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Persistent Homology Analysis of Brain Artery Trees.

dc.contributor.author Bendich, P
dc.contributor.author Marron, JS
dc.contributor.author Miller, E
dc.contributor.author Pieloch, A
dc.contributor.author Skwerer, S
dc.coverage.spatial United States
dc.date.accessioned 2015-12-12T09:14:26Z
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/27642379
dc.identifier.issn 1932-6157
dc.identifier.uri https://hdl.handle.net/10161/11157
dc.description.abstract 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.
dc.language eng
dc.publisher Institute of Mathematical Statistics
dc.relation.ispartof Ann Appl Stat
dc.relation.isversionof 10.1214/15-AOAS886
dc.subject Persistent homology
dc.subject angiography
dc.subject statistics
dc.subject topological data analysis
dc.subject tree-structured data
dc.title Persistent Homology Analysis of Brain Artery Trees.
dc.type Journal article
duke.contributor.id Bendich, P|0308528
duke.contributor.id Miller, E|0490663
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/27642379
pubs.begin-page 198
pubs.end-page 218
pubs.issue 1
pubs.organisational-group Duke
pubs.organisational-group Mathematics
pubs.organisational-group Statistical Science
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 10


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