Epilepsy is increasingly recognized as a network-level disorder, rather than one confined to isolated brain regions. This shift in understanding emphasizes the importance of analyzing the brain's structural and functional connectivity to uncover epileptogenic networks and explain seizure propagation. Diffusion MRI (dMRI), is among the few non-invasive imaging techniques that allow in vivo reconstruction of the intricate white matter pathways of the brain. It plays a key role in the effort to uncover epileptogenic networks by enabling tractography to reveal the underlying architecture of these networks.
CoBundleMap Parametrization
Manifold-Learning allows a parametrization along the tract to compare tracts across hemispheres and subjects
Rotation Visualization of a Whole Brain Tractography
Vertices are colored by local directionality
Furthermore, it helps to understand structural reorganization in the presence of lesions or after insults or surgery. To move beyond region-based analyses, we develop methodologies that leverage deep-learned whole-brain parametrizations to encode complex anatomical features into compact representations that are comparative across subjects and hemispheres.
Alterations of local and global network topology
Epilepsia, 2020, Bauer et al.
Additionally, our research focuses on streamline-centric graph models and connectivity evaluation offer a promising direction by directly modeling the brain’s connectivity at the streamline level, preserving fine-grained topological information critical for identifying subtle network disruptions. Together with the Institute for Informatics II of the University Bonn, we develop methods to support a more comprehensive characterization of the pathways involved in epilepsy and pre-surgical evaluation, paving the way for targeted interventions and improved diagnostics.