Linked Life Data

16586151

Source:http://linkedlifedata.com/resource/pubmed/id/16586151

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2006-6-28
pubmed:abstractText
The techniques currently available for studying drug self-administration in animals offer the unique opportunity to carry out micro-analysis of initial episodes of drug use which are extremely difficult to obtain for human subjects. Nonetheless, traditional self-administration techniques do not allow a cost-effective output of large sample sizes needed for genetic analysis. Additionally, the statistical techniques that allow the integration of within-subject temporal data with genetic information are scant. We therefore propose a two-stage method for analyzing strain differences in dynamic phenotypes for a high-throughput version of the self-administration procedure. On a first phenotype-refinement stage, a change-point algorithm (Gallistel et al. (2004) Proc. Natl Acad. Sci. USA 101:13124-13131) was used to separate individual drug self-administration response curves into three distinct components. In a second stage, strains differences in these indexes were assessed. This two-stage approach is illustrated with drug self-administration data and through a computer simulation.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0001-8244
pubmed:author
pubmed:issnType
Print
pubmed:volume
36
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
525-35
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A method for analyzing strain differences in acquisition of IV cocaine self-administration in mice.
pubmed:affiliation
Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298-0126 USA. cvargasirwin@vcu.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural