Persistent homology analysis of brain artery trees Article Swipe

View

Related Concepts

Persistent homology Topological data analysis Branching (polymer chemistry) Covariate Persistence (discontinuity) Data set Computer science Population Tree (set theory) Correlation Statistics Mathematics Artificial intelligence Medicine Combinatorics Algorithm Geology Materials science Environmental health Geometry Composite material Geotechnical engineering

Paul Bendich , J. S. Marron , Ezra Miller , Alex Pieloch , Sean Skwerer ·

YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.1214/15-aoas886 · OA: W1753705553

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.