Linked Life Data

8622592

Source:http://linkedlifedata.com/resource/pubmed/id/8622592

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1996-6-19
pubmed:abstractText
Two questions arising in the analysis of functional magnetic resonance imaging (fMRI) data acquired during periodic sensory stimulation are: i) how to measure the experimentally determined effect in fMRI time series; and ii) how to decide whether an apparent effect is significant. Our approach is first to fit a time series regression model, including sine and cosine terms at the (fundamental) frequency of experimental stimulation, by pseudogeneralized least squares (PGLS) at each pixel of an image. Sinusoidal modeling takes account of locally variable hemodynamic delay and dispersion, and PGLS fitting corrects for residual or endogenous autocorrelation in fMRI time series, to yield best unbiased estimates of the amplitudes of the sine and cosine terms at fundamental frequency; from these parameters the authors derive estimates of experimentally determined power and its standard error. Randomization testing is then used to create inferential brain activation maps (BAMs) of pixels significantly activated by the experimental stimulus. The methods are illustrated by application to data acquired from normal human subjects during periodic visual and auditory stimulation.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0740-3194
pubmed:author
pubmed:issnType
Print
pubmed:volume
35
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
261-77
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1996
pubmed:articleTitle
Statistical methods of estimation and inference for functional MR image analysis.
pubmed:affiliation
Department of Biostatistics & Computing, Institute of Psychiatry, Maudsley Hospital, London, United Kingdom.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't