Confound modelling in UK Biobank brain imaging

• Published in: NeuroImage (Orlando, Fla.) Vol. 224; p. 117002
• Main Authors: Alfaro-Almagro, Fidel, McCarthy, Paul, Afyouni, Soroosh, Andersson, Jesper L.R., Bastiani, Matteo, Miller, Karla L., Nichols, Thomas E., Smith, Stephen M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2021; Elsevier Limited; Elsevier
• Subjects: Age; Big data imaging; Biobanks; Biological Specimen Banks; Brain; Confounds; Data modelling; Datasets; Electronic Data Processing; Epidemiological studies; Head; Humans; Image analysis; Machine learning; Medical imaging; Multi-modal data integration; Neuroimaging; Neuroimaging - methods; Scanners; Software; Statistica l modelling; Time Factors; United Kingdom; Variables; United Kingdom; United Kingdom > UK; Epidemiological studies; Big data imaging; Image analysis; Statistica l modelling; Machine learning; Confounds; Multi-modal data integration; Data modelling
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117002

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.