Linked Life Data

21995042

Source:http://linkedlifedata.com/resource/pubmed/id/21995042

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2011-10-14
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
302-9
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric.
pubmed:affiliation
Department of Nuclear Medicine, Seoul National University, College of Medicine, Seoul, Korea.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't