Persistent Homology Analysis of Brain Artery Trees

New representations of tree-structured data objects, using ideas from topological data analysis, enable improved statistical analyses of a population of brain artery trees. A number of representations of each data tree arise from persistence diagrams that quantify branching and looping of vessels at...

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Bibliographic Details
Published inThe annals of applied statistics Vol. 10; no. 1; p. 198
Main Authors Bendich, Paul, Marron, J S, Miller, Ezra, Pieloch, Alex, Skwerer, Sean
Format Journal Article
LanguageEnglish
Published United States 01.03.2016
Subjects
Persistent homology
tree-structured data
angiography
topological data analysis
statistics
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Summary:New representations of tree-structured data objects, using ideas from topological data analysis, enable improved statistical analyses of a population of brain artery trees. A number of representations of each data tree arise from persistence diagrams that quantify branching and looping of vessels at multiple scales. Novel approaches to the statistical analysis, through various summaries of the persistence diagrams, lead to heightened correlations with covariates such as age and sex, relative to earlier analyses of this data set. The correlation with age continues to be significant even after controlling for correlations from earlier significant summaries.
ISSN:1932-6157
DOI:10.1214/15-aoas886