title: Modeling for intergroup comparisons of imaging data.
abstract: Intergroup comparisons pose unique challenges in the analysis of functional imaging data. Imperfections in intersubject stereotaxis can give rise to artifactual results and make it particularly important to allow for intersubject differences in task-related changes when formulating statistical models. Because intergroup comparisons generally involve inferences about the populations from which the subjects were drawn rather than inferences about the particular subjects themselves, subjects must be treated as random rather than fixed effects in the statistical model. These requirements, when combined with the need to adjust for multiple spatial comparisons, result in low statistical power when the number of subjects in each group is small. Functional imaging studies to identify differences between groups generally require many more subjects than other types of functional imaging studies and require careful advance planning to maximize the likelihood of reaching meaningful conclusions.
authors.affiliation: Division of Brain Mapping, UCLA School of Medicine 90095, USA.
authors.forename: R P
authors.initials: RP
authors.lastname: Woods
mesh_terms: D001931:Brain Mapping; D003952:Diagnostic Imaging / Q000706:statistics & numerical data*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D015233:Models, Statistical; D012016:Reference Values; D012680:Sensitivity and Specificity; D014055:Tomography, Emission-Computed / Q000706:statistics & numerical data