Electromechanical Response of Mesenteric Ischemia Defined Through Simultaneous High-Resolution Bioelectrical and Video Mapping.

Intestinal motility is governed in part by bioelectrical slow-waves and spike-bursts. Mesenteric ischemia is a substantial clinical challenge, but its electrophysiological and contractile mechanisms are not well understood. Simultaneous high-resolution bioelectrical and video mapping techniques were used to capture the changes in slow-waves, spike-bursts, and contractile activity during baseline, ischemia, and reperfusion periods. Experiments were performed on anesthetized pigs where intestinal contractions were quantified using surface strain and diameter measurements, while slow-wave and spike-bursts were quantified using frequency and amplitude. Slow-waves entrainment within the ischemic region diminished during ischemia, resulting in irregular slow-wave activity and a reduction in the frequency from 12.4 ± 3.0 cycles-per-minute (cpm) to 2.5 ± 2.7 cpm (p = 0.0006). At the end of the reperfusion period, normal slow-wave entrainment was observed at a frequency of 11.5 ± 2.9 cpm. There was an increase in spike-burst activity between the baseline and ischemia periods (1.1 ± 1.4 cpm to 8.7 ± 3.3 cpm, p = 0.0003) along with a spasm of circumferential contractions. At the end of the reperfusion period, the frequency of spike-bursts decreased to 2.7 ± 1.4 cpm, and contractions subsided. The intestine underwent tonal contraction during ischemia, with the diameter decreasing from 29.3 ± 2.6 mm to 21.2 ± 6.2 mm (p = 0.0020). At the end of the reperfusion period, the intestinal diameter increased to 27.3 ± 3.9 mm. The decrease in slow-wave activity, increase in spike-bursts, and tonal contractions can objectively identify ischemic segments in the intestine. It is anticipated that the use of electrophysiological slow-wave and spike-burst biomarkers, along with contractile measures, could identify mesenteric ischemia in surgical settings and allow an objective biomarker for successful revascularization.

SPARClink: an interactive tool to visualize the impact of the SPARC program.

The National Institutes of Health (NIH) Stimulating Peripheral Activity to Relieve Conditions (SPARC) program seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. SPARC-funded researchers are generating rich datasets from neuromodulation research that are curated and shared according to FAIR (Findable, Accessible, Interoperable, and Reusable) guidelines and are accessible to the public on the SPARC data portal. Keeping track of the utilization of these datasets within the larger research community is a feature that will benefit data-generating researchers in showcasing the impact of their SPARC outcomes. This will also allow the SPARC program to display the impact of the FAIR data curation and sharing practices that have been implemented. This manuscript provides the methods and outcomes of SPARClink, our web tool for visualizing the impact of SPARC, which won the Second prize at the 2021 SPARC FAIR Codeathon. With SPARClink, we built a system that automatically and continuously finds new published SPARC scientific outputs (datasets, publications, protocols) and the external resources referring to them. SPARC datasets and protocols are queried using publicly accessible REST application programming interfaces (APIs, provided by Pennsieve and Protocols.io) and stored in a publicly accessible database. Citation information for these resources is retrieved using the NIH reporter API and National Center for Biotechnology Information (NCBI) Entrez system. A novel knowledge graph-based structure was created to visualize the results of these queries and showcase the impact that the FAIR data principles can have on the research landscape when they are adopted by a consortium.

Relationship Between Intestinal Slow-waves, Spike-bursts, and Motility, as Defined Through High-resolution Electrical and Video Mapping.

Background/Aims: High-resolution extracellular mapping has improved our understanding of bioelectric slow-wave and spike-burst activity in the small intestine. The spatiotemporal correlation of electrophysiology and motility patterns is of critical interest to intestinal function but remains incompletely defined. Methods: Intestinal jejunum segments from in vivo pigs and rabbits were exteriorized, and simultaneous high-resolution extracellular recordings and video recordings were performed. Contractions were quantified with strain fields, and the frequencies and velocities of motility patterns were calculated. The amplitudes, frequencies, and velocities of slow-wave propagation patterns and spike-bursts were quantified and visualized. In addition, the duration, size and energy of spike-burst patches were quantified. Results: Slow-wave associated spike-bursts activated periodically at 10.8 ± 4.0 cycles per minute (cpm) in pigs and 10.2 ± 3.2 cpm in rabbits, while independent spike-bursts activated at a frequency of 3.2 ± 1.8 cpm. Independent spike-bursts had higher amplitude and longer duration than slow-wave associated spike-bursts (1.4 ± 0.8 mV vs 0.1 ± 0.1 mV, P < 0.001; 1.8 ± 1.4 seconds vs 0.8 ± 0.3 seconds, P < 0.001 in pigs). Spike-bursts that activated as longitudinal or circumferential patches were associated with contractions in the respective directions. Spontaneous peristaltic contractions were elicited by independent spike-bursts and travelled slower than slow-wave velocity (3.7 ± 0.5 mm/sec vs 10.1 ± 4.7 mm/sec, P = 0.007). Cyclic peristaltic contractions were driven by slow-wave associated spike-bursts and were coupled to slow-wave velocity and frequency in rabbit (14.2 ± 2.3 mm/sec vs 11.5 ± 4.6 mm/sec, P = 0.162; 11.0 ± 0.6 cpm vs 10.8 ± 0.6 cpm, P = 0.970). Conclusions: Motility patterns were dictated by patterns of spike-burst patches. When spike-bursts were coupled to slow-waves, periodic motility patterns were observed, while when spike-bursts were not coupled to slow-waves, spontaneous aperiodic motility patterns were captured.

High-Resolution Spatiotemporal Quantification of Intestinal Motility With Free-Form Deformation.

OBJECTIVE: To develop a method to quantify strain fields from in vivo intestinal motility recordings that mitigate accumulation of tracking error. METHODS: The deforming geometry of the intestine in video sequences was modeled by a biquadratic B-spline mesh. Green-Lagrange strain fields were computed to quantify the surface deformations. A nonlinear optimization scheme was applied to mitigate the accumulation of tracking error associated with image registration. RESULTS: The optimization scheme maintained the RMS strain error under 1% and reduced the rate of strain error by 97% during synthetic tests. The algorithm was applied to map 64 segmental, 12 longitudinal, and 23 propagating circular contractions in the jejunum. Coordinated activity of the two muscle layers could be identified and the strain fields were able to map and quantify the anisotropic contractions of the intestine. Frequency and velocity were also quantified, from which two types of propagating circular contractions were identified: (i) [Formula: see text] strain contractions that originated spontaneously and propagated at [Formula: see text] mm/s in two pigs, and (ii) cyclic propagating contractions of [Formula: see text] strain occurred at [Formula: see text] cpm and propagated at [Formula: see text] mm/s in a rabbit. CONCLUSION: The algorithm simultaneously mapped the circular, longitudinal activity of the intestine with high spatial resolution and quantified anisotropic contractions and relaxations. SIGNIFICANCE: The proposed algorithm can now be used to define the interactions of muscle layers during motility patterns. It can be integrated with high-resolution bioelectrical recordings to investigate the regulatory mechanisms of motility.

High-Resolution Mapping of Intestinal Spike Bursts and Motility.

Gastrointestinal (GI) motility and functional disorders affect up to 25% of the American population. Electrophysiological studies had shown a link between these functional motility disorders and abnormalities in GI bioelectrical activity. However, the dynamics between GI electrical activity (slow waves and spike bursts) and motility are not well understood. This study presents a framework to simultaneously record and quantify GI spike bursts and motility in vivo, in high-resolution. The dynamics between spike burst events and motility observed in 4 pig studies were investigated. A clear connection between spike burst patches and localized contractions was observed. The dataset consisted of 685 spike burst events in 191 patches. Contractions were associated with 81 patches. Spike burst patches associated with contractions had significantly higher amplitude, duration, and size compared to the ones that did not show an association. The amplitude, duration, and size of spike burst patches were positively correlated with the contraction strength. The spike burst patch energy displayed the highest correlation (r = 0.74). The contraction strength had a linear trend with spike burst patch energy. However, it could only account for 52% of the variance in contraction strength.

A Framework for Spatiotemporal Analysis of Gastrointestinal Spike Burst Propagation.

Gastrointestinal spike bursts are a bioelectrical phenomenon associated with motility. These events when initiated propagate a small distance and abruptly terminate activating a small area defined as a patch. Understanding normal and abnormal propagation patterns of these events may shed light on the root causes of functional motility disorders. This study develops an automated framework for spatiotemporal analysis of spike bursts. High-resolution electrical signals were obtained from the pig intestine, after which intestinal spike bursts were identified and clustered into their propagating wavefronts. Propagation velocity was estimated by fitting a polynomial surface to the activation times. The fit was able to estimate the velocity of spike burst patches covering at least six channels with an average RMSE of 0.4 s. Propagation within patches was visualized by plotting the fit as activation maps and velocity maps. Average velocities were calculated to compare the propagation characteristics of different types of patches. In the future, this framework will be extended to generate amplitude maps and spike burst duration maps. These tools can be used to analyze spike patch propagation and their relationship to motility.