Abstract
Abstract Total intracranial volume (TIV) is a major confounding factor in neuroimaging studies, particularly when studying sex differences in the brain. Different methods have been proposed to adjust for this effect, however, their impact has not been directly studied and compared. Furthermore, when studying cortical metrics at the vertex level, the choice of smoothing level can impact analysis outcomes which can in turn impact the degree of TIV based biases and the effectiveness of the correction methods. In this study, we sought to evaluate the impact of four most commonly used adjustment methods in the literature on the estimations of neuroanatomical sex differences. These methods included: the proportions method, the residuals method, the power corrected proportions method, and adding TIV as a covariate in a regression analysis. Leveraging data from the UK Biobank, we employed a matching approach to devise a gold standard as reference for comparing TIV correction methods. To achieve this, we matched the male and female participants based on age and TIV to remove the impact of TIV differences between sexes. We further modeled aging trajectories at the regional level, vertexwise using data with different smoothing levels, and voxelwise, using raw and adjusted values, and compared the obtained estimates against the gold standard. We found that across different metrics, adding TIV as a covariate was the best-performing method for removing the effect of TIV, in terms of the correlation between the estimates of the different subsamples and the gold standard as well as the degree of estimation bias. Furthermore, we showed that the commonly used smoothing of the morphometric measures can result in biased estimation of sex differences in these measures. Finally, we showed that while small in effect size, there still remains some neuroanatomically specific uncorrected effects for all adjustment methods.