  Biological neural networks define the brain function and intelligence of
humans and other mammals, and form ultra-large, spatial, structured graphs.
Their neuronal organization is closely interconnected with the spatial
organization of the brain's microvasculature, which supplies oxygen to the
neurons and builds a complementary spatial graph. This vasculature (or the
vessel structure) plays an important role in neuroscience; for example, the
organization of (and changes to) vessel structure can represent early signs of
various pathologies, e.g. Alzheimer's disease or stroke. Recently, advances in
tissue clearing have enabled whole brain imaging and segmentation of the
entirety of the mouse brain's vasculature. Building on these advances in
imaging, we are presenting an extendable dataset of whole-brain vessel graphs
based on specific imaging protocols. Specifically, we extract vascular graphs
using a refined graph extraction scheme leveraging the volume rendering engine
Voreen and provide them in an accessible and adaptable form through the OGB and
PyTorch Geometric dataloaders. Moreover, we benchmark numerous state-of-the-art
graph learning algorithms on the biologically relevant tasks of vessel
prediction and vessel classification using the introduced vessel graph dataset.
Our work paves a path towards advancing graph learning research into the field
of neuroscience. Complementarily, the presented dataset raises challenging
graph learning research questions for the machine learning community, in terms
of incorporating biological priors into learning algorithms, or in scaling
these algorithms to handle sparse,spatial graphs with millions of nodes and
edges. All datasets and code are available for download at
https://github.com/jocpae/VesselGraph .
