Persistent Homology Analysis of Brain Artery Trees.

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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.

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10.1214/15-AOAS886

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Bendich, P, JS Marron, E Miller, A Pieloch and S Skwerer (n.d.). Persistent Homology Analysis of Brain Artery Trees. Ann Appl Stat, 10(1). pp. 198–218. 10.1214/15-AOAS886 Retrieved from https://hdl.handle.net/10161/11157.

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Scholars@Duke

Bendich

Paul L Bendich

Research Professor of Mathematics

I am a mathematician whose main research focus lies in adapting theory from ostensibly pure areas of mathematics, such as topology, geometry, and abstract algebra, into tools that can be broadly used in many data-centered applications.

My initial training was in a recently-emerging field called topological data analysis (TDA). I have been responsible for several essential and widely-used elements of its theoretical toolkit, with a particular focus on building TDA methodology for use on stratified spaces. Some of this work involves the creation of efficient algorithms, but much of it centers around theorem-proof mathematics, using proof techniques not only from algebraic topology, but also from computational geometry, from probability, and from abstract algebra.

Recently, I have done foundational work on TDA applications in several areas, including to neuroscience, to multi-target tracking, to multi-modal data fusion, and to a probabilistic theory of database merging. I am also becoming involved in efforts to integrate TDA within deep learning theory and practice.

I typically teach courses that connect mathematical principles to machine learning, including upper-level undergraduate courses in topological data analysis and more general high-dimensional data analysis, as well as a sophomore level course (joint between pratt and math) that serves as a broad introduction to machine learning and data analysis concepts.

Miller

Ezra Miller

Professor of Mathematics

Professor Miller's research centers around problems in geometry,
algebra, topology, probability, statistics, and computation
originating in mathematics and the sciences, including biology,
chemistry, computer science, and imaging.

The techniques range, for example, from abstract algebraic geometry or
commutative algebra of ideals and varieties to concrete metric or
discrete geometry of polyhedral spaces; from deep topological
constructions such as equivariant K-theory and stratified Morse theory
to elementary simplicial and persistent homology; from functorial
perspectives on homological algebra in the derived category to
specific constructions of complexes based on combinatorics of cell
decompositions; from geodesic collapse applied to central limit
theorems for samples from stratified spaces to dynamics of explicit
polynomial vector fields on polyhedra.

Beyond motivations from within mathematics, the sources of these
problems lie in, for example, graphs and trees in evolutionary biology
and medical imaging; mass-action kinetics of chemical reactions;
computational geometry, symbolic computation, and combinatorial game
theory; Lie theory; and geometric statistics of data sampled from
highly non-Euclidean spaces. Examples of datasets under consideration
include MRI images of blood vessels in human brains and lungs, 3D
folded protein structures, and photographs of fruit fly wings for
developmental morphological studies.


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