We assess the potential of detecting cortical laminar patterns and areal borders by directly clustering voxel values of microstructural parameters derived from high-resolution mean apparent propagator (MAP) magnetic resonance imaging (MRI), as an alternative to conventional template-warping-based cortical parcellation methods. We acquired MAP-MRI data with 200μm resolution in a fixed macaque monkey brain. To improve the sensitivity to cortical layers, we processed the data with a local anisotropic Gaussian filter determined voxel-wise by the plane tangent to the cortical surface. We directly clustered all cortical voxels using only the MAP-derived microstructural imaging biomarkers, with no information regarding their relative spatial location or dominant diffusion orientations. MAP-based 3D cytoarchitectonic segmentation revealed laminar patterns similar to those observed in the corresponding histological images. Moreover, transition regions between these laminar patterns agreed more accurately with histology than the borders between cortical areas estimated using conventional atlas/template-warping cortical parcellation. By cross-tabulating all cortical labels in the atlas- and MAP-based segmentations, we automatically matched the corresponding MAP-derived clusters (i.e., cytoarchitectonic domains) across the left and right hemispheres. Our results demonstrate that high-resolution MAP-MRI biomarkers can effectively delineate three-dimensional cortical cytoarchitectonic domains in single individuals. Their intrinsic tissue microstructural contrasts enable the construction of whole-brain mesoscopic cortical atlases.
Keywords: MAP-MRI; clustering analysis; cortical areas; cortical layers; cortical parcellation; cytoarchitecture; diffusion anisotropy; diffusion propagator; histological validation; quantitative biomarker; unsupervised.