期刊
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
卷 98, 期 -, 页码 29-46出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neubiorev.2018.12.022
关键词
Latent variable; Structural equation modeling; Psychometrics; Human connectome project
资金
- NIH Institutes and Centers [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
- NIH [R37 MH066078]
- National Science Foundation Graduate Research Fellowship [DGE-1143954]
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 modem 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.
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