Association and prediction of phenotypic traits from neuroimaging data using a multi-component mixed model excluding the target vertex

Mass-univariate association analyses aim at mapping the brain regions associated with a trait/disorder. The correlation between vertices may cause a true association to spread locally (cluster of association) or distally (false positive cluster). We previously showed that controlling for all vertices in the model (using a linear mixed model: LMM), could greatly reduce the probability of false positive and improve mapping precision. Here, we investigated a new LMM called MOMENT which reduces false positive rate in methylome-wide association studies. Compared to LMM, MOMENT had enhanced power and mapping precision but failed at reducing the rates of false positives clusters.The increasing sample sizes from neuroimaging studies should allow detection of image measures are associated with phenotypic traits with smaller effect sizes, which will advance progress in the mapping of the brain regions associated with traits and diseases [1]. The UK Biobank (UKB) is one of the best example of this new generation of samples [2]. Multimodal Brain MRI collection is currently ongoing, with tens of thousands of individuals already imaged out of a target of 100,000 [2]. The large sample size, together with the breadth of phenotyping (incl. self-reports, in lab assessments, prescription and medical history), should allow new insights into the factors contributing to brain differences between older adults.