Linked Life Data

9417973

Source:http://linkedlifedata.com/resource/pubmed/id/9417973

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
1998-2-4
pubmed:abstractText
This paper presents a new method for characterizing brain responses in both PET and fMRI data. The aim is to capture the correlations between the scans of an experiment and a set of external predictor variables that are thought to affect the scans, such as type, intensity, or shape of stimulus response. Its main feature is a Canonical Variates Analysis (CVA) of the estimated effects of the predictors from a multivariate linear model (MLM). The advantage of this over current methods is that temporal correlations can be incorporated into the model, making the MLM method suitable for fMRI as well as PET data. Moreover, tests for the presence of any correlation, and inference about the number of canonical variates needed to capture that correlation, can be based on standard multivariate statistics, rather than simulations. When applied to an fMRI data set previously analyzed by another CVA method, the MLM method reveals a pattern of responses that is closer to that detected in an earlier non-CVA analysis.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1053-8119
pubmed:author
pubmed:copyrightInfo
Copyright 1997 Academic Press.
pubmed:issnType
Print
pubmed:volume
6
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
305-19
pubmed:dateRevised
2009-9-29
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Characterizing the response of PET and fMRI data using multivariate linear models.
pubmed:affiliation
Department of Mathematics and Statistics, McGill University, 805 Sherbrooke Street West, Montreal, Québec, H3A 2K6, Canada.
pubmed:publicationType
Journal Article