PERSISTENT HOMOLOGY ANALYSIS OF BRAIN ARTERY TREES

• 成果类型: Article
• 署名作者: Bendich, Paul; Marron, J. S.; Miller, Ezra; Pieloch, Alex; Skwerer, Sean
• 署名单位: Duke University; University of North Carolina; University of North Carolina Chapel Hill; Yale University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/15-AOAS886
• 发表日期: 2016
• 页码: 198-218
• 关键词: space

摘要

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.