittent procedures in specialized centers. Although common procedures capture motor aspects of gastric function, which do not correlate with symptoms or treatment response, recent findings with invasive electrical recordings show that spatiotemporal patterns of the gastric slow wave are associated with diagnosis, symptoms, and treatment response. We here consider developing non-invasive approaches to extract this information. Using CT scans from human subjects, we simulate normative and disordered gastric surface electrical activity along with associated abdominal activity. We employ Bayesian inference to solve the ill-posed inverse problem of estimating gastric surface activity from cutaneous recordings. We utilize a prior distribution on the spatiotemporal activity pertaining to sparsity in the number of wavefronts on the stomach surface, and smooth evolution of these wavefronts across time. We implement an efficient procedure to construct the Bayes optimal estimate and demonstrate its superiority compared to other commonly used inverse methods, for both normal and disordered gastric activity. Region-specific wave direction information is calculated and consistent with the simulated normative and disordered cases. We apply these methods to cutaneous multi-electrode recordings of two human subjects with the same clinical description of motor function, but different diagnosis of underlying cause. Our method finds statistically significant wave propagation in all stomach regions for both subjects, anterograde activity throughout for the subject with diabetic gastroparesis, and retrograde activity in some regions for the subject with idiopathic gastroparesis. These findings provide a further step towards towards non-invasive phenotyping of gastric function and indicate the long-term potential for enabling population health opportunities with objective GI assessment.

A Theoretical Analysis of Electrogastrography (EGG) Signatures Associated With Gastric Dysrhythmias

Routine screening and accurate diagnosis of chronic gastrointestinal motility disorders represent a significant problem in current clinical practice. The electrogastrography (EGG) provides a noninvasive option for assessing gastric slow waves, as a means of diagnosing gastric dysrhythmias, but its uptake in motility practice has been limited partly due to an incomplete sensitivity and specificity. This paper presents the development of a human whole-organ gastric model to enable virtual (in silico) testing of gastric electrophysiological dispersion in order to improve the diagnostic accuracy of EGG. The model was developed to simulate normal gastric slow wave conduction as well as three types of dysrhythmias identified in recent high-resolution gastric mapping studies: conduction block, re-entry, and ectopic pacemaking. The stomach simulations were then applied in a torso model to identify predicted EGG signatures of normal and dysrhythmic slow wave profiles. The resulting EGG data were compared using percentage differences and correlation coefficients. Virtual EGG channels that demonstrated a percentage difference over 100% and a correlation coefficient less than ±0.2 (threshold relaxed to ±0.5 for the ectopic pacemaker case) were further investigated for their specific distinguishing features. In particular, anatomical locations from the epigastric region and specific channel configurations were identified that could be used to clinically diagnose the three classes of human gastric dysrhythmia. These locations and channels predicted by simulations present a promising methodology for improving the clinical reliability and applications of EGG.

Multiscale modeling of gastrointestinal electrophysiology and experimental validation

Normal gastrointestinal (GI) motility results from the coordinated interplay of multiple cooperating mechanisms, both intrinsic and extrinsic to the GI tract. A fundamental component of this activity is an omnipresent electrical activity termed slow waves, which is generated and propagated by the interstitial cells of Cajal (ICCs). The role of ICC loss and network degradation in GI motility disorders is a significant area of ongoing research. This review examines recent progress in the multiscale modeling framework for effectively integrating a vast range of experimental data in GI electrophysiology, and outlines the prospect of how modeling can provide new insights into GI function in health and disease. The review begins with an overview of the GI tract and its electrophysiology, and then focuses on recent work on modeling GI electrical activity, spanning from cell to body biophysical scales. Mathematical cell models of the ICCs and smooth muscle cell are presented. The continuum framework of monodomain and bidomain models for tissue and organ models are then considered, and the forward techniques used to model the resultant body surface potential and magnetic field are discussed. The review then outlines recent progress in experimental support and validation of modeling, and concludes with a discussion on potential future research directions in this field.

Characterization of gastric electrical activity using magnetic field measurements: a simulation study

Gastric disorders are often associated with abnormal propagation of gastric electrical activity (GEA). The identification of clinically relevant parameters of GEA using noninvasive measures would therefore be highly beneficial for clinical diagnosis. While magnetogastrograms (MGG) are known to provide a noninvasive representation of GEA, standard methods for their analysis are limited. It has previously been shown in simplistic conditions that the surface current density (SCD) calculated from multichannel MGG measurements provides an estimate of the gastric source location and propagation velocity. We examine the accuracy of this technique using more realistic source models and an anatomically realistic volume conductor model. The results showed that the SCD method was able to resolve the GEA parameters more reliably when the dipole source was located within 100 mm of the sensor. Therefore, the theoretical accuracy of SCD method would be relatively diminished for patients with a larger body habitus, and particularly in those patients with significant truncal obesity. However, many patients with gastric motility disorders are relatively thin due to food intolerance, meaning that the majority of the population of gastric motility patients could benefit from the methods developed here. Large errors resulted when the source was located deep within the body due to the distorting effects of the secondary sources on the magnetic fields. Larger errors also resulted when the dipole was oriented normal to the sensor plane. This was believed to be due to the relatively small contribution of the dipole source when compared to the field produced by the volume conductor. The use of three orthogonal magnetic field components rather than just one component to calculate the SCD yielded marginally more accurate results when using a realistic dipole source. However, this slight increase in accuracy may not warrant the use of more complex vector channels in future superconducting quantum interference device designs. When multiple slow waves were present in the stomach, the SCD map contained only one maximum point corresponding to the more dominant source located in the distal stomach. Parameters corresponding to the slow wave in the proximal stomach were obtained once the dominant slow terminated at the antrum. Additional validation studies are warranted to address the utility of the SCD method to resolve parameters related to gastric slow waves in a clinical setting.