The method 3D polarised light imaging (3D-PLI) measures the birefringence of histological brain sections to determine the spatial course of nerve fibres (myelinated axons). While the in-plane fibre directions can be determined with high accuracy, the computation of the out-of-plane fibre inclinations is more challenging because they are derived from the amplitude of the birefringence signals, which depends e.g. on the amount of nerve fibres. One possibility to improve the accuracy is to consider the average transmitted light intensity (transmittance weighting). The current procedure requires effortful manual adjustment of parameters and anatomical knowledge. Here, we introduce an automated, optimised computation of the fibre inclinations, allowing for a much faster, reproducible determination of fibre orientations in 3D-PLI. Depending on the degree of myelination, the algorithm uses different models (transmittance-weighted, unweighted, or a linear combination), allowing to account for regionally specific behaviour. As the algorithm is parallelised and GPU optimised, it can be applied to large data sets. Moreover, it only uses images from standard 3D-PLI measurements without tilting, and can therefore be applied to existing data sets from previous measurements. The functionality is demonstrated on unstained coronal and sagittal histological sections of vervet monkey and rat brains.

Unraveling the structure and function of the brain requires a detailed knowledge about the neuronal connections, i.e., the spatial architecture of the nerve fibers. One of the most powerful histological methods to reconstruct the three-dimensional nerve fiber pathways is 3D-polarized light imaging (3D-PLI). The technique measures the birefringence of histological brain sections and derives the spatial fiber orientations of whole human brain sections with micrometer resolution. However, the technique yields only a single fiber orientation for each measured tissue voxel even if it is composed of fibers with different orientations, so that in-plane crossing fibers are misinterpreted as out-of-plane fibers. When generating a detailed model of the three-dimensional nerve fiber architecture in the brain, a correct detection and interpretation of nerve fiber crossings is crucial. Here, we show how light scattering in transmission microscopy measurements can be leveraged to identify nerve fiber crossings in 3D-PLI data and demonstrate that measurements of the scattering pattern can resolve the substructure of brain tissue like the crossing angles of the nerve fibers. For this purpose, we develop a simulation framework that permits the study of transmission microscopy measurements - in particular, light scattering - on large-scale complex fiber structures like brain tissue, using finite-difference time-domain (FDTD) simulations and high-performance computing. The simulations are used not only to model and explain experimental observations, but also to develop new analysis methods and measurement techniques. We demonstrate in various experimental studies on brain sections from different species (rodent, monkey, and human) and in FDTD simulations that the polarization-independent transmitted light intensity (transmittance) decreases significantly (by more than 50%) with an increasing out-of-plane angle of the nerve fibers and that it is mostly independent of the in-plane crossing angle. Hence, the transmittance can be used to distinguish regions with low fiber density and in-plane crossing fibers from regions with out-of-plane fibers, solving a major problem in 3D-PLI and allowing for a much better reconstruction of the complex nerve fiber architecture in the brain. Finally, we show that light scattering (oblique illumination) in the visible spectrum reveals the underlying structure of brain tissue like the crossing angle of the nerve fibers with micrometer resolution, enabling an even more detailed reconstruction of nerve fiber crossings in the brain and opening up new fields of research.

In this work, we employ an integrated label-free dual approach that combines 3DPolarized light imaging with two-photon fluorescence microscopy to study the mixture of various fiber orientations within the sample of interest.