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

• Published in: PloS one Vol. 14; no. 10; p. e0220315
• Main Authors: Allegra, Alexis B., Gharibans, Armen A., Schamberg, Gabriel E., Kunkel, David C., Coleman, Todd P.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.10.2019; Public Library of Science (PLoS)
• Subjects: Abdomen; Bayesian analysis; Bioengineering; Biology and Life Sciences; Biomedical engineering; Care and treatment; CAT scans; Computed tomography; Computer engineering; Diabetes; Diabetes Complications - physiopathology; Diabetes mellitus; Diagnosis; Diagnostic imaging; Electrical properties; Electrodes; Electrophysiological Phenomena; Food; Gastroenterology; Gastrointestinal diseases; Gastrointestinal Motility; Gastrointestinal system; Gastrointestinal tract; Gastroparesis; Human motion; Humans; Ill posed problems; Infection; Information processing; Inverse problems; Medicine and Health Sciences; Models, Biological; Motility; Patients; Phenotyping; Physical Sciences; Physicians; Practice; Questionnaires; Research and Analysis Methods; Signs and symptoms; Statistical analysis; Statistical inference; Stomach; Stomach - physiopathology; Stomach Diseases - physiopathology; Surface activity; Wave direction; Wave fronts; Wave propagation; La Jolla California; United States > US
• DOI: 10.1371/journal.pone.0220315
• ISSN: 1932-6203

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.