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Article: Statistical methods of estimation and inference for functional MR image analysis

TitleStatistical methods of estimation and inference for functional MR image analysis
Authors
Issue Date1996
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0740-3194/
Citation
Magnetic Resonance In Medicine, 1996, v. 35 n. 2, p. 261-277 How to Cite?
AbstractTwo 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. Randomizatian 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.
Persistent Identifierhttp://hdl.handle.net/10722/175760
ISSN
2015 Impact Factor: 3.782
2015 SCImago Journal Rankings: 2.197
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorBullmore, Een_US
dc.contributor.authorBrammer, Men_US
dc.contributor.authorWilliams, SCRen_US
dc.contributor.authorRabeHesketh, Sen_US
dc.contributor.authorJanot, Nen_US
dc.contributor.authorDavid, Aen_US
dc.contributor.authorMellers, Jen_US
dc.contributor.authorHoward, Ren_US
dc.contributor.authorSham, Pen_US
dc.date.accessioned2012-11-26T09:01:04Z-
dc.date.available2012-11-26T09:01:04Z-
dc.date.issued1996en_US
dc.identifier.citationMagnetic Resonance In Medicine, 1996, v. 35 n. 2, p. 261-277en_US
dc.identifier.issn0740-3194en_US
dc.identifier.urihttp://hdl.handle.net/10722/175760-
dc.description.abstractTwo 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. Randomizatian 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.en_US
dc.languageengen_US
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0740-3194/en_US
dc.relation.ispartofMagnetic Resonance in Medicineen_US
dc.subject.meshAcoustic Stimulationen_US
dc.subject.meshBrain - Physiologyen_US
dc.subject.meshBrain Mappingen_US
dc.subject.meshEcho-Planar Imaging - Methodsen_US
dc.subject.meshHumansen_US
dc.subject.meshMagnetic Resonance Imaging - Methodsen_US
dc.subject.meshPhotic Stimulationen_US
dc.subject.meshRegression Analysisen_US
dc.subject.meshSensitivity And Specificityen_US
dc.titleStatistical methods of estimation and inference for functional MR image analysisen_US
dc.typeArticleen_US
dc.identifier.emailSham, P: pcsham@hku.hken_US
dc.identifier.authoritySham, P=rp00459en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1002/mrm.1910350219en_US
dc.identifier.pmid8622592-
dc.identifier.scopuseid_2-s2.0-0030063642en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0030063642&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume35en_US
dc.identifier.issue2en_US
dc.identifier.spage261en_US
dc.identifier.epage277en_US
dc.identifier.isiWOS:A1996TT42200018-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridBullmore, E=35405771500en_US
dc.identifier.scopusauthoridBrammer, M=7006746119en_US
dc.identifier.scopusauthoridWilliams, SCR=35419560700en_US
dc.identifier.scopusauthoridRabeHesketh, S=7003779088en_US
dc.identifier.scopusauthoridJanot, N=6503883707en_US
dc.identifier.scopusauthoridDavid, A=7402606754en_US
dc.identifier.scopusauthoridMellers, J=6603665190en_US
dc.identifier.scopusauthoridHoward, R=34769841900en_US
dc.identifier.scopusauthoridSham, P=34573429300en_US
dc.identifier.citeulike4258812-

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