Improved Identification of Brain Activity to Predict Human Intelligence by using Machine Learning

Cognitive neuroscience has recently shown that functional and structural brain data (as assessed with an MRI scanner) can predict individual intelligence scores. However, many preposing steps are involved and associated with many degrees of freedom so that the resulting “cleaned” brain signal is only a coarse approximation of the “true” underlying brain activity. This thesis aims to develop a new machine learning-based MRI artefact correction method. We will start with substituting single preprocessing steps and investigate how much of the common methodology can be replaced by more data-driven methods utilizing machine learning - without reducing prediction accuracy of phenotypic measures like intelligence, personality and age.

Betreuer: Andreas Hotho, Kirsten Hilger