Automated segmentation of cortical layers in BigBrain reveals divergent cortical and laminar thickness gradients in sensory and motor cortices.

Large-scale in vivo neuroimaging datasets offer new possibilities for reliable, well-powered measures of interregional structural differences and biomarkers of pathological changes in a wide variety of neurological and psychiatric diseases. However, it has been impossible to determine the cortical layer or neurobiological processes causing these changes. We developed artificial neural networks to segment cortical and laminar surfaces in the BigBrain, a 3D histological model of the human brain, to test whether gradients of MRI thickness in sensory and motor processing cortices were present in a histological atlas of cortical thickness, and which cortical layers were contributing to these gradients. Identifying common gradients of cortical organisation enables the formation of mechanistic links between microstructural, macrostructural and functional cortical parameters. In our fully segmented 6-layered model of the cerebral isocortex, we found that histological thickness corroborated MRI thickness gradients in sensory cortices but was the inverse from those reported in fronto-motor cortices, with layers III, V and VI being the primary drivers of these thickness gradients. Our laminar atlas creates a link between single-neuron morphological changes, mesoscale cortical layers and macroscale cortical thickness, and our findings suggest that across multiple measurement domains, the primary motor cortex should not be indiscriminately grouped with primary sensory areas.