Linked Life Data

17633720

Source:http://linkedlifedata.com/resource/pubmed/id/17633720

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-7-18
pubmed:abstractText
In this paper we describe a new method for quantifying metabolic asymmetry modulo structural hemispheric differences. The study of metabolic asymmetry in Alzheimer's disease (AD) serves as a driving application. The approach is based on anatomical atlas construction by large deformation diffeomorphic metric mapping (LDDMM) first introduced in [1]. Using invariance properties of the LDDMM, we define a structurally symmetric coordinate frame in which metabolic asymmetries between the left and the right hemispheres can be studied. This structurally symmetric coordinate system of each subject provides the correspondence between left and right hemispheric structures in an individual brain. These correspondences are used for measuring metabolic asymmetry modulo structural asymmetry. Again using the atlas construction framework, we build a common symmetric coordinate system of a entire population. The metabolic asymmetry maps of individuals in a population under study are mapped into the common structurally symmetric coordinate frame, allowing for a statistical description of the populations metabolic asymmetry. In this paper we prove certain invariance properties of the LDDMM atlas construction framework that make the definition of structurally symmetric coordinate systems possible. We present results from applying the methodology to images from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1011-2499
pubmed:author
pubmed:issnType
Print
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
446-57
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Quantifying metabolic asymmetry modulo structure in Alzheimer's disease.
pubmed:affiliation
School of Computing, University of Utah, Salt Lake City, UT, USA.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies, Research Support, N.I.H., Extramural