Figure 1 from: Huntenburg J, Wagstyl K, Steele C, Funck T, Bethlehem R, Foubet O, Larrat B, Borrell V, Bazin P (2017) Laminar Python: tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python. Research Ideas and Outcomes 3: e12346. https://doi.org/10.3897/rio.3.e12346

Figure 1 - Laminar python pipeline, demonstrated using high-resolution MR data of a ferret brain. a) Binary images demarcating inner (grey-white matter interface, top) and outer (pial surface, bottom) boundaries of the cortex. b) Levelset representations of the same surfaces, where positive values are assigned to voxels outside of the volume deliminated by the surface, and negative values to voxels inside, each increasing in value with euclidean distance from the surface. c) Continuous equivolumetric intracortical depth, which models the positions of laminae relative to cortical morphology. d) Discrete representations of equivolumetric depth levels. e) T2 values, sampled at the six equivolumetric intracortical depths. Note that the equivolumetric laminae do not represent architectonic layers, but provide an anatomically meaningful coordinate system of cortical depth.