Deep brain: decision mechanisms in deep brain networks
Making everyday life decisions is vital to our survival and relies as much on cerebral cortex as on regions that reside Deep in our Brain, namely the subcortex. Studies on networks connecting small nuclei in the Deep Brain with cortical regions can provide key insights into learning, memory-guided, economic, and perceptual decision-making processes. However, the structural and functional organization of these processes is still incompletely understood, highlighting a knowledge gap in the field. The aim of this proposal is to reduce the gap in functional understanding by studying the mechanisms of different decision-making domains implemented in Deep Brain networks. 
The human brain consists of densely connected networks of interacting areas and nuclei, and while the cerebral cortex has been mapped with great precision, research on the human subcortex has been relatively sidelined (Forstmann et al., Nat Rev Neurosci, 2017). The subcortex contains hundreds of small grey matter nuclei, which take up approximately 1/4th of the entire human brain volume. Importantly, only approximately 7% of these nuclei are currently accessible in standard human brain magnetic resonance imaging (MRI) atlases. Major ongoing efforts in my group have focused on structural imaging of the subcortex, thereby removing an important technical barrier. These efforts have paved the way for functional studies on the role of Deep Brain in decision making as described in the current proposal.
Ultra-high-field (UHF) 7Tesla (T) MRI and one of the three world-wide available Connectom MRI will be used to investigate decision mechanisms in Deep Brain networks. Three projects are proposed using a strong theory-driven and statistically high-powered model-based approach to combine beyond the-state-of-the-art techniques ranging from cognitive neuroscience, experimental psychology, to quantitative modeling.