Dataset Information

Spatial confidence regions for combinations of excursion sets in image analysis.

ABSTRACT: The analysis of excursion sets in imaging data is essential to a wide range of scientific disciplines such as neuroimaging, climatology, and cosmology. Despite growing literature, there is little published concerning the comparison of processes that have been sampled across the same spatial region but which reflect different study conditions. Given a set of asymptotically Gaussian random fields, each corresponding to a sample acquired for a different study condition, this work aims to provide confidence statements about the intersection, or union, of the excursion sets across all fields. Such spatial regions are of natural interest as they directly correspond to the questions 'Where do all random fields exceed a predetermined threshold?', or 'Where does at least one random field exceed a predetermined threshold?'. To assess the degree of spatial variability present, our method provides, with a desired confidence, subsets and supersets of spatial regions defined by logical conjunctions (i.e. set intersections) or disjunctions (i.e. set unions), without any assumption on the dependence between the different fields. The method is verified by extensive simulations and demonstrated using task-fMRI data to identify brain regions with activation common to four variants of a working memory task.

SUBMITTER: Maullin-Sapey T

PROVIDER: S-EPMC10852994 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Spatial confidence regions for combinations of excursion sets in image analysis.

Maullin-Sapey Thomas T Schwartzman Armin A Nichols Thomas E TE

Journal of the Royal Statistical Society. Series B, Statistical methodology 20230921 1

The analysis of excursion sets in imaging data is essential to a wide range of scientific disciplines such as neuroimaging, climatology, and cosmology. Despite growing literature, there is little published concerning the comparison of processes that have been sampled across the same spatial region but which reflect different study conditions. Given a set of asymptotically Gaussian random fields, each corresponding to a sample acquired for a different study condition, this work aims to provide co ...[more]

PMID: 38344135

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Dataset Information

Spatial confidence regions for combinations of excursion sets in image analysis.

Publications

Spatial confidence regions for combinations of excursion sets in image analysis.

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