Linked Life Data

8931913

Source:http://linkedlifedata.com/resource/pubmed/id/8931913

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
1997-3-11
pubmed:abstractText
Functional brain imaging data may contain large individual differences in information about whole brain and regional levels of activity, and it is common to remove these differences using arithmetic transformation (normalization) prior to statistical analysis. As no single transformation is widely accepted, we examine the effects of four normalizing methods (ratioing, residuals from regressions on global cerebral blood flow, Z scores, and subject residual profiles) on 1) profile shape, 2) correlations between regions, 3) correlations between subjects, and 4) analysis of variance results. These effects are evaluated using an empirical data set consisting of regional cerebral blood flow values from 22 regions of interest in 46 depressed adults and 48 age-matched normal controls obtained by 133Xe single photon emission computed tomography. Results show that normalization method has substantial but different effects on characteristics of the data and statistical results. The rationing method appears to be an optimal choice for most analyses.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0006-3223
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
40
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1106-21
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1996
pubmed:articleTitle
Effects of four normalizing methods on data analytic results in functional brain imaging.
pubmed:affiliation
Department of Psychiatry, University of Texas Southwestern Medical Center at Dallas, TX, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.