Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric

Hyekyoung Lee, Moo K. Chung, Hyejin Kang, Boong Nyun Kim, Dong Soo Lee

Research output: Contribution to journalConference articleResearchpeer-review

22 Citations (Scopus)

Abstract

The difference between networks has been often assessed by the difference of global topological measures such as the clustering coefficient, degree distribution and modularity. In this paper, we introduce a new framework for measuring the network difference using the Gromov-Hausdorff (GH) distance, which is often used in shape analysis. In order to apply the GH distance, we define the shape of the brain network by piecing together the patches of locally connected nearest neighbors using the graph filtration. The shape of the network is then transformed to an algebraic form called the single linkage matrix. The single linkage matrix is subsequently used in measuring network differences using the GH distance. As an illustration, we apply the proposed framework to compare the FDG-PET based functional brain networks out of 24 attention deficit hyperactivity disorder (ADHD) children, 26 autism spectrum disorder (ASD) children and 11 pediatric control subjects.

Original languageEnglish
Pages (from-to)302-309
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
StatePublished - 11 Oct 2011
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 18 Sep 201122 Sep 2011

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Hausdorff Metric
Filtration
Brain
Pediatrics
Hausdorff Distance
Computing
Graph in graph theory
Linkage
Disorder
Locally Connected
Clustering Coefficient
Shape Analysis
Degree Distribution
Modularity
Patch
Nearest Neighbor

Cite this

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abstract = "The difference between networks has been often assessed by the difference of global topological measures such as the clustering coefficient, degree distribution and modularity. In this paper, we introduce a new framework for measuring the network difference using the Gromov-Hausdorff (GH) distance, which is often used in shape analysis. In order to apply the GH distance, we define the shape of the brain network by piecing together the patches of locally connected nearest neighbors using the graph filtration. The shape of the network is then transformed to an algebraic form called the single linkage matrix. The single linkage matrix is subsequently used in measuring network differences using the GH distance. As an illustration, we apply the proposed framework to compare the FDG-PET based functional brain networks out of 24 attention deficit hyperactivity disorder (ADHD) children, 26 autism spectrum disorder (ASD) children and 11 pediatric control subjects.",
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Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric. / Lee, Hyekyoung; Chung, Moo K.; Kang, Hyejin; Kim, Boong Nyun; Lee, Dong Soo.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6892 LNCS, No. PART 2, 11.10.2011, p. 302-309.

Research output: Contribution to journalConference articleResearchpeer-review

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