Neuroimaging of individual differences: A latent variable modeling perspective

HighlightsUnderstanding individual differences requires consideration of psychometricsPsychometric frameworks are not often used in fMRI due to power/sample size issuesLatent variable models are flexible and can address psychometric concernsIndividual differences in fMRI can be better understood via latent variable modelsRecent big data fMRI projects provide opportunity to harness latent variable models ABSTRACT Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task‐related fMRI (t‐fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t‐fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain‐behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t‐fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t‐fMRI individual differences research can benefit by utilizing latent variable models.