GeoSPM: Geostatistical parametric mapping for medicine.

ABSTRACT: The characteristics and determinants of health and disease are often organized in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Drawing on statistical parametric mapping, a framework for topological inference well established in the realm of neuroimaging, we propose and validate an approach to the spatial analysis of diverse clinical data-GeoSPM-based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is readily interpretable, can be implemented with ease by non-specia

SUBMITTER: Engleitner H 

PROVIDER: S-EPMC9768692 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature