Persistent homology analysis of brain artery trees

Published

Journal Article

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

Full Text

Duke Authors

Cited Authors

  • Bendich, P; Marron, JS; Miller, E; Pieloch, A; Skwerer, S

Published Date

  • 2016

Published In

  • Annals of Applied Statistics

Volume / Issue

  • 10 / 1

Start / End Page

  • 198 - 218

PubMed ID

  • 27642379

Pubmed Central ID

  • 27642379