  Cytoarchitecture describes the spatial organization of neuronal cells in the
brain, including their arrangement into layers and columns with respect to cell
density, orientation, or presence of certain cell types. It allows to segregate
the brain into cortical areas and subcortical nuclei, links structure with
connectivity and function, and provides a microstructural reference for human
brain atlases. Mapping boundaries between areas requires to scan histological
sections at microscopic resolution. While recent high-throughput scanners allow
to scan a complete human brain in the order of a year, it is practically
impossible to delineate regions at the same pace using the established gold
standard method. Researchers have recently addressed cytoarchitectonic mapping
of cortical regions with deep neural networks, relying on image patches from
individual 2D sections for classification. However, the 3D context, which is
needed to disambiguate complex or obliquely cut brain regions, is not taken
into account. In this work, we combine 2D histology with 3D topology by
reformulating the mapping task as a node classification problem on an
approximate 3D midsurface mesh through the isocortex. We extract deep features
from cortical patches in 2D histological sections which are descriptive of
cytoarchitecture, and assign them to the corresponding nodes on the 3D mesh to
construct a large attributed graph. By solving the brain mapping problem on
this graph using graph neural networks, we obtain significantly improved
classification results. The proposed framework lends itself nicely to
integration of additional neuroanatomical priors for mapping.
