Mapping the brain's fiber network is crucial for understanding its function and malfunction, but resolving nerve trajectories over large fields of view is challenging. Electron microscopy only studies small brain volumes, diffusion magnetic resonance imaging (dMRI) has limited spatial resolution, and polarization microscopy provides unidirectional orientations in birefringence-preserving tissues. Scattered light imaging (SLI) has previously enabled micron-resolution mapping of multi-directional fibers in unstained brain cryo-sections. Here, we show that using a highly sensitive setup, computational SLI (ComSLI) can map fiber networks in histology independent of sample preparation, also in fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissues, including whole human brain sections. We showcase this method in new and archived, animal and human brain sections, for different stains and steps of sample preparation (in paraffin, deparaffinized, stained). Employing novel analyses, we convert microscopic orientations to microstructure-informed fiber orientation distributions (μFODs). Adapting MR tractography tools, we trace axonal trajectories via orientation distribution functions and microstructure-derived tractograms, revealing white and gray matter connectivity. These allow us to identify altered microstructure in multiple sclerosis and leukoencephalopathy, reveal deficient tracts in hippocampal sclerosis and Alzheimer's disease, and show key advantages over dMRI, polarization microscopy, and structure tensor analysis. Finally, we map fibers in non-brain tissues -including muscle, bone, and blood vessels- unveiling the tissue's function. Our cost-effective, versatile approach enables micron-resolution studies of intricate fiber networks across tissues, species, diseases, and sample preparations, offering new dimensions to neuroscientific and biomedical research.

Sample preparation

Whole human brain (BigBrain) sections

The silver-stained human brain section (Fig. 1, Supplementary Movie 1, and Supplementary Figs. 1, 2, and 10) was obtained from a 30-year-old male body donor without neurological disorders. The brain was removed within 24 hours after death, fixed in 4% formalin, dehydrated in increasing alcohol series (80%, 90%, 96%, 100% ethanol for at least one week), and embedded in 57-60°C paraffin solution for two to three months. For a more detailed description, see Amunts et al., 2013¹¹. Subsequently, the brain was coronally cut into 20 µm-thin sections from anterior to posterior with a large-scale microtome (Leica SM2500 Microtome) and mounted. The sections were placed in a decreasing alcohol series to remove the paraffin, stained with silver following the protocol of Merker⁶² to highlight neuronal cell bodies, and mounted on glass slides. The sections are part of the so-called second BigBrain data set⁶³, 3D-reconstructed with the same spatial resolution of 20μm isotropic, such as the original Jülich BigBrains¹¹. For our study, we used section no. 3452.

The Cresyl-violet stained human brain sections (Fig. 4, and Supplementary Figs. 8, 9, and 11) were obtained from a 71-year-old male body donor without neurological disorders. The brain was prepared as described above, but stained with Cresyl-violet instead of silver. For our study, sections no. 3301 (Fig. 4, and Supplementary Figs. 8 and 9) and 2520 (Supplementary Fig. 11) were selected and measured one and a half years after tissue preparation.

The body donors gave written informed consent for the general use of postmortem tissue in this study for the aims of research and education. The usage is covered by a vote of the ethics committee of the medical faculty of the Heinrich Heine University Düsseldorf, Germany (#4863).

120-year-old myelin-stained human brain section

The myelin-stained human brain section (Fig. 2B) comes from the brain collection of the Cécile and Oskar Vogt Institute for Brain Research, Heinrich Heine University Düsseldorf, Germany. The brain of a 25-year-old male was embedded in celloidin and stained according to Weigert’s iron hematoxylin myelin staining in 1904⁶⁴.

Human hippocampus, cortex, and pathology FFPE brain sections

Four-millimeter thick formalin-fixed human specimens were dehydrated in increasing ethanol steps (70% x2, 95% x2, 100% x3, 3.5hrs each step), cleared in xylene (3.5hrs x2), paraffin-embedded (3.5hrs x2), and sectioned into 5µm-thin sections. The sections were de-waxed and stained with agents as indicated. The hippocampal sections in Fig. 2A and Supplementary Fig. 3 were from an 89-year-old male with Alzheimer’s pathology, stained against microglia (CD163), Perl’s iron with Diaminobenzidine (DAB) enhancement, tau, and amyloid, with hematoxylin counterstain where indicated. Sections from brains with multiple sclerosis (80 years old, male, from temporal periventricular white matter and cortex) and leukoencephalopathy (43 years old, male, from periventricular white matter and cingulum) were stained with hematoxylin and eosin, luxol fast blue plus hematoxylin and eosin, and neurofilament (2F11) (Fig. 3 and Supplementary Figs. 5-6). Hippocampal and visual cortex sections in Supplementary Fig. 4 were from a 60-year-old male, stained with hematoxylin & eosin, and a 67-year-old female, stained with luxol fast blue, respectively. The sclerotic hippocampal section in Supplementary Fig. 7 was from a 69-year-old female with epilepsy, the control was from a 66-year-old female with no neuropathologic abnormality, and the AD tau-stained hippocampus was the same as in Fig. 2A, as described above. Specimens were acquired under Stanford ADRC IRB (Assurance nr. FWA00000935).

Fresh-frozen human hippocampus and visual cortex sections

The human hippocampus and primary visual cortex fresh-frozen sections (Supplementary Fig. 5) were from an 88-year-old male with Lewy Body Disease, low Alzheimer’s disease pathology, and cerebrovascular dementia. The brain was processed according to Stanford ADRC procedures; after autopsy, it was cut into 5mm coronal slabs and frozen using frozen metal plates in dry ice. The specimens were excised from the frozen slab, and 30μm sections were cut using a cryostat. The sections were uncover-slipped and let thaw under the microscope, which happened in the first ~100 seconds given their thickness. Specimens were acquired under Stanford ADRC IRB (Assurance nr. FWA00000935).

Mouse brain section

A female ~10-week-old C57BL/6 mouse (Jackson Laboratories) was housed in a temperature-controlled environment, with a 12-hour light/dark schedule and ad libitum food/water access. It was euthanized for the purposes of a different study (APLAC #32577) under anesthesia with 2-3% isoflurane followed by cardiac puncture and perfusion with 20 mL phosphate-buffered saline (PBS). The brain was harvested, kept in 4% paraformaldehyde (PFA) in PBS for 24 hours at 4°C, transferred to 10%, 20%, and 30% sucrose in PBS, embedded in Tissue-Tek O.C.T. in dry ice for 1 hour, and cut sagittally into 10μm sections using a cryotome (Leica CM1860). The sections were subsequently washed, mounted on a glass slide, incubated with Iba1 antibody (dilution 1:200), secondary antibody (goat anti-rabbit Cy3 1:200), and cover-slipped. A mid-sagittal section was selected for evaluation (Fig. 2G-J).

Pig brain section

A 4-week female Yorkshire pig (#2) was euthanized for a different study (Stanford APLAC protocol nr 33684), the brain was harvested, cut into 5-mm coronal slabs using a brain slicer, and a mid-frontal slab (#5) was paraffin-embedded, similar to the human pathologic specimen preparation above. The slab was cut in 10μm sections using a Leica HistoCore AUTOCUT microtome. After deparaffinization, a section (#127) was stained with hematoxylin and eosin and cover-slipped (Fig. 2K-N).

Human tongue, colorectal, bone, and artery wall sections

The non-brain tissue sections (Fig. 5 and Supplementary Fig. 12) were obtained from a tissue archive at Erasmus Medical Center, Rotterdam, the Netherlands, approved by the Medisch Ethische Toetsing Commissie (METC) under number MEC-2023-0587. The tissue samples were obtained from patients during surgery. The bone sample was decalcified first using DecalMATE by Milestone Medical. Afterwards, all samples were fixed in 4% formaldehyde for 24 hours, dehydrated in increasing alcohol series (70%, 80%, 90%, 96%, 100% ethanol), treated with xylene, embedded in paraffin, and cut with a microtome (Leica RM2165) into 4μm-thin sections. The sections were placed in a decreasing alcohol series to remove the paraffin, mounted on glass slides, stained with hematoxylin and eosin (artery wall with Verhoeff-Van Gieson elastin staining), and then cover-slipped.

Brightfield microscopy

The whole human brain sections were scanned with the TissueScope LE120 Slide Scanner by Huron Digital Pathology, Huron Technologies International Inc. The device measures in brightfield mode with 20X magnification and 0.74 NA, providing a pixel size of 0.4µm. The final images were stored with a pixel size of 1µm.

The hippocampus, cortex, pathology, and animal brain sections were scanned using an Aperio AT2 whole slide scanner with the ImageScope software and a 20X magnification, resulting in brightfield images with a pixel size of 0.5μm.

The stained non-brain microscopy slides were scanned using the Nanozoomer 2.0 HT digital slide scanner by Hamamatsu Photonics K.K., offering a 20X magnification and a pixel size of 0.46µm. The unstained non-brain microscopy slides were scanned using the Keyence VHX-6000 Digital Microscope (with VH-ZST objective, 20X), with a pixel size of 10μm.

ComSLI

Whole human brain (silver-stained), hippocampus, cortex, pathology, and animal brain sections

Measurements were performed with a rotating light source and camera (cf. Fig. 1B), using a Flexacam C3 12 MP microscope camera (Leica) and a Navitar 12X Zoom Lens with a 0.67X Standard Adapter and a 0.5X Lens Attachment, with 4.25-9μm pixel size, as indicated in the figure captions. As a light source, an ADJ Pinspot LED II was used, with a 5.1 cm diameter and 3.5° full-angle of divergence, oriented at ~45° with respect to the sample plane. A motorized specimen stage enabled the whole-human-brain section scanning in 8x5 tiles, all other brain sections were scanned at a single tile. Images were acquired at 10o rotation steps (36 images/sample) with 125ms exposure time, except for the sections in paraffin that gave very strong scattering and were imaged with 7ms exposure time. Prior to the measurement, a 100mm diameter diffuser plate (Thorlabs) was measured for calibration (see below for calibration details).

Whole human brain (Cresyl-violet) and non-brain tissue sections

Measurements were similarly performed with a rotating light source and camera (cf. Fig. 1B), using a fiber-coupled LED light source consisting of an ultra-high power LED (UHP-FB-W50 by Prizmatix) with 400-750nm wavelength (peak at 443nm), 2-meter long step-index multimode silica (low OH) fiber patch cord (Thorlabs), a 25.4 mm diameter collimating optics (FCM1-0.5-CN by Prizmatix), and a 25.4 mm diameter engineered diffuser (beam shaper) for homogenizing the illumination (ED1-S20-MD by Thorlabs), yielding top-hat beam with 20° full-angle of divergence. The exposure time was adjusted manually per sample for maximizing the dynamic range of the captured signal while avoiding saturation (range: 50-100 ms). The light source was oriented at ~45° with respect to the sample and rotated with a motorized specimen stage (ZABER X-RSB060AD-E01-KX14A) in steps of 15° (24 images/sample). Images were taken with a 20 MP monochromatic CMOS camera (BASLER acA5472-17um) and a Rodenstock Apo-Rodagon-D120 Lens, yielding a pixel size of 3μm (4μm optical resolution) and a field-of-view of 16x11mm². A motorized specimen stage was used to perform whole-slide scanning. Prior to the measurement, a diffuser plate (DG100x100 N-BK7 ground glass diffuser, 1500 grit, Thorlabs) was measured for calibration.

120-year-old myelin-stained brain section

The measurement was performed with a similar camera and lens as for the non-brain tissue sections (BASLER acA5472-17uc and Rodenstock Apo-Rodagon-D120), using an LED display instead of a focused light source (50x50 cm², 128x128 RGB-LEDs, Absen Polaris 3.9pro In/Outdoor LED Cabinet). The sample was illuminated by a green circle segment (9° azimuthal and polar widths, respectively) with an effective illumination angle of 47°, which was rotated in 15° steps. Images were taken with 10-second exposure time and a gain of 10, and 4 images were averaged per illumination angle to increase signal-to-noise.

ComSLI image analysis

Flat-field correction

Prior to each measurement session, a diffuser plate was measured under similar conditions. A 100-pixel Gaussian blur was applied to diffuser images to homogenize defects. Subsequently, the blurred images of all angles were divided by the average of their maxima for normalization. These normalized diffuser images were used to calibrate the measured tissue images, aiming to account for the uneven illumination across the field of view for each image: Each tissue image was divided by its corresponding normalized diffuser image of the same illumination angle.

Generation of fiber orientation and vector maps

Each calibrated image series from a ComSLI measurement was evaluated with the open-source software SLIX³⁴, which analyzes the position of scattering peaks to compute the fiber orientations and visualize them in color-encoded maps, using multi-colored pixels and colored vector lines. Measurements with 15° azimuthal steps were processed without filtering. Measurements with 10° azimuthal steps were processed with a Fourier low-pass filter (40% cutoff frequency, 0.225 window width) before generating the parameter maps, as described in ²².

Microstructure-derived fiber orientation distributions (μFODs)

To calculate and plot the μFODs, the ComSLI fiber orientation map was partitioned into pixel kernels depending on the intended μFOD resolution. For instance, for a ComSLI dataset with pixel size of 7μm and intended μFOD resolutions of ~50 or ~500μm, a 7x7 or 71x71 pixel kernel was used respectively (Fig. 1H). Similarly, to calculate μFODs for specific regions of interest, e.g., the lesions in pathology (Fig. 3), orientations of all pixels in the area were considered. All fiber orientations of all pixels within each kernel or area of interest were then rendered as a polar histogram in 20 bins of 9°, covering 0° -180°, and mirrored to 180° -360°. To create a continuous polar function representing the μFOD, the mid-points of the bins were fitted by a spline, which was then rendered as a polar plot (Fig. 1H, Fig. 3D, G, X, Fig. 4C, D, Fig. 5C, G, L, O).

Diffusion MR-based orientation distribution functions (ODFs) and tractography

To generate ODFs and enable tractography, we opted for using existing tools developed in MR tractography, given the advanced and widely tested algorithms used in the field. To achieve that, an artificial diffusion MRI dataset was created based on the ComSLI μFODs described above. The dataset included 3 b=0ms/μm² and 60 b=1ms/μm² volumes. The 3 b=0ms/μm² volumes had a value of 1 at all voxels. The 60 b=1ms/μm² volumes consisted of 3 sets: i) The first 20 were derived for the 20 in-plane orientations of the μFOD angles (20 angles, 9° apart, covering 180°), see above. There, the signal for each angle was calculated for every pixel as , where n is the frequency of fiber orientations for that angle in that pixel, based on the μFOD polar histogram.  ii) The second 20 orientations were at the plane perpendicular to the plane of the section. For these, the signal was set to 1 for all pixels, indicating no signal loss and hence no axons along that plane, thus enforcing the orientations to be within the section plane. iii) The third set of 20 orientations was the initial set of 20 polar histogram orientations but tilted 20° off the section plane towards the perpendicular plane. There, each pixel’s signal was , where Signal₀ is the signal of the first set of angles. This aimed to approximate a diffusion MRI signal and fiber distribution off-plane, where the signal loss at 20° off the detected in-plane axon orientation is 20% of the signal loss along the axons. 

To compute the ODFs and tractograms, MRtrix3³⁵ functions were used with the generated artificial MRI signal along with the corresponding bval and bvec files as input: dwi2response with the fa algorithm and l_max=6 to calculate the fiber response and dwi2fod with the csd algorithm and l_max=6 to calculate the ODFs. Finally, the tckgen function was used to generate tractograms, with a minimum tract length of 2mm, which was found to be reasonable to compute given the micron-scale pixel sizes.

Nissl-ST

The fiber orientation maps were computed in Matlab following the procedure described and code shared by Schurr & Mezer²⁵, using default settings and 100μm as a kernel to compute the structure tensor (effective resolution).

ComSLI – Histology registration

To enable quantitative, pixel-wise comparison between ComSLI and Nissl-ST outputs (Fig. 4 and Supplementary Figs. 10-11) as well as quantification of the ComSLI and histology stain intensities (Fig. 3 and Supplementary Fig. 6), the histology images were linearly 2D-registered to the corresponding ComSLI average scattering images of the same section using the antsRegistration function in ANTs⁶⁵ (options: --transform Similarity[0.1], --convergence [100 x 70 x 50 x 0,1e-6,10] --smoothing-sigmas 3x2x1x0vox --shrink-factors 8x4x2x1).

To register consecutive histology sections (Figs. 2, 3, and Supplementary Fig. 6) the SyN transform of the same ANTs command was used (options: --transform SyN[0.1,3,0] --convergence [800 x 400 x 200 x 100 x 70 x 50 x 0,1e-6,10] --smoothing-sigmas 15x9x5x3x2x1x0vox --shrink-factors 64x32x16x8x4x2x1).

3D-PLI

The 3D-PLI measurements were performed using the LMP3D microscope (Taorad GmbH, Germany), containing an evo4070MFLGEC (2048x2048) camera and a Nikon 4x (NA 0.2) lens, which achieves a pixel size of 1.85μm and an in-plane optical resolution of 2.2μm (determined by US-Airforce target). The sample was illuminated by linearly polarized light at 20° rotation angles and analyzed by a circular analyzer as described by Axer et al.¹⁹. 3D-PLI FOM was computed on the supercomputer JURECA at Forschungszentrum Jülich (grant no. 28954).

Diffusion MRI

The diffusion MRI dataset³⁰ is from a 30-year-old male who underwent 18 hours of diffusion MRI scanning in the MGH-USC 3T Connectom scanner using gSlider-SMS⁶⁶. After manually identifying the MR plane that most closely matched the BigBrain histology sections, the entire dataset was rotated using FreeSurfer’s freeview and the b-vectors were rotated at the same angles (rotation angles for the Silver-stained section were -34° sagittal and 1.5° axial, for the Cresyl-violet -30.4° sagittal). Fiber responses and orientation distributions were computed using the dwi2response and dwi2fod functions in MRtrix3³⁵, using the multi-tissue, multi-shell algorithm³⁸, and visualized in mrview. To generate whole-brain colormaps in Fig. 4 and Supplementary Fig. 10, MRtrix3’s sh2amp function was used to probe fiber orientations at the coronal plane at 5° intervals, and colormaps were generated using SLIX³⁴.

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