title: Evaluation of Second-Level Inference in fMRI Analysis.
abstract: We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference.
authors.affiliation: Department of Data Analysis, Ghent University, H. Dunantlaan 1, 9000 Ghent, Belgium.
authors.forename: Sanne P
authors.initials: SP
authors.lastname: Roels
authors.affiliation: Department of Data Analysis, Ghent University, H. Dunantlaan 1, 9000 Ghent, Belgium.
authors.forename: Tom
authors.initials: T
authors.lastname: Loeys
authors.affiliation: Department of Data Analysis, Ghent University, H. Dunantlaan 1, 9000 Ghent, Belgium.
authors.forename: Beatrijs
authors.initials: B
authors.lastname: Moerkerke
mesh_terms: D000465:Algorithms; D001921:Brain / Q000098:blood supply* / Q000502:physiology; D001931:Brain Mapping; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008606:Mental Processes / Q000502:physiology*; D010100:Oxygen / Q000097:blood; D012372:ROC Curve
chemical_list: D010100:Oxygen
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