Columnar structures in the human visual cortex are studied using high-resolution fMRI methods in order to localize the actual source of neural processing more precisely.
We used high-resolution fMRI and multivariate pattern analysis (MVPA) to explore how attentional modulation of working memory affects laminar specific representations in dorsolateral prefrontal cortex (dlPFC).
We performed laminar fMRI during a delayed match-to-sample task and varied working memory load and the requirement for a motor response. We found layer specific univariate and multivariate effects.
We linked the effective transverse relaxation rate R2* with dopaminergic cell densities and iron concentrations in nigrosome 1 by combining 3D quantitative iron histology, post mortem ultra-high resolution MRI, tissue deironing, and analytical modeling approaches.
A recent fMRI study showed layer-specific responses in the dorsolateral prefrontal cortex during a working memory task. We attempted to replicate the original findings using newly acquired data and a fully automated analysis.
We characterize the cortical layers by biomechanical modeling and simulation of the developed human cortex tissue in-vivo using hyperelastic material models.
In this project, we studied cortical myelin in living humans at the spatial scale of cortical columns using high-resolution quantitative magnetic resonance imaging (MRI) methods at 7 T.
We explore spatially resolved lipid imaging using matrix-assisted laser desorption/ionization (MALDI) as a method for validating MRI-based myelin biomarkers.
We investigate the relationship between quantitative MRI (qMRI) at different cortical depths and cell counts, gene expression and white matter connections in the brain in order to provide novel biomarkers for tracking neurodegenerative diseases.
Robust U-fibre connectivity mapping can be achieved in vivo in the early visual processing stream using combined diffusion weighted imaging and functional retinotopy