Mathematical Modeling I: Convergent data analysis and statistics
The goal of this mathematical modeling multicenter subproject 6 of the eMED Alcohol Addiction Consortium: A Systems-Oriented Approach is to assess neurobehavioral risk profiles including genetic risk factors in adolescents that are predictive of alcohol binging and alcohol use disorders later in life. Statistical techniques from the area of machine learning are used to build and test prediction models incorporating genetic, neuropsychological, neuroimaging, and behavioral data from a database of adolescents (IMAGEN, SP4), and a data collection of adults (NGFNplus, eMEd SP10), respectively.
The aim is to identify predictors within the IMAGEN population (SP4) that are predictive for binge drinking at the age of 19 years, which will then be compared to predictors of an established alcohol addiction identified in the NGFN sample (SP10). As a first step, structural and functional features are identified in the brains of the 19 year old adolescents that differentiate them with respect to their drinking behavior. It will then be investigated whether the same features are (at least partially) already present at the age of 14 years. Finally, the influence of genetic and other factors on the identified structural and functional changes in the brain will be analyzed.
Keywords: alcoholism, genetics, endophenotyping, MRT, imaging genetics