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dc.contributor.authorZhang, Huien_US
dc.contributor.authorLuo, Wen-Linen_US
dc.contributor.authorNichols, Thomas E.en_US
dc.date.accessioned2007-05-02T14:17:48Z
dc.date.available2007-05-02T14:17:48Z
dc.date.issued2006-05en_US
dc.identifier.citationZhang, Hui; Luo, Wen-Lin; Nichols, Thomas E. (2006). "Diagnosis of single-subject and group fMRI data with SPMd." Human Brain Mapping 27(5): 442-451. <http://hdl.handle.net/2027.42/50665>en_US
dc.identifier.issn1065-9471en_US
dc.identifier.issn1097-0193en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/50665
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=16615119&dopt=citationen_US
dc.description.abstractExcept for purely nonparametric methods, statistical methods depend on assumptions about the distribution of the data studied. While these assumptions are easily checked for a single univariate dataset with diagnostic plots, in the massively univariate model used with functional MRI (fMRI) it is impractical to check with a massive number of plots. In previous work we have demonstrated how to diagnose model assumptions and lack-of-fit for single-subject fMRI models using a working assumption of independent errors; our work depended on images and time series of summary statistics that, when simultaneously viewed dynamically, identify problem scans and voxels. In this article we extend our previous work to account for temporal autocorrelation in single-subject models and show how analogous methods can be used on group models where multiple subjects are studied. We apply these methods to the single-subject Functional Image Analysis Contest (FIAC) data and find several anomalies, but none that appear to invalidate the results for that subject. With the group FIAC data we find one subject (and possibly two more) that demonstrate a different pattern of activity. None of our conclusions would be arrived at by simply looking at images of t statistics, demonstrating the importance of model assessment through exploration of the data and diagnosis of model assumptions. Hum Brain Mapp 27:442–451, 2006. © 2006 Wiley-Liss, Inc.en_US
dc.format.extent1132794 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherWiley Subscription Services, Inc., A Wiley Companyen_US
dc.subject.otherLife and Medical Sciencesen_US
dc.subject.otherNeuroscience, Neurology and Psychiatryen_US
dc.titleDiagnosis of single-subject and group fMRI data with SPMden_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelNeurosciencesen_US
dc.subject.hlbsecondlevelKinesiology and Sportsen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, Michiganen_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan ; Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029en_US
dc.contributor.affiliationotherCBARDS, Merck & Co., Rahway, New Jerseyen_US
dc.identifier.pmid16615119en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/50665/1/20253_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/hbm.20253en_US
dc.identifier.sourceHuman Brain Mappingen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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