Enhancement of brain atlases with laminar coordinate systems: Flatmaps and barrel column annotations

Abstract

Digital brain atlases define a hierarchy of brain regions and their locations in three-dimensional space. They provide a standard coordinate system in which diverse datasets can be integrated for visualization and analysis. They also enable building of data-driven computational models of brain regions. For atlases of the cerebral cortex, additional information is required to work effectively with its particular, layered architecture and curved geometry. Although some approaches have been employed in the literature, no usable method to produce such information is openly available. To fill this gap, we describe here methods to enhance a cortical atlas with three auxiliary, voxel-wise datasets: first, a field of cortical depth; second, a field of local orientations towards the cortical surface; and third, a flatmap of the cortical volume: a two-dimensional map where each pixel represents a subvolume of voxels along the depth axis, akin to a cortical column. We apply these methods to the somatosensory regions of a digitized version of Paxinos and Watson's rat brain atlas, and define metrics to assess the quality of our results. Among the many applications of the resulting flatmap, we show their usefulness for: decomposing the cortical volume into uniform columnar subvolumes and defining a topographic mapping for long-range connections between subregions. We also generate a flatmap of the isocortex regions of the Allen Mouse Common Coordinate Framework. Combining this with established two-photon tomography data, we then annotate individual barrels and barrel columns in the mouse barrel cortex. Finally, we use the flatmap to visualize volumetric data and long-range axons. We provide an open source implementation of our methods for the benefit of the community.

Cortical cell assemblies and their underlying connectivity: An in silico study

“…In detail, the 5388 VPM fibers innervating the simulated volume were first restricted to be ≤ 500 μ m from the middle of the circuit in the horizontal plane to avoid boundary artifacts. To measure these distances we use a flat map, i.e., a two dimensional projection of the volume onto the horizontal plane, orthogonal to layer boundaries [ 37 , 71 ]. Second, the flat map locations of the resulting 3017 fibers were clustered using k-means to form 100 bundles of fibers.…”

A connectome manipulation framework for the systematic and reproducible study of structure function relationships through simulations

“…As depicted in Figure 2A, we applied connectome manipulations only to connections between neurons within the central cortical column of the seven column subvolume, in order to avoid potential edge effects. For all manipulations with geometry-dependent connection probabilities (i.e., 2 nd to 5 th order), we used the flat coordinate system mapping released in Bolaños-Puchet et al (2024) under 10.5281/zenodo.10686776, with the x/y-axes parallel to the cortical layers, and the z-axis along the cortical depth.…”

“…We then rewired the connectivity originating from BTC and SBC interneurons in the central cortical column using the code module (see Table S3, 3, and S4). We employed the storing the adjacency matrix as deterministic connectivity description, i.e., containing only connection probability values of zeros and ones as determined by the matrix.…”

“…We rewired the whole connectivity between excitatory neurons within the central cortical column of the baseline connectome using the code module (see Table S3, 3, and S4) based on the stochastic 1 st to 5 th order connectivity models that had been fitted against the baseline connectome beforehand. For each of the connectivity models, we did this in an iterative way in order to match the total number of connections in the rewired connectomes to the baseline, using the procedure detailed in Section 4.1.4.…”

“…Coordinate mapping for the SSCx network model to a flat coordinate system aligned with the cortical layers, which has been released in Bolaños-Puchet et al (2024) under 10.5281/zenodo.10686776.…”