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Approved research

A study of decision making using UK Biobank and fMRI studies of economic choices

Principal Investigator: Professor Aldo Rustichini
Approved Research ID: 31028
Approval date: March 15th 2018

Lay summary

The main goal of the research is the systematic integration of genetic and brain activation data to explain the underpinnings of economic and social choice, and use the results to understand neurocognitive and mental health outcomes. We develop a novel approach integrating functional neuro-imaging and the genetic data using both Bayesian and classical statistical methods. We plan to build a Hierarchical Bayesian model and use other classical statistic method of decision making. Analysis of UK Biobank data will be cross-validated with independent samples available at the University of Minnesota, relevant for the study of schizophrenia and addiction. The research will address health-related questions in the public interest both directly and indirectly. Directly, through the application of the study to causal factors of schizophrenia and addiction (both drug/alcohol and behavioral). Indirectly because it will improve the understanding of the impact of cognitive functions on important life outcomes, which eventually affect health conditions. The specific aim of this study is to examine association signals from SNPs, examining how much of the variation is captured by examining all SNPs simultaneously (using Polygenic Risk Scores), and look at the extent to which SNPs that predict variation in two joint sets of phenotypes (in our case, patterns of brain activation and choice). For this purpose we will use statistical techniques (Gene Set Enrichment Analysis, Group Lasso) testing the hypotheses that specific biological pathways affect jointly the choices and brain activity implementing it. We will integrate several data sets for cross-validation. The research requires data on the full cohort of subjects available. In particular we need the full genotype data and the raw imaging data. We are aware that this will require large data storage, so a clarification is needed. In our team the effort is aimed at integrating the analysis of the UK Biobank data and the Human Connectome Project ( For this we need in addition to functional and T1 data the T2 data. We also like to be sure that the geometric B0 correction is done properly.