Microstructural Analysis of Human White Matter Architecture Using Polarized Light Imaging: Views from Neuroanatomy

To date, there are several methods for mapping connectivity, ranging from the macroscopic to molecular scales. However, it is difficult to integrate this multiply-scaled data into one concept. Polarized light imaging (PLI) is a method to quantify fiber orientation in gross histological brain sections based on the birefringent properties of the myelin sheaths. The method is capable of imaging fiber orientation of larger-scale architectural patterns with higher detail than diffusion MRI of the human brain. PLI analyses light transmission through a gross histological section of a human brain under rotation of a polarization filter combination. Estimates of the angle of fiber direction and the angle of fiber inclination are automatically calculated at every point of the imaged section. Multiple sections can be assembled into a 3D volume. We describe the principles of PLI and present several studies of fiber anatomy as a synopsis of PLI: six brainstems were serially sectioned, imaged with PLI, and 3D reconstructed. Pyramidal tract and lemniscus medialis were segmented in the PLI datasets. PLI data from the internal capsule was related to results from confocal laser scanning microscopy, which is a method of smaller scale fiber anatomy. PLI fiber architecture of the extreme capsule was compared to macroscopical dissection, which represents a method of larger-scale anatomy. The microstructure of the anterior human cingulum bundle was analyzed in serial sections of six human brains. PLI can generate highly resolved 3D datasets of fiber orientation of the human brain and has high comparability to diffusion MR. To get additional information regarding axon structure and density, PLI can also be combined with classical histological stains. It brings the directional aspects of diffusion MRI into the range of histology and may represent a promising tool to close the gap between larger-scale diffusion orientation and microstructural histological analysis of connectivity

Derivation of Fiber Orientations From Oblique Views Through Human Brain Sections in 3D-Polarized Light Imaging

3D-Polarized Light Imaging (3D-PLI) enables high-resolution three-dimensional mapping of the nerve fiber architecture in unstained histological brain sections based on the intrinsic birefringence of myelinated nerve fibers. The interpretation of the measured birefringent signals comes with conjointly measured information about the local fiber birefringence strength and the fiber orientation. In this study, we present a novel approach to disentangle both parameters from each other based on a weighted least squares routine (ROFL) applied to oblique polarimetric 3D-PLI measurements. This approach was compared to a previously described analytical method on simulated and experimental data obtained from a post mortem human brain. Analysis of the simulations revealed in case of ROFL a distinctly increased level of confidence to determine steep and flat fiber orientations with respect to the brain sectioning plane. Based on analysis of histological sections of a human brain dataset, it was demonstrated that ROFL provides a coherent characterization of cortical, subcortical, and white matter regions in terms of fiber orientation and birefringence strength, within and across sections. Oblique measurements combined with ROFL analysis opens up new ways to determine physical brain tissue properties by means of 3D-PLI microscopy

Replication Data for: Fast data-driven computation and intuitive visualization of fiber orientation uncertainty in 3D-Polarized Light Imaging

This repository provides the data to replicate the results from: 'Fast data-driven computation and intuitive visualization of fiber orientation uncertainty in 3D-Polarized Light Imaging'. The dataset contains 20 folders for the data of 20 brain sections (XX.zip). For every brain section PLI-Modality Maps (Retardation, Trel, Inclination), PLI-Modality Uncertainty Maps (Direction CI, Inclination CI, Trel CI), and a binary mask are provided

Fast data-driven computation and intuitive visualization of fiber orientation uncertainty in 3D-polarized light imaging

In recent years, the microscopy technology referred to as Polarized Light Imaging (3D-PLI) has successfully been established to study the brain’s nerve fiber architecture at the micrometer scale. The myelinated axons of the nervous tissue introduce optical birefringence that can be used to contrast nerve fibers and their tracts from each other. Beyond the generation of contrast, 3D-PLI renders the estimation of local fiber orientations possible. To do so, unstained histological brain sections of 70 μm thickness cut at a cryo-microtome were scanned in a polarimetric setup using rotating polarizing filter elements while keeping the sample unmoved. To address the fundamental question of brain connectivity, i. e., revealing the detailed organizational principles of the brain’s intricate neural networks, the tracing of fiber structures across volumes has to be performed at the microscale. This requires a sound basis for describing the in-plane and out-of-plane orientations of each potential fiber (axis) in each voxel, including information about the confidence level (uncertainty) of the orientation estimates. By this means, complex fiber constellations, e. g., at the white matter to gray matter transition zones or brain regions with low myelination (i. e., low birefringence signal), as can be found in the cerebral cortex, become quantifiable in a reliable manner. Unfortunately, this uncertainty information comes with the high computational price of their underlying Monte-Carlo sampling methods and the lack of a proper visualization. In the presented work, we propose a supervised machine learning approach to estimate the uncertainty of the inferred model parameters. It is shown that the parameter uncertainties strongly correlate with simple, physically explainable features derived from the signal strength. After fitting these correlations using a small sub-sample of the data, the uncertainties can be predicted for the remaining data set with high precision. This reduces the required computation time by more than two orders of magnitude. Additionally, a new visualization of the derived three-dimensional nerve fiber information, including the orientation uncertainty based on ellipsoids, is introduced. This technique makes the derived orientation uncertainty information visually interpretable.</p

Derivation of Fiber Orientations From Oblique Views Through Human Brain Sections in 3D-Polarized Light Imaging

3D-Polarized Light Imaging (3D-PLI) enables high-resolution three-dimensional mapping of the nerve fiber architecture in unstained histological brain sections based on the intrinsic birefringence of myelinated nerve fibers. The interpretation of the measured birefringent signals comes with conjointly measured information about the local fiber birefringence strength and the fiber orientation. In this study, we present a novel approach to disentangle both parameters from each other based on a weighted least squares routine (ROFL) applied to oblique polarimetric 3D-PLI measurements. This approach was compared to a previously described analytical method on simulated and experimental data obtained from a post mortem human brain. Analysis of the simulations revealed in case of ROFL a distinctly increased level of confidence to determine steep and flat fiber orientations with respect to the brain sectioning plane. Based on analysis of histological sections of a human brain dataset, it was demonstrated that ROFL provides a coherent characterization of cortical, subcortical, and white matter regions in terms of fiber orientation and birefringence strength, within and across sections. Oblique measurements combined with ROFL analysis opens up new ways to determine physical brain tissue properties by means of 3D-PLI microscop