Conference Agenda

Background:

Gastric slow waves, generated and propagated by interstitial cells of Cajal (ICC), coordinate stomach motility and are essential for healthy digestion. Dysrhythmic slow wave activity is increasingly recognized as a biomarker for motility disorders such as gastroparesis and functional dyspepsia. Multiscale modeling of gastric electrophysiology linking structural, tissue, and organ-level dynamics enhances our understanding of gastric motility mechanisms and offers a pathway toward elucidating pathological changes and enabling non-invasive diagnostics.

Methods:

A combination of multiscale experimental and computational techniques was employed to investigate how ICC microstructure affects slow wave dynamics, and how these dynamics manifest at the organ and far-field levels. Detailed ICC networks in murine gastric tissues were imaged using multiphoton confocal microscopy. Region-specific continuum models of ICC networks in the antrum and corpus were constructed using machine learning-based segmentation and ICC density and morphological metrics were quantified. The impact of ICC structures on slow wave propagation was investigated using a self-excitatory ICC cell model. At the organ level, anatomically realistic stomach models were developed, and simulations of normal and dysrhythmic slow wave patterns informed by in-vivo high-resolution gastric mapping were performed. Additionally, forward simulations of biomagnetic fields were conducted using volume current source formulations. Simulated biomagnetic signals were compared against experimental recordings from porcine models undergoing simultaneous high-resolution serosal mapping and biomagnetic acquisition. Finally, an anatomically-constrained source imaging method was developed to map biomagnetic signals back to the stomach surface and reconstruct slow wave activation sequences.

Results:

Microscopic analysis revealed significant regional differences in ICC density and thickness across the stomach. The antrum exhibited the highest ICC density (13.0±5.1%), followed by the corpus (6.0±1.9%) and fundus (1.5±0.5%), which lacked myenteric plexus ICCs. These variations produced marked effects on slow wave speed and synchrony, with the antrum supporting faster, more coherent propagation. In the forward modeling analysis, simulated biomagnetic signals based on serosal activation patterns reproduced key features of experimental data, including waveform morphology and spatial distribution. Anatomically-based source imaging accurately reconstructed slow wave propagation patterns including simultaneous ectopic and normal wavefronts with mean localization errors within 6–14% of the longitudinal axis of the stomach.

Conclusion:

This multiscale framework, linking ICC structure to tissue-level slow wave behavior and far-field biomagnetic signatures, provides a foundation for non-invasive diagnosis of gastric dysrhythmias. The validated source imaging approach enables spatially resolved estimation of slow wave propagation from surface biomagnetic recordings offering a novel non-invasive clinical biomarker for the diagnosis of gastric motility disorders.

The stomach plays a fundamental role in digestion through accommodation, mechanical mixing, and chemical breakdown of food. Gastric peristalsis, a coordinated process of muscular contractions, ensures the efficient mixing and propulsion of food for subsequent digestion and absorption in the intestines. These contractions are governed by a complex electromechanical system, making a detailed understanding of gastric motility essential for diagnosing and treating gastrointestinal disorders such as gastroparesis, gastroesophageal reflux disease, and dyspepsia.

Despite its physiological importance, gastric biomechanics remains poorly understood, both in terms of biophysical mechanisms and computational modeling. While computational modeling has emerged as a promising, non-invasive tool for studying biomechanical systems, particularly when informed by medical imaging, there remains a scarcity of robust, physiologically accurate models that comprehensively capture the biomechanical complexity of gastric motility. Whole-stomach electromechanical models will provide valuable insights. However, whole-stomach models face critical computational challenges, including large deformations in nonlinear domains and patient-specific anatomical variability.

To address these challenges, we present a robust computational multiphysics framework for modeling human gastric motility using patient-specific geometries. Our approach integrates key biomechanical factors, including prestress, regional material properties, and non-uniform boundary conditions. Additionally, it incorporates an anisotropic active strain formulation within a homogenized constrained mixture framework, enabling more accurate simulations of structural and material heterogeneities in gastric biomechanics. Although patient-specific geometries can be reconstructed from medical imaging, crucial biomechanical properties—such as smooth muscle and collagen fiber orientations and the regional distribution of material parameters—are challenging to obtain directly. To overcome this, we introduce a novel workflow leveraging Laplace-Dirichlet-based reparameterization to infer physiological principal fiber orientations and spatial material distributions.

The proposed computational model successfully replicates essential features of gastric electromechanics, including slow wave entrainment and the physiologically accurate propagation of ring-shaped peristaltic contraction waves with large deformations. These findings emphasize the intricate coupling between electrical excitation and large-scale nonlinear tissue deformation. Moreover, the model’s adaptability to patient-specific geometries allows for precise representation of anatomical and physiological variations, which is crucial for understanding individual differences in gastric motility.

By integrating computational techniques with patient-specific imaging data, our framework enables large-scale in-silico studies of gastric motility. This approach provides a powerful tool for improving diagnostic accuracy, advancing the understanding of gastric motility disorders, and developing personalized treatment strategies. Ultimately, this work bridges the gap between computational modeling and clinical practice, offering a pathway toward more precise, patient-tailored interventions for disorders such as gastroparesis, gastroesophageal reflux disease, and dyspepsia.

Introduction: Contractions of the smooth muscles throughout the gastrointestinal (GI) tract facilitate digestion. These contractions are coordinated by rhythmic depolarisations of the smooth muscles, known as slow waves. Impairments to these electrical events are associated with a variety of debilitating motility disorders such as gastroparesis and functional dyspepsia. Gastric pacing is a potential therapy to restore normal slow wave function, similar to how cardiac pacemakers regulate heart rhythms. To investigate slow wave physiology and potential treatments, researchers often rely on acute animal studies under anaesthesia, which provide controlled environments for precise physiological measurements. However, different anaesthetic agents may influence gastric slow wave activity in distinct ways, potentially affecting experimental outcomes. Two common anaesthetic agents are: isoflurane, a widely used volatile anaesthetic, and propofol, a newer intravenous agent gaining broader use. This study compares their effects on gastric slow waves and examines how these differences impact the evaluation of gastric pacing as a therapeutic approach.

Methods: With ethical approval, experiments were conducted on pigs anesthetised with either isoflurane or propofol. To study the slow wave response, surface-contact electrode arrays were placed on the mid-corpus of the stomach, covering both the anterior (front-facing) and posterior (back-facing) surfaces (64 electrodes each side, 5 mm inter-electrode spacing). These surfaces are separated by the greater curvature and the lesser curvature. Following a baseline recording period, pacing was applied using a pulse-width of 400 ms and pulse-amplitudes of 5 mA at 1.1 times the intrinsic slow wave frequency. Slow waves were characterised by their period, amplitude, speed, and spatial propagation patterns, while pacing efficacy was assessed by measuring the rate of spatial entrainment under propofol and isoflurane anaesthesia.

Results: Ordered propagation of slow waves in the antegrade direction was observed across both the anterior and posterior surfaces with 86% symmetry under propofol, while isoflurane resulted in more dynamic propagation patterns and significantly less symmetry (25%, p=0.0187). Baseline slow wave period (18.8±5.1 vs 28.1±14.3 s, p=0.016), amplitude (1.5±0.7 vs 0.7±0.4 mV, p=0.002), and speed (4.4±1.1 vs 3.5±0.7 mm/s, p=0.018) differed significantly between propofol and isoflurane groups, respectively. When evaluating the efficacy of pacing under different anaesthesia, pigs anaesthetised with propofol achieved a spatial entrainment success rate of 83% compared to 57% with isoflurane.

Conclusion: Different anaesthetic agents affect gastric slow waves differently. Propofol preserves more regular activity, while isoflurane is associated with a greater proportion of disordered patterns. This contrast likely stems from their distinct mechanisms of action, with propofol providing targeted sedation and isoflurane exerting broader systemic effects, potentially including on the GI tract. Accounting for these differences is crucial when using acute models to study GI electrophysiology. These differences in anaesthetic effects can be leveraged to study diagnostic and therapeutic interventions where propofol provides a model for normal, ordered slow wave activity, while isoflurane can simulate dysrhythmic states, offering valuable insights into disease mechanisms and potential treatments.

Uterine peristalsis plays an important role in embryo implantation and retention, potentially impacting pregnancy outcomes in assisted reproductive technologies. Previous studies have demonstrated uterine contractions during embryo transfer, but their predictive value for pregnancy outcomes remains unclear. Electrophysiological imaging systems, such as uterine peristalsis imaging (UPI), have allowed non-invasive characterization of uterine peristalsis, though few studies have explored its potential as a predictive biomarker for FET outcomes.

This observational study involved 18 patients undergoing a single blastocyst FET cycle between January to October 2024. Uterine peristalsis was assessed on the day of transfer using a non-invasive electrophysiologic imaging system immediately prior to the FET. Of the 18 patients enrolled, 12 became pregnant and 6 did not following the FET.

Eighteen patients undergoing a single blastocyst FET cycle at an academic fertility clinic were enrolled. Uterine peristalsis was assessed using transabdominal ultrasound for anatomical mapping followed by the application of electrode patches to record electrical signals for up to 30 minutes. UPI software derived peristalsis characteristics (frequency, magnitude, and ratios of C-F and F-C motion). Statistical analysis compared these variables between pregnant and non-pregnant groups using the Mann-Whitney U test.

Both the pregnant (n=12) and non-pregnant (n=6) groups exhibited similar peristalsis frequencies per minute (2.08±0.48 vs 2.07±0.27, p=0.47). No significant differences were observed in the magnitude of uterine peristalsis in either the C-F or F-C directions (p=0.1 and p=0.23, respectively). However, the F-C ratio was significantly higher in the non-pregnant group (0.65±0.13) compared with the pregnant group (0.37±0.12, p<0.02). Conversely, the C-F ratio was significantly higher in the pregnant group (0.63±0.11) compared with the non-pregnant group (0.35±0.14, p<0.02).

Uterine peristalsis characteristics, particularly cervix-to-fundus (C-F) and fundus-to-cervix (F-C) ratios, significantly differ between patients who do and do not achieve pregnancy after FET. The small sample size limits the generalizability of these findings. Additionally, the observational nature of the study does not allow for establishing causality between uterine peristalsis and pregnancy outcomes.

The findings indicate that uterine peristalsis characteristics, particularly the fundus-to-cervix and cervix-to-fundus UP, may serve as a non-invasive biomarker for predicting pregnancy success in FET cycles. Further research is needed to confirm these results and explore clinical applicability.