Computing the shape of brain networks using graph filtration and gromov-hausdorff metric

  • Authors:

  • Hyekyoung Lee;Moo K. Chung;Hyejin Kang;Boong-Nyun Kim;Dong Soo Lee

  • Affiliations:

  • Department of Nuclear Medicine, College of Medicine, Seoul, Korea and Department of Brain and Cognitive Sciences, College of Medicine, Seoul, Korea and Institute of Radiation Medicine, Medical Res ...;Department of Brain and Cognitive Sciences, College of Medicine, Seoul, Korea and Department of Biostatistics and Medical Informatics, Madison, WI and Waisman Laboratory for Brain Imaging and Beha ...;Department of Nuclear Medicine, College of Medicine, Seoul, Korea and Institute of Radiation Medicine, Medical Research Center, College of Medicine, Seoul, Korea;Department of Neuropsychiatry, Seoul National University, College of Medicine, Seoul, Korea;Department of Nuclear Medicine, College of Medicine, Seoul, Korea and Institute of Radiation Medicine, Medical Research Center, College of Medicine, Seoul, Korea and WCU Department of Molecular Me ...

  • Venue:
  • MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

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.