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An understanding how neurological disorders lead to mechanical dysfunction of the esophagus requires knowledge of the neural circuit of the enteric nervous system. Historically, this has been elusive. Here, we present an empirically guided neural circuit for the esophagus. It has a chain of unidirectionally coupled relaxation oscillators, receiving excitatory signals from stretch receptors along the esophagus. The resulting neuromechanical model reveals complex patterns and behaviors that emerge from interacting components in the system. A wide variety of clinically observed normal and abnormal esophageal responses to distension are successfully predicted. Specifically, repetitive antegrade contractions (RACs) are conclusively shown to emerge from the coupled neuromechanical dynamics in response to sustained volumetric distension. Normal RACs are shown to have a robust balance between excitatory and inhibitory neuronal populations, and the mechanical input through stretch receptors. When this balance is affected, contraction patterns akin to motility disorders are observed. For example, clinically observed repetitive retrograde contractions emerge due to a hyper stretch sensitive wall. Such neuromechanical insights could be crucial to eventually develop targeted pharmacological interventions.

Certain biological systems exhibit both direct and retrograde propagating wave signals, despite unidirectional neural coupling. However, there is no model to explain this. Therefore, the underlying physics of reversing the signal's direction for one-way coupling remains unclear. Here, we resolve this issue using a Wilson-Cowan oscillators network. By analyzing the limit cycle period of various coupling configurations, we determine that intrinsic frequency differences among oscillators control wave directionality.

Functional luminal imaging probe (FLIP) is used to measure cross-sectional area (CSA) and pressure at sphincters. It consists of a catheter surrounded by a fluid filled cylindrical bag, closed on both ends. Plotting the pressure-CSA hysteresis of a sphincter during a contraction cycle, which is available through FLIP testing, offers information on its functionality, and can provide diagnostic insights. However, limited work has been done to explain the mechanics of these pressure-CSA loops. This work presents a consolidated picture of pressure-CSA loops of different sphincters. Clinical data reveal that although sphincters have a similar purpose (controlling the flow of liquids and solids by opening and closing), two different pressure-CSA loop patterns emerge: negative slope loop (NSL) and positive slope loop (PSL). We show that the loop type is the result of an interplay between (or lack thereof) two mechanical modes: (i) neurogenic mediated relaxation of the sphincter muscle and (ii) muscle contraction proximal to the sphincter which causes mechanical distention. We conclude that sphincters which only function through mechanism (i) exhibition NSL whereas sphincters which open as a result of both (i) and (ii) display a PSL. This work provides a fundamental mechanical understanding of human sphincters. This can be used to identify normal and abnormal phenotypes for the different sphincters and help in creating physiomarkers based on work calculation.

Dynamic magnetic resonance imaging (MRI) is a popular medical imaging technique to generate image sequences of the flow of a contrast material inside tissues and organs. However, its application to imaging bolus movement through the esophagus has only been demonstrated in few feasibility studies and is relatively unexplored. In this work, we present a computational framework called mechanics-informed MRI (MRI-MECH) that enhances that capability thereby increasing the applicability of dynamic MRI for diagnosing esophageal disorders. Pineapple juice was used as the swallowed contrast material for the dynamic MRI and the MRI image sequence was used as input to the MRI-MECH. The MRI-MECH modeled the esophagus as a flexible one-dimensional tube and the elastic tube walls followed a linear tube law. Flow through the esophagus was then governed by one-dimensional mass and momentum conservation equations. These equations were solved using a physics-informed neural network (PINN). The PINN minimized the difference between the measurements from the MRI and model predictions ensuring that the physics of the fluid flow problem was always followed. MRI-MECH calculated the fluid velocity and pressure during esophageal transit and estimated the mechanical health of the esophagus by calculating wall stiffness and active relaxation. Additionally, MRI-MECH predicted missing information about the lower esophageal sphincter during the emptying process, demonstrating its applicability to scenarios with missing data or poor image resolution. In addition to potentially improving clinical decisions based on quantitative estimates of the mechanical health of the esophagus, MRI-MECH can also be enhanced for application to other medical imaging modalities to enhance their functionality as well., Comment: 21 pages, 15 figures

The esophagogastric junction (EGJ) is located at the distal end of the esophagus and acts as a valve allowing swallowed materials to enter the stomach and preventing acid reflux. Irregular weakening or stiffening of the EGJ muscles result in changes to its opening and closing patterns which can progress into esophageal disorders. Therefore, understanding the physics behind the opening and closing cycle of the EGJ provides a mechanistic insight into its function and can help identify the underlying conditions that cause its degradation. Using clinical FLIP data, we plotted the pressure-area hysteresis at the EGJ location and distinguished two major loop types, a pressure dominant loop (PDL) and a tone dominant loop (TDL). In this study, we aimed to identify the key characteristics that define each loop type and find what causes the inversion from one loop to another. To do so, the clinical observations were reproduced using 1D simulations of flow inside a FLIP device located in the esophagus, and the work done by the EGJ wall over time was calculated. This work was decomposed into active and passive components, which revealed the competing mechanisms that dictate the loop type. These mechanisms are esophagus stiffness, fluid viscosity, and the EGJ relaxation pattern. In PDL, the leading source of energy in the cycle is coming from the fluid pressure increase from the peristaltic contraction wave, and in TDL the leading source of energy in the cycle is coming from the contraction and relaxation of the EGJ tone.

A FLIP device gives cross-sectional area along the length of the esophagus and one pressure measurement, both as a function of time. Deducing mechanical properties of the esophagus including wall material properties, contraction strength, and wall relaxation from these data is a challenging inverse problem. Knowing mechanical properties can change how clinical decisions are made because of its potential for in-vivo mechanistic insights. To obtain such information, we conducted a parametric study to identify peristaltic regimes by using a 1D model of peristaltic flow through an elastic tube closed on both ends and also applied it to interpret clinical data. The results gave insightful information about the effect of tube stiffness, fluid/bolus density and contraction strength on the resulting esophagus shape through quantitive representations of the peristaltic regimes. Our analysis also revealed the mechanics of the opening of the contraction area as a function of bolus flow resistance. Lastly, we concluded that peristaltic driven flow displays three modes of peristaltic geometries, but all physiologically relevant flows fall into two peristaltic regimes characterized by a tight contraction.

The pathogenesis of esophageal disorders is related to the esophageal wall mechanics. Therefore, to understand the underlying fundamental mechanisms behind various esophageal disorders, it is crucial to map the esophageal wall mechanics-based parameters onto physiological and pathophysiological conditions corresponding to altered bolus transit and supraphysiologic IBP. In this work, we present a hybrid framework that combines fluid mechanics and machine learning to identify the underlying physics of the various esophageal disorders and maps them onto a parameter space which we call the virtual disease landscape (VDL). A one-dimensional inverse model processes the output from an esophageal diagnostic device called endoscopic functional lumen imaging probe (EndoFLIP) to estimate the mechanical "health" of the esophagus by predicting a set of mechanics-based parameters such as esophageal wall stiffness, muscle contraction pattern and active relaxation of esophageal walls. The mechanics-based parameters were then used to train a neural network that consists of a variational autoencoder (VAE) that generates a latent space and a side network that predicts mechanical work metrics for estimating esophagogastric junction motility. The latent vectors along with a set of discrete mechanics-based parameters define the VDL and form clusters corresponding to the various esophageal disorders. The VDL not only distinguishes different disorders but can also be used to predict disease progression in time. Finally, we also demonstrate the clinical applicability of this framework for estimating the effectiveness of a treatment and track patient condition after a treatment., Comment: 26 pages, 17 figures

Over the past few decades, in silico modeling of organ systems has significantly furthered our understanding of their physiology and biomechanical function. In this work, we present a detailed numerical model of the upper gastrointestinal (GI) tract that not only accounts for the fiber architecture of the muscle walls, but also the multiphasic components they help transport during normal digestive function. Construction details for 3D models of representative stomach geometry are presented along with a simple strategy for assigning circular and longitudinal muscle fiber orientations for each layer. Based on our previous work that created a fully resolved model of esophageal peristalsis, we extend the same principles to simulate gastric peristalsis by systematically activating muscle fibers embedded in the stomach. Following this, for the first time, we simulate gravity driven bolus emptying into the stomach due to density differences between ingested contents and fluid contents of the stomach. This detailed computational model of the upper gastrointestinal tract provides a foundation on which future models can be based that seek to investigate the biomechanics of acid reflux and probe various strategies for gastric bypass surgeries to address the growing problem of adult obesity.

Fluoroscopy is a radiographic procedure for evaluating esophageal disorders such as achalasia, dysphasia and gastroesophageal reflux disease (GERD). It performs dynamic imaging of the swallowing process and provides anatomical detail and a qualitative idea of how well swallowed fluid is transported through the esophagus. In this work, we present a method called mechanics informed fluoroscopy (FluoroMech) that derives patient-specific quantitative information about esophageal function. FluoroMech uses a Convolutional Neural Network to perform segmentation of image sequences generated from the fluoroscopy, and the segmented images become input to a one-dimensional model that predicts the flow rate and pressure distribution in fluid transported through the esophagus. We have extended this model by developing a FluoroMech reference model to identify and estimate potential physiomarkers such as esophageal wall stiffness and active relaxation ahead of the peristaltic wave in the esophageal musculature. FluoroMech requires minimal computational time, and hence can potentially be applied clinically in the diagnosis of esophageal disorders., Comment: 18 pages, 14 figures

Balloon dilation catheters are often used to quantify the physiological state of peristaltic activity in tubular organs and comment on their ability to propel fluid which is important for healthy human function. To fully understand this system's behavior, we analyzed the effect of a solitary peristaltic wave on a fluid-filled elastic tube with closed ends. A reduced order model that predicts the resulting tube wall deformations, flow velocities and pressure variations is presented. This simplified model is compared with detailed fluid-structure 3D immersed boundary simulations of peristaltic pumping in tube walls made of hyperelastic material. The major dynamics observed in the 3D simulations were also displayed by our 1D model under laminar flow conditions. Using the 1D model, several pumping regimes were investigated and presented in the form of a regime map that summarizes the system's response for a range of physiological conditions. Finally, the amount of work done during a peristaltic event in this configuration was defined and quantified. The variation of elastic energy and work done during pumping was found to have a unique signature for each regime. An extension of the 1D model is applied to enhance patient data collected by the device and find the work done for a typical esophageal peristaltic wave. This detailed characterization of the system's behavior aids in better interpreting the clinical data obtained from dilation catheters. Additionally, the pumping capacity of the esophagus can be quantified for comparative studies between disease groups., Comment: Paper was updated based on comments. Some content was moved to supplementary materials section to reduce the length of the paper

In this work, we extend our previous esophageal transport model using an immersed boundary (IB) method with discrete fiber-based structures, to one using a continuum mechanics-based model that is approximated based on finite elements (IB-FE). To deal with the leakage of flow when the Lagrangian mesh becomes coarser than the fluid mesh, we employ adaptive interaction quadrature points for Lagrangian-Eulerian interaction equations based on a previous work. In particular, we introduce a new anisotropic adaptive interaction quadrature rule. The new rule permits us to vary the interaction quadrature points not only at each time-step and element but also at different orientations per element. For the material model, we extend our previous fiber-based model to a continuum-based model. We first study a case in which a three-dimensional short tube is dilated. Results match very well with those from the implicit FE method. We remark that in our IB-FE case, the three-dimensional tube undergoes a very large deformation and the Lagrangian mesh-size becomes about 6 times of Eulerian mesh-size. To validate the method in handling fiber-matrix material models, we perform a second study on dilating a long fiber-reinforced tube. Errors are small when we compare numerical solutions with analytical solutions. The technique is then applied to the problem of esophageal transport. We present three cases that differ in the material model and muscle fiber architecture. The overall transport features are consistent with those from the previous model. We remark that the continuum-based model can handle more realistic and complicated material behavior. This is demonstrated in our third case with spatially varying fiber architecture. We find this unique muscle fiber architecture could generate a so-called pressure transition zone. This suggests an important role of muscle fiber architecture in esophageal transport., Comment: 47 pages, 17 figures

Esophageal transport is a physiological process that mechanically transports an ingested food bolus from the pharynx to the stomach via the esophagus, a multi-layered muscular tube. This process involves interactions between the bolus, the esophagus, and the neurally coordinated activation of the esophageal muscles. In this work, we use an immersed boundary (IB) approach to simulate peristaltic transport in the esophagus. The bolus is treated as a viscous fluid that is actively transported by the muscular esophagus, which is modeled as an actively contracting, fiber-reinforced tube. A simplified version of our model is verified by comparison to an analytic solution to the tube dilation problem. Three different complex models of the multi-layered esophagus, which differ in their activation patterns and the layouts of the mucosal layers, are then extensively tested. To our knowledge, these simulations are the first of their kind to incorporate the bolus, the multi-layered esophagus tube, and muscle activation into an integrated model. Consistent with experimental observations, our simulations capture the pressure peak generated by the muscle activation pulse that travels along the bolus tail. These fully resolved simulations provide new insights into roles of the mucosal layers during bolus transport. In addition, the information on pressure and the kinematics of the esophageal wall due to the coordination of muscle activation is provided, which may help relate clinical data from manometry and ultrasound images to the underlying esophageal motor function.