GeoSPM: Geostatistical parametric mapping for medicine.
epidemiology
geostatistics
kriging
spatial analysis
statistical parametric mapping
Journal
Patterns (New York, N.Y.)
ISSN: 2666-3899
Titre abrégé: Patterns (N Y)
Pays: United States
ID NLM: 101767765
Informations de publication
Date de publication:
09 Dec 2022
09 Dec 2022
Historique:
received:
31
05
2022
revised:
01
07
2022
accepted:
11
11
2022
entrez:
26
12
2022
pubmed:
27
12
2022
medline:
27
12
2022
Statut:
epublish
Résumé
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-specialists, enables flexible modeling of complex spatial relations, exhibits robustness to noise and under-sampling, offers principled criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation.
Identifiants
pubmed: 36569555
doi: 10.1016/j.patter.2022.100656
pii: S2666-3899(22)00296-3
pmc: PMC9768692
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100656Subventions
Organisme : Wellcome Trust
ID : 213038
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Informations de copyright
© 2022 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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