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
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

100656

Subventions

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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Auteurs

Holger Engleitner (H)

UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.

Ashwani Jha (A)

UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.

Marta Suarez Pinilla (MS)

UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.

Amy Nelson (A)

UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.

Daniel Herron (D)

Research & Development, NIHR University College London Hospitals Biomedical Research Centre, London W1T 7DN, UK.

Geraint Rees (G)

UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.

Karl Friston (K)

UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.

Martin Rossor (M)

UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.

Parashkev Nachev (P)

UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.

Classifications MeSH