How complex analyses of large multidimensional datasets advance psychology – examples from large-scale studies on behavior, brain imaging, and genetics

Psychology investigates the interplay of human mind, body, and its environment in health and disease. Fully understanding these complex interrelations requires comprehensive analyses across multiple modalities and multidimensional datasets. Large-scale analyses on complex datasets are the exception rather than the rule in current psychological research. At the same time, large and complex datasets are becoming increasingly available. This thesis points out benefits, challenges and adequate approaches for analyzing complex multidimensional datasets in psychology. We applied these approaches and analysis strategies in two studies. In the first publication, we reduced the dimensionality of brain activation during a working memory task based on data from a very large sample. We observed that a mainly parietally-centered brain network was associated with working memory performance and global measures of white matter integrity. In the second publication, we exhaustively assessed pairwise interaction effects of genetic markers onto epigenetic modifications of the genome. Such modifications are complex traits that can be influenced by the environment and in turn affect development and behavior. The lack of observed strong interaction effects in our study suggested that focusing on additive effects is a suitable approach for investigating the link between genetic markers and epigenetic modifications. Both studies demonstrate how psychological scientists can exploit large complex datasets by applying adequate research practices and methodologies. Further adopting these approaches will prepare psychological research for harnessing large and complex datasets, leading towards a better understanding of mental health and disease.