title: Modeling fMRI data: challenges and opportunities.
abstract: We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data--the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site consortium, the Function Biomedical Informatics Research Network. Due to the sheer volume of data, univariate procedures are often applied, which leads to a multiple comparisons problem (since the data are necessarily multivariate). The papers in this section include interesting applications, such as a state-space model applied to these data, and conclude with a reflection on basic measurement problems in fMRI. All in all, they provide a good overview of the challenges that fMRI data present to the standard psychometric toolbox, but also to the opportunities they offer for new psychometric modeling.
authors.affiliation: Faculty of Psychology, University of Barcelona, P. Vall d'Hebron 171, 08035, Barcelona, Spain, amaydeu@ub.edu.
authors.forename: Alberto
authors.initials: A
authors.lastname: Maydeu-Olivares
authors.forename: Gregory
authors.initials: G
authors.lastname: Brown
mesh_terms: D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D011594:Psychometrics / Q000379:methods*
references.citation: Psychometrika. 2013 Apr;78(2):260-78
references.pmid: 25107616
references.citation: Psychometrika. 2013 Apr;78(2):308-21
references.pmid: 25107618
references.citation: Psychometrika. 2013 Apr;78(2):243-59
references.pmid: 25107615
references.citation: Psychometrika. 2013 Jul;78(3):396-416
references.pmid: 25106392
references.citation: Psychometrika. 2013 Apr;78(2):279-307
references.pmid: 25107617