Source Localization of the Gastric Slow Wave
Gastrointestinal (GI) problems give rise to 10 percent of initial patient visits to their primary care 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. In this dissertation, I develop non-invasive approaches to extract this spatial information. Using Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans from human subjects, I simulate normative and disordered gastric surface electrical activity along with associated abdominal activity. I employ Bayesian inference to solve the ill-posed inverse problem of estimating gastric surface activity from cutaneous multi-electrode human subject recordings as well as the simulated observations. In Chapter 1, I explore Bayesian inference methods with different prior distributions to extract spatiotemporal patterns and identify an optimal technique termed Group Sparsity whose prior enforces both spatial sparsity in the recovered sources and temporal smoothness. In Chapter 2, I explore a modification to the inference method and develop automated techniques for isolating an optimal solution for the Group Sparsity approach. Finally in Chapter 3, Using Fisher information, I turn to exploring the limits of this problem by investigating how electrode size and density affect the reconstruction of the gastric slow wave. I find that there is an electrode array configuration that optimally trades off electrode noise variance with spatial resolution, both of which increase with electrode density. Ultimately, this research shows that the gastric slow wave can be non-invasively characterized with a recording from a cutaneous electrode array and medical imaging (CT or MRI). While my approach still requires a simultaneous invasive measure for complete validation, this body of work represents a critical step towards clinical non-invasive measures of gastric health.