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dc.contributor.authorOlszowy, Wiktoren
dc.contributor.authorAston, Johnen
dc.contributor.authorRua, Catarinaen
dc.contributor.authorWilliams, Guyen
dc.date.accessioned2019-03-08T00:30:22Z
dc.date.available2019-03-08T00:30:22Z
dc.date.issued2019-12-25en
dc.identifier.issn2041-1723
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/290313
dc.description.abstractGiven the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. Here, we employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. We found that autocorrelation modeling in AFNI, although imperfect, performed much better than the autocorre- lation modeling of FSL and SPM. The presence of residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low- frequency experimental designs. SPM’s alternative pre-whitening method, FAST, performed better than SPM’s default. The reliability of task fMRI studies could be improved with more accurate autocorrelation modeling. We recommend that fMRI analysis packages provide diagnostic plots to make users aware of any pre-whitening problems.
dc.format.mediumElectronicen
dc.languageengen
dc.publisherNature Publishing Group
dc.rights
dc.rights.uri
dc.subjectBrainen
dc.subjectHumansen
dc.subjectMagnetic Resonance Imagingen
dc.subjectArtifactsen
dc.subjectLinear Modelsen
dc.subjectReproducibility of Resultsen
dc.subjectAlgorithmsen
dc.subjectComputer Simulationen
dc.subjectImage Processing, Computer-Assisteden
dc.subjectFunctional Neuroimagingen
dc.subjectDatasets as Topicen
dc.titleAccurate autocorrelation modeling substantially improves fMRI reliability.en
dc.typeArticle
prism.issueIdentifier1en
prism.publicationDate2019en
prism.publicationNameNature communicationsen
prism.startingPage1220
prism.volume10en
dc.identifier.doi10.17863/CAM.37543
dcterms.dateAccepted2019-02-25en
rioxxterms.versionofrecord10.1038/s41467-019-09230-wen
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-12-25en
dc.contributor.orcidOlszowy, Wiktor [0000-0002-6080-6597]
dc.contributor.orcidWilliams, Guy [0000-0001-5223-6654]
dc.identifier.eissn2041-1723
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/N014588/1)
pubs.funder-project-idMEDICAL RESEARCH COUNCIL (MR/M009041/1)
pubs.funder-project-idMEDICAL RESEARCH COUNCIL (MR/M024873/1)
cam.orpheus.successMon Jun 08 08:21:08 BST 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2022-03-07


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