Accuracy of image registration between MRI and light microscopy in the ex vivo brain

Molecular signatures of cortical expansion in the human fetal brain

The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas.1,2 Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, μBrain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, μBrain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation3 with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones.4 Using μBrain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types5 and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The μBrain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.

Wide field block face imaging using deep ultraviolet induced autofluorescence of the human brain

BACKGROUND

Imaging large volume human brains at cellular resolution involve histological methods that cause structural changes. A reference point prior to sectioning is needed to quantify these changes and is achieved by serial block face imaging (BFI) methods that have been applied to small volume tissue (∼1 cm3).

NEW METHOD

We have developed a BFI uniquely designed for large volume tissues (∼1300 cm3) with a very large field of view (20 × 20 cm) at a resolution of 70 µm/pixel under deep ultraviolet (UV-C) illumination which highlights key features.

RESULTS

The UV-C imaging ensures high contrast imaging of the brain tissue and highlights salient features of the brain. The system is designed to provide uniform and stable illumination across the entire surface area of the tissue and to work at low temperatures, which are required during cryosectioning. Most importantly, it has been designed to maintain its optical focus over the large depth of tissue and over long periods of time, without readjustments. The BFI was installed within a cryomacrotome, and was used to image a large cryoblock of an adult human cerebellum and brainstem (∼6 cm depth resulting in 2995 serial images) with precise optical focus and no loss during continuous serial acquisition.

COMPARISON WITH EXISTING METHOD(S)

The deep UV-C induced BFI highlights several large fibre tracts within the brain including the cerebellar peduncles, and the corticospinal tract providing important advantage over white light BFI.

CONCLUSIONS

The 3D reconstructed serial BFI images can assist in the registration and alignment of the microscopic high-resolution histological tissue sections.

Tensor image registration library: Deformable registration of stand-alone histology images to whole-brain post-mortem MRI data

BACKGROUND

Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data.

METHODS

Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline.

RESULTS

All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2-0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks.

CONCLUSIONS

Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies.

Ex Vivo Study Using Diffusion Tensor Imaging to Identify Biomarkers of Atherosclerotic Disease in Human Cadaveric Carotid Arteries

BACKGROUND

This study aims to address the potential of ex vivo diffusion tensor imaging to provide insight into the microstructural composition and morphological arrangement of aged human atherosclerotic carotid arteries.

METHODS

In this study, whole human carotid arteries were investigated both anatomically and by comparing healthy and diseased regions. Nonrigid image registration was used with unsupervised segmentation to investigate the influence of elastin, collagen, cell density, glycosaminoglycans, and calcium on diffusion tensor imaging derived metrics (fractional anisotropy and mean diffusivity). Early stage atherosclerotic features were also investigated in terms of microstructural components and diffusion tensor imaging metrics.

RESULTS

All vessels displayed a dramatic decrease in fractional anisotropy compared with healthy animal arterial tissue, while the mean diffusivity was sensitive to regions of advanced disease. Elastin content strongly correlated with both fractional anisotropy (r>0.7, P<0.001) and mean diffusivity (r>-0.79, P<0.0002), and the thickened intima was also distinguishable from arterial media by these metrics.

CONCLUSIONS

These different investigations point to the potential of diffusion tensor imaging to identify characteristics of arterial disease progression, at early and late-stage lesion development.

microRNA Biology on Brain Development and Neuroimaging Approach

Proper brain development requires the precise coordination and orchestration of various molecular and cellular processes and dysregulation of these processes can lead to neurological diseases. In the past decades, post-transcriptional regulation of gene expression has been shown to contribute to various aspects of brain development and function in the central nervous system. MicroRNAs (miRNAs), short non-coding RNAs, are emerging as crucial players in post-transcriptional gene regulation in a variety of tissues, such as the nervous system. In recent years, miRNAs have been implicated in multiple aspects of brain development, including neurogenesis, migration, axon and dendrite formation, and synaptogenesis. Moreover, altered expression and dysregulation of miRNAs have been linked to neurodevelopmental and psychiatric disorders. Magnetic resonance imaging (MRI) is a powerful imaging technology to obtain high-quality, detailed structural and functional information from the brains of human and animal models in a non-invasive manner. Because the spatial expression patterns of miRNAs in the brain, unlike those of DNA and RNA, remain largely unknown, a whole-brain imaging approach using MRI may be useful in revealing biological and pathological information about the brain affected by miRNAs. In this review, we highlight recent advancements in the research of miRNA-mediated modulation of neuronal processes that are important for brain development and their involvement in disease pathogenesis. Also, we overview each MRI technique, and its technological considerations, and discuss the applications of MRI techniques in miRNA research. This review aims to link miRNA biological study with MRI analytical technology and deepen our understanding of how miRNAs impact brain development and pathology of neurological diseases.

Expected affine: A registration method for damaged section in serial sections electron microscopy

Registration is essential for the volume reconstruction of biological tissues using serial section electron microscope (ssEM) images. However, due to environmental disturbance in section preparation, damage in long serial sections is inevitable. It is difficult to register the damaged sections with the common serial section registration method, creating significant challenges in subsequent neuron tracking and reconstruction. This paper proposes a general registration method that can be used to register damaged sections. This method first extracts the key points and descriptors of the sections to be registered and matches them via a mutual nearest neighbor matcher. K-means and Random Sample Consensus (RANSAC) are used to cluster the key points and approximate the local affine matrices of those clusters. Then, K-nearest neighbor (KNN) is used to estimate the probability density of each cluster and calculate the expected affine matrix for each coordinate point. In clustering and probability density calculations, instead of the Euclidean distance, the path distance is used to measure the correlation between sampling points. The experimental results on real test images show that this method solves the problem of registering damaged sections and contributes to the 3D reconstruction of electronic microscopic images of biological tissues. The code of this paper is available at https://github.com/TongXin-CASIA/Excepted_Affine.

Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.

A semi-automatic registration protocol to match ex-vivo high-field 7T MR images and histological slices in surgical samples from patients with drug-resistant epilepsy

BACKGROUND

MRI is a fundamental tool to detect brain structural anomalies and improvement in this technique has the potential to visualize subtle abnormalities currently undetected. Correlation between pre-operative MRI and histopathology is required to validate the neurobiological basis of MRI abnormalities. However, precise MRI-histology matching is very challenging with the surgical samples. We previously developed a coregistration protocol to match the in-vivo MRI with ex-vivo MRI obtained from surgical specimens. Now, we complete the process to successfully align ex-vivo MRI data with the proper digitalized histological sections in an automatic way.

NEW METHOD

The implemented pipeline is composed by the following steps: a) image pre-processing made of MRI and histology volumes conversion and masking; b) gross rigid body alignment between MRI volume and histology virtual slides; c) rigid alignment between each MRI section and histology slice and estimate of the correlation coefficient for each step to select the MRI slice that best matches histology; d) final linear registration of the selected slices.

RESULTS

This method is fully automatic, except for the first masking step, fast and reliable in comparison to the manual one, as assessed using a Bland-Altman plot.

COMPARISON WITH EXISTING METHODS

The visual assessment usually employed for choosing the best fitting ex-vivo MRI slice for each stained section takes hours and requires practice. Goubran et al. (2015) proposed an iterative registration protocol but its aim and methods were different from ours. No others similar methods are reported in the literature.

CONCLUSIONS

This protocol completes our previous pipeline. The ultimate goal will be to apply the entire process to finely investigate the relationship between clinical MRI data and histopathological features in patients with drug-resistant epilepsy.

Histological Validation of MRI: A Review of Challenges in Registration of Imaging and Whole-Mount Histopathology

Rigorous validation with ground truth information such as histology is needed to reliably assess the current and potential value of MRI techniques to characterize tissue and identify disease-related tissue alterations. Commonly used methods that aim to directly correlate histology and MRI data generally fall short of this goal due to spatial errors that preclude direct matching. Errors result from tissue deformation, differences in spatial resolution and slice thickness, non-coplanar and/or nonintersecting plane orientations, and different image contrast mechanisms. Some of these problems arise from limitations in standard protocols for clinical tissue processing and histology-based pathology reporting, and to some extent can be addressed by modifications to standard protocols without compromising the clinical process. Typical modifications include ex vivo specimen MRI, block-face photography, addition of fiducial markers, and 3D printed molds to constrain tissue deformation and guide sectioning. This review summarizes the advantages and limitations of MRI validation techniques based on coregistration of MRI with whole-mount histology of tissue specimens. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

Challenges for biophysical modeling of microstructure

The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.

A multimodal computational pipeline for 3D histology of the human brain

Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic level, using a wide array of different stains that highlight different microanatomical features. Complementing MRI with histology has important applications in ex vivo atlas building and in modeling the link between microstructure and macroscopic MR signal. However, histology requires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples. Here, we present an open-source computational pipeline to produce 3D consistent histology reconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves as undistorted reference, and on an intermediate imaging modality (blockface photography) that bridges the gap between MRI and histology. We present results on 3D histology reconstruction of whole human hemispheres from two donors.

A novel ex vivo, in situ method to study the human brain through MRI and histology

BACKGROUND

MRI-histology correlation studies of the ex vivo brain mostly employ fresh, extracted (ex situ) specimens, aldehyde fixed by immersion, which has several disadvantages for MRI scanning (e.g. deformation of the organ). A minority of studies are done ex vivo-in situ (unfixed brain), requiring an MRI scanner readily available within a few hours of the time of death.

NEW METHOD

We propose a new technique, exploited by anatomists, for scanning the ex vivo brain: fixation by whole body perfusion, which implies fixation of the brain in situ. This allows scanning the brain surrounded by fluids, meninges, and skull, preserving the structural relationships of the brain in vivo. To evaluate the proposed method, five heads perfused-fixed with a saturated sodium chloride solution were employed. Three sequences were acquired on a 1.5 T MRI scanner: T1weighted, T2weighted-FLAIR, and Gradient-echo. Histology analysis included immunofluorescence for myelin basic protein and neuronal nuclei.

RESULTS

All MRIs were successfully processed through a validated pipeline used with in vivo MRIs. All cases exhibited positive antigenicity for myelin and neuronal nuclei.

COMPARISON WITH EXISTING METHODS

All scans registered to a standard neuroanatomical template in pseudo-Talairach space more accurately than an ex vivo-ex situ scan. The time interval to scan the ex vivo brain in situ was increased to at least 10 months.

CONCLUSIONS

MRI and histology study of the ex vivo-in situ brain fixed by perfusion is an alternative approach that has important procedural and practical advantages over the two standard methods to study the ex vivo brain.

Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE

Intra-voxel models of the diffusion signal are essential for interpreting organization of the tissue environment at micrometer level with data at millimeter resolution. Recent advances in data driven methods have enabled direct comparison and optimization of methods for in-vivo data with externally validated histological sections with both 2-D and 3-D histology. Yet, all existing methods make limiting assumptions of either (1) model-based linkages between b-values or (2) limited associations with single shell data. We generalize prior deep learning models that used single shell spherical harmonic transforms to integrate the recently developed simple harmonic oscillator reconstruction (SHORE) basis. To enable learning on the SHORE manifold, we present an alternative formulation of the fiber orientation distribution (FOD) object using the SHORE basis while representing the observed diffusion weighted data in the SHORE basis. To ensure consistency of hyper-parameter optimization for SHORE, we present our Deep SHORE approach to learn on a data-optimized manifold. Deep SHORE is evaluated with eight-fold cross-validation of a preclinical MRI-histology data with four b-values. Generalizability of in-vivo human data is evaluated on two separate 3T MRI scanners. Specificity in terms of angular correlation (ACC) with the preclinical data improved on single shell: 0.78 relative to 0.73 and 0.73, multi-shell: 0.80 relative to 0.74 (p < 0.001). In the in-vivo human data, Deep SHORE was more consistent across scanners with 0.63 relative to other multi-shell methods 0.39, 0.52 and 0.57 in terms of ACC. In conclusion, Deep SHORE is a promising method to enable data driven learning with DW-MRI under conditions with varying b-values, number of diffusion shells, and gradient directions per shell.

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI

PURPOSE

Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens through which DW-MRI data are interpreted. Numerous modern approaches offer opportunities to assess more complex intra-voxel structures. Nevertheless, there remains a substantial gap between intra-voxel estimated structures and ground truth captured by 3-D histology.

METHODS

Herein, we propose a novel data-driven approach to model the non-linear mapping between observed DW-MRI signals and ground truth structures using a sequential deep neural network regression using residual block deep neural network (ResDNN). Training was performed on two 3-D histology datasets of squirrel monkey brains and validated on a third. A second validation was performed using scan-rescan datasets of 12 subjects from Human Connectome Project. The ResDNN was compared with multiple micro-structure reconstruction methods and super resolved-constrained spherical deconvolution (sCSD) in particular as baseline for both the validations.

RESULTS

Angular correlation coefficient (ACC) is a correlation/similarity measure and can be interpreted as accuracy when compared with a ground truth. The median ACC of ResDNN is 0.82 and median ACC's of different variants of CSD are 0.75, 0.77, 0.79. The mean, median and std. of ResDNN & sCSD ACC across 12 subjects from HCP are 0.74, 0.88, 0.31 and 0.61, 0.71, 0.31 respectively.

CONCLUSION

This work highlights the ability of deep learning to capture linkages between ex-vivo ground truth data with feasible MRI sequences. The data-driven approach is applicable to human in-vivo data and results in intriguingly high reproducibility of orientation structure.

A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain

The squirrel monkey (Saimiri sciureus) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI and histological atlases serve as valuable resources for anatomical, physiological, and functional studies of the brain; however, no digital MRI/histology atlas is currently available for the squirrel monkey. This paper describes the construction of a web-based multi-modal atlas of the squirrel monkey brain. The MRI-derived information includes anatomical MRI contrast (i.e., T2-weighted and proton-density-weighted) and diffusion MRI metrics (i.e., fractional anisotropy and mean diffusivity) from data acquired both in vivo and ex vivo on a 9.4 Tesla scanner. The histological images include Nissl and myelin stains, co-registered to the corresponding MRI, allowing identification of cyto- and myelo-architecture. In addition, a bidirectional neuronal tracer, biotinylated dextran amine (BDA) was injected into the primary motor cortex, enabling highly specific identification of regions connected to the injection location. The atlas integrates the results of common image analysis methods including diffusion tensor imaging glyphs, labels of 57 white-matter tracts identified using DTI-tractography, and 18 cortical regions of interest identified from Nissl-revealed cyto-architecture. All data are presented in a common space, and all image types are accessible through a web-based atlas viewer, which allows visualization and interaction of user-selectable contrasts and varying resolutions. By providing an easy to use reference system of anatomical information, our web-accessible multi-contrast atlas forms a rich and convenient resource for comparisons of brain findings across subjects or modalities. The atlas is called the Combined Histology-MRI Integrated Atlas of the Squirrel Monkey (CHIASM). All images are accessible through our web-based viewer ( https://chiasm.vuse.vanderbilt.edu /), and data are available for download at ( https://www.nitrc.org/projects/smatlas/ ).

Matching ex vivo MRI With Iron Histology: Pearls and Pitfalls

Iron levels in the brain can be estimated using newly developed specific magnetic resonance imaging (MRI) sequences. This technique has several applications, especially in neurodegenerative disorders like Alzheimer's disease or Parkinson's disease. Coupling ex vivo MRI with histology allows neuroscientists to better understand what they see in the images. Iron is one of the most extensively studied elements, both by MRI and using histological or physical techniques. Researchers were initially only able to make visual comparisons between MRI images and different types of iron staining, but the emergence of specific MRI sequences like R2^* or quantitative susceptibility mapping meant that quantification became possible, requiring correlations with physical techniques. Today, with advances in MRI and image post-processing, it is possible to look for MRI/histology correlations by matching the two sorts of images. For the result to be acceptable, the choice of methodology is crucial, as there are hidden pitfalls every step of the way. In order to review the advantages and limitations of ex vivo MRI correlation with iron-based histology, we reviewed all the relevant articles dealing with the topic in humans. We provide separate assessments of qualitative and quantitative studies, and after summarizing the significant results, we emphasize all the pitfalls that may be encountered.

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers-An ex vivo validation study in the squirrel monkey brain

Understanding the relationship between the diffusion-weighted MRI signal and the arrangement of white matter fibers is fundamental for accurate voxel-wise reconstruction of the fiber orientation distribution (FOD) and subsequent fiber tractography. Spherical deconvolution reconstruction techniques model the diffusion signal as the convolution of the FOD with a response function that represents the signal profile of a single fiber orientation. Thus, given the signal and a fiber response function, the FOD can be estimated in every imaging voxel by deconvolution. However, the selection of the appropriate response function remains relatively under-studied, and requires further validation. In this work, using 3D histologically defined FODs and the corresponding diffusion signal from three ex vivo squirrel monkey brains, we derive the ground truth response functions. We find that the histologically derived response functions differ from those conventionally used. Next, we find that response functions statistically vary across brain regions, which suggests that the practice of using the same kernel throughout the brain is not optimal. We show that different kernels lead to different FOD reconstructions, which in turn can lead to different tractography results depending on algorithmic parameters, with large variations in the accuracy of resulting reconstructions. Together, these results suggest there is room for improvement in estimating and understanding the relationship between the diffusion signal and the underlying FOD.

LEARNING 3D WHITE MATTER MICROSTRUCTURE FROM 2D HISTOLOGY

Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due to increased field-of-view, higher in-plane resolutions, ease of acquisition, decreased costs, and a large number of available contrasts compared to 3-dimensional (3D) analysis. Because of this, it would be of great interest to be able to learn the 3D tissue microstructure from 2D histology. In this study, we use diffusion MRI (dMRI) of a squirrel monkey brain and corresponding myelin stained sections in combination with a convolution neural network to learn the relationship between the 3D diffusion estimated axonal fiber orientation distributions and the 2D myelin stain. We find that we are able to estimate the 3D fiber distribution with moderate to high angular agreement with the ground truth (median angular correlation coefficients of 0.48 across the unseen slices). This network could be used to validate dMRI neuronal structural measurements in 3D, even if only 2D histology is available for validation. Generalization is possible to transfer this network to human stained sections to infer the 3D fiber distribution at resolutions currently unachievable with dMRI, which would allow diffusion fiber tractography at unprecedented resolutions. We envision the use of similar networks to learn other 3D microstructural measures from an array of potential common 2D histology contrasts.

Limits to anatomical accuracy of diffusion tractography using modern approaches

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

Anatomical accuracy of standard-practice tractography algorithms in the motor system - A histological validation in the squirrel monkey brain

For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.

Rigid and non-rigid registration of polarized light imaging data for 3D reconstruction of the temporal lobe of the human brain at micrometer resolution

To understand the spatial organization as well as long- and short-range connections of the human brain at microscopic resolution, 3D reconstruction of histological sections is important. We approach this challenge by reconstructing series of unstained histological sections of multi-scale (1.3μm and 64μm) and multi-modal 3D polarized light imaging (3D-PLI) data. Since spatial coherence is lost during the sectioning procedure, image registration is the major step in 3D reconstruction. We propose a non-rigid registration method which comprises of a novel multi-modal similarity metric and an improved regularization scheme to cope with deformations inevitably introduced during the sectioning procedure, as well as a rigid registration approach using a robust similarity metric for improved initial alignment. We also introduce a multi-scale feature-based localization and registration approach for mapping of 1.3μm sections to 64μm sections and a scale-adaptive method that can handle challenging sections with large semi-global deformations due to tissue splits. We have applied our registration method to 126 consecutive sections of the temporal lobe of the human brain with 64μm and 1.3μm resolution. Each step of the registration method was quantitatively evaluated using 10 different sections and manually determined ground truth, and a quantitative comparison with previous methods was performed. Visual assessment of the reconstructed volumes and comparison with reference volumes confirmed the high quality of the registration result.

The VALiDATe29 MRI Based Multi-Channel Atlas of the Squirrel Monkey Brain

We describe the development of the first digital atlas of the normal squirrel monkey brain and present the resulting product, VALiDATe29. The VALiDATe29 atlas is based on multiple types of magnetic resonance imaging (MRI) contrast acquired on 29 squirrel monkeys, and is created using unbiased, nonlinear registration techniques, resulting in a population-averaged stereotaxic coordinate system. The atlas consists of multiple anatomical templates (proton density, T1, and T2* weighted), diffusion MRI templates (fractional anisotropy and mean diffusivity), and ex vivo templates (fractional anisotropy and a structural MRI). In addition, the templates are combined with histologically defined cortical labels, and diffusion tractography defined white matter labels. The combination of intensity templates and image segmentations make this atlas suitable for the fundamental atlas applications of spatial normalization and label propagation. Together, this atlas facilitates 3D anatomical localization and region of interest delineation, and enables comparisons of experimental data across different subjects or across different experimental conditions. This article describes the atlas creation and its contents, and demonstrates the use of the VALiDATe29 atlas in typical applications. The atlas is freely available to the scientific community.

Three-dimensional Cardiomyocytes Structure Revealed By Diffusion Tensor Imaging and Its Validation Using a Tissue-Clearing Technique

We characterized the microstructural response of the myocardium to cardiovascular disease using diffusion tensor imaging (DTI) and performed histological validation by intact, un-sectioned, three-dimensional (3D) histology using a tissue-clearing technique. The approach was validated in normal (n = 7) and ischemic (n = 8) heart failure model mice. Whole heart fiber tracking using DTI in fixed ex-vivo mouse hearts was performed, and the hearts were processed with the tissue-clearing technique. Cardiomyocytes orientation was quantified on both DTI and 3D histology. Helix angle (HA) and global HA transmurality (HAT) were calculated, and the DTI findings were confirmed with 3D histology. Global HAT was significantly reduced in the ischemic group (DTI: 0.79 ± 0.13°/% transmural depth [TD] and 3D histology: 0.84 ± 0.26°/%TD) compared with controls (DTI: 1.31 ± 0.20°/%TD and 3D histology: 1.36 ± 0.27°/%TD, all p < 0.001). On direct comparison of DTI with 3D histology for the quantitative assessment of cardiomyocytes orientation, significant correlations were observed in both per-sample (R2 = 0.803) and per-segment analyses (R2 = 0.872). We demonstrated the capability and accuracy of DTI for mapping cardiomyocytes orientation by comparison with the intact 3D histology acquired by tissue-clearing technique. DTI is a promising tool for the noninvasive characterization of cardiomyocytes architecture.

Tests of cortical parcellation based on white matter connectivity using diffusion tensor imaging

The cerebral cortex is conventionally divided into a number of domains based on cytoarchitectural features. Diffusion tensor imaging (DTI) enables noninvasive parcellation of the cortex based on white matter connectivity patterns. However, the correspondence between DTI-connectivity-based and cytoarchitectural parcellation has not been systematically established. In this study, we compared histological parcellation of New World monkey neocortex to DTI- connectivity-based classification and clustering in the same brains. First, we used supervised classification to parcellate parieto-frontal cortex based on DTI tractograms and the cytoarchitectural prior (obtained using Nissl staining). We performed both within and across sample classification, showing reasonable classification performance in both conditions. Second, we used unsupervised clustering to parcellate the cortex and compared the clusters to the cytoarchitectonic standard. We then explored the similarities and differences with several post-hoc analyses, highlighting underlying principles that drive the DTI-connectivity-based parcellation. The differences in parcellation between DTI-connectivity and Nissl histology probably represent both DTI's bias toward easily-tracked bundles and true differences between cytoarchitectural and connectivity defined domains. DTI tractograms appear to cluster more according to functional networks, rather than mapping directly onto cytoarchitectonic domains. Our results show that caution should be used when DTI-tractography classification, based on data from another brain, is used as a surrogate for cytoarchitectural parcellation.

Tests of clustering thalamic nuclei based on various dMRI models in the squirrel monkey brain

Background

Clustering thalamic nuclei is important for both research and clinical purposes. For example, ventral intermediate nuclei in thalami serve as targets in both deep brain stimulation neurosurgery and radiosurgery for treating patients suffering from movement disorders (e.g., Parkinson's disease and essential tremor). Diffusion magnetic resonance imaging (dMRI) is able to reflect tissue microstructure in the central nervous system via fitting different models, such as, the diffusion tensor (DT), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI), diffusion kurtosis imaging (DKI) and the spherical mean technique (SMT).

Purpose

To test which of the above-mentioned dMRI models is better for thalamic parcellation, we proposed a framework of k-means clustering, implemented it on each model, and evaluated the agreement with histology.

Method

An ex vivo monkey brain was scanned in a 9.4T MRI scanner at 0.3mm resolution with b values of 3000, 6000, 9000 and 12000 s/mm². K-means clustering on each thalamus was implemented using maps of dMRI models fitted to the same data. Meanwhile, histological nuclei were identified by AChE and Nissl stains of the same brain. Overall agreement rate and agreement rate for each nucleus were calculated between clustering and histology. Sixteen thalamic nuclei on each hemisphere were included.

Results

Clustering with the DKI model has slightly higher overall agreement rate but clustering with other dMRI models result in higher agreement rate in some nuclei.

Conclusion

dMRl models should be carefully selected to better parcellate the thalamus, depending on the specific purpose of the parcellation.

Confirmation of a gyral bias in diffusion MRI fiber tractography

Diffusion MRI fiber tractography has been increasingly used to map the structural connectivity of the human brain. However, this technique is not without limitations; for example, there is a growing concern over anatomically correlated bias in tractography findings. In this study, we demonstrate that there is a bias for fiber tracking algorithms to terminate preferentially on gyral crowns, rather than the banks of sulci. We investigate this issue by comparing diffusion MRI (dMRI) tractography with equivalent measures made on myelin-stained histological sections. We begin by investigating the orientation and trajectories of axons near the white matter/gray matter boundary, and the density of axons entering the cortex at different locations along gyral blades. These results are compared with dMRI orientations and tract densities at the same locations, where we find a significant gyral bias in many gyral blades across the brain. This effect is shown for a range of tracking algorithms, both deterministic and probabilistic, and multiple diffusion models, including the diffusion tensor and a high angular resolution diffusion imaging technique. Additionally, the gyral bias occurs for a range of diffusion weightings, and even for very high-resolution datasets. The bias could significantly affect connectivity results using the current generation of tracking algorithms.

Histological validation of diffusion MRI fiber orientation distributions and dispersion

Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ∼10° for the primary fiber direction and ∼20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy.

Towards a comprehensive atlas of cortical connections in a primate brain: Mapping tracer injection studies of the common marmoset into a reference digital template

The marmoset is an emerging animal model for large‐scale attempts to understand primate brain connectivity, but achieving this aim requires the development and validation of procedures for normalization and integration of results from many neuroanatomical experiments. Here we describe a computational pipeline for coregistration of retrograde tracing data on connections of cortical areas into a 3D marmoset brain template, generated from Nissl‐stained sections. The procedure results in a series of spatial transformations that are applied to the coordinates of labeled neurons in the different cases, bringing them into common stereotaxic space. We applied this procedure to 17 injections, placed in the frontal lobe of nine marmosets as part of earlier studies. Visualizations of cortical patterns of connections revealed by these injections are supplied as Supplementary Materials. Comparison between the results of the automated and human‐based processing of these cases reveals that the centers of injection sites can be reconstructed, on average, to within 0.6 mm of coordinates estimated by an experienced neuroanatomist. Moreover, cell counts obtained in different areas by the automated approach are highly correlated (r = 0.83) with those obtained by an expert, who examined in detail histological sections for each individual. The present procedure enables comparison and visualization of large datasets, which in turn opens the way for integration and analysis of results from many animals. Its versatility, including applicability to archival materials, may reduce the number of additional experiments required to produce the first detailed cortical connectome of a primate brain. J. Comp. Neurol. 524:2161–2181, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

The possibility to perform an early and repeatable assessment of imaging performance is fundamental in the design and development process of new ultrasound (US) probes. Particularly, a more realistic analysis with application-specific imaging targets can be extremely valuable to assess the expected performance of US probes in their potential clinical field of application. The experimental protocol presented in this work was purposely designed to provide an application-specific assessment procedure for newly-developed US probe prototypes based on Capacitive Micromachined Ultrasonic Transducer (CMUT) technology in relation to brain imaging. The protocol combines the use of a bovine brain fixed in formalin as the imaging target, which ensures both realism and repeatability of the described procedures, and of neuronavigation techniques borrowed from neurosurgery. The US probe is in fact connected to a motion tracking system which acquires position data and enables the superposition of US images to reference Magnetic Resonance (MR) images of the brain. This provides a means for human experts to perform a visual qualitative assessment of the US probe imaging performance and to compare acquisitions made with different probes. Moreover, the protocol relies on the use of a complete and open research and development system for US image acquisition, i.e. the Ultrasound Advanced Open Platform (ULA-OP) scanner. The manuscript describes in detail the instruments and procedures involved in the protocol, in particular for the calibration, image acquisition and registration of US and MR images. The obtained results prove the effectiveness of the overall protocol presented, which is entirely open (within the limits of the instrumentation involved), repeatable, and covers the entire set of acquisition and processing activities for US images.

Quantitative validation of a nonlinear histology-MRI coregistration method using generalized Q-sampling imaging in complex human cortical white matter

Advanced diffusion MRI methods have recently been proposed for detection of pathologies such as traumatic axonal injury and chronic traumatic encephalopathy which commonly affect complex cortical brain regions. However, radiological-pathological correlations in human brain tissue that detail the relationship between the multi-component diffusion signal and underlying pathology are lacking. We present a nonlinear voxel based two dimensional coregistration method that is useful for matching diffusion signals to quantitative metrics of high resolution histological images. When validated in ex vivo human cortical tissue at a 250×250×500 μm spatial resolution, the method proved robust in correlations between generalized q-sampling imaging and histologically based white matter fiber orientations, with r=0.94 for the primary fiber direction and r=0.88 for secondary fiber direction in each voxel. Importantly, however, the correlation was substantially worse with reduced spatial resolution or with fiber orientations derived using a diffusion tensor model. Furthermore, we have detailed a quantitative histological metric of white matter fiber integrity termed power coherence capable of distinguishing architecturally complex but intact white matter from disrupted white matter regions. These methods may allow for more sensitive and specific radiological-pathological correlations of neurodegenerative diseases affecting complex gray and white matter.

Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology.

Reproducibility and variation of diffusion measures in the squirrel monkey brain, in vivo and ex vivo

PURPOSE

Animal models are needed to better understand the relationship between diffusion MRI (dMRI) and the underlying tissue microstructure. One promising model for validation studies is the common squirrel monkey, Saimiri sciureus. This study aims to determine (1) the reproducibility of in vivo diffusion measures both within and between subjects; (2) the agreement between in vivo and ex vivo data acquired from the same specimen and (3) normal diffusion values and their variation across brain regions.

METHODS

Data were acquired from three healthy squirrel monkeys, each imaged twice in vivo and once ex vivo. Reproducibility of fractional anisotropy (FA), mean diffusivity (MD), and principal eigenvector (PEV) was assessed, and normal values were determined both in vivo and ex vivo.

RESULTS

The calculated coefficients of variation (CVs) for both intra-subject and inter-subject MD were below 10% (low variability) while FA had a wider range of CVs, 2-14% intra-subject (moderate variability), and 3-31% inter-subject (high variability). MD in ex vivo tissue was lower than in vivo (30%-50% decrease), while FA values increased in all regions (30-39% increase). The mode of angular differences between in vivo and ex vivo PEVs was 12 degrees.

CONCLUSION

This study characterizes the diffusion properties of the squirrel monkey brain and serves as the groundwork for using the squirrel monkey, both in vivo and ex vivo, as a model for diffusion MRI studies.

Deformable image registration between pathological images and MR image via an optical macro image

Computed tomography (CT) and magnetic resonance (MR) imaging have been widely used for visualizing the inside of the human body. However, in many cases, pathological diagnosis is conducted through a biopsy or resection of an organ to evaluate the condition of tissues as definitive diagnosis. To provide more advanced information onto CT or MR image, it is necessary to reveal the relationship between tissue information and image signals. We propose a registration scheme for a set of PT images of divided specimens and a 3D-MR image by reference to an optical macro image (OM image) captured by an optical camera. We conducted a fundamental study using a resected human brain after the death of a brain cancer patient. We constructed two kinds of registration processes using the OM image as the base for both registrations to make conversion parameters between the PT and MR images. The aligned PT images had shapes similar to the OM image. On the other hand, the extracted cross-sectional MR image was similar to the OM image. From these resultant conversion parameters, the corresponding region on the PT image could be searched and displayed when an arbitrary pixel on the MR image was selected. The relationship between the PT and MR images of the whole brain can be analyzed using the proposed method. We confirmed that same regions between the PT and MR images could be searched and displayed using resultant information obtained by the proposed method. In terms of the accuracy of proposed method, the TREs were 0.56±0.39mm and 0.87±0.42mm. We can analyze the relationship between tissue information and MR signals using the proposed method.

Possum-A Framework for Three-Dimensional Reconstruction of Brain Images from Serial Sections

Techniques based on imaging serial sections of brain tissue provide insight into brain structure and function. However, to compare or combine them with results from three dimensional imaging methods, reconstruction into a volumetric form is required. Currently, there are no tools for performing such a task in a streamlined way. Here we propose the Possum volumetric reconstruction framework which provides a selection of 2D to 3D image reconstruction routines allowing one to build workflows tailored to one's specific requirements. The main components include routines for reconstruction with or without using external reference and solutions for typical issues encountered during the reconstruction process, such as propagation of the registration errors due to distorted sections. We validate the implementation using synthetic datasets and actual experimental imaging data derived from publicly available resources. We also evaluate efficiency of a subset of the algorithms implemented. The Possum framework is distributed under MIT license and it provides researchers with a possibility of building reconstruction workflows from existing components, without the need for low-level implementation. As a consequence, it also facilitates sharing and data exchange between researchers and laboratories.

Cancer stem cells are underestimated by standard experimental methods in clear cell renal cell carcinoma

Rare cancer stem cells (CSC) are proposed to be responsible for tumour propagation and re-initiation and are functionally defined by identifying tumour-initiating cells (TICs) using the xenotransplantation limiting dilution assay (LDA). While TICs in clear cell renal cell carcinoma (ccRCC) appeared rare in NOD/SCID/IL2Rγ(-/-) (NSG) mice, xenografts formed more efficiently from small tumour fragments, indicating the LDA underestimated ccRCC TIC frequency. Mechanistic interrogation of the LDA identified multiple steps that influence ccRCC TIC quantitation. For example, tissue disaggregation destroys most ccRCC cells, common assays significantly overestimate tumour cell viability, and microenvironmental supplementation with human extracellular factors or pharmacological inhibition of anoikis increase clonogenicity and tumourigenicity of ccRCC cell lines and primary tumour cells. Identification of these previously uncharacterized concerns that cumulatively lead to substantial underestimation of TICs in ccRCC provides a framework for development of more accurate TIC assays in the future, both for this disease and for other cancers.

A 3D high resolution ex vivo white matter atlas of the common squirrel monkey (Saimiri sciureus) based on diffusion tensor imaging

Modern magnetic resonance imaging (MRI) brain atlases are high quality 3-D volumes with specific structures labeled in the volume. Atlases are essential in providing a common space for interpretation of results across studies, for anatomical education, and providing quantitative image-based navigation. Extensive work has been devoted to atlas construction for humans, macaque, and several non-primate species (e.g., rat). One notable gap in the literature is the common squirrel monkey - for which the primary published atlases date from the 1960's. The common squirrel monkey has been used extensively as surrogate for humans in biomedical studies, given its anatomical neuro-system similarities and practical considerations. This work describes the continued development of a multi-modal MRI atlas for the common squirrel monkey, for which a structural imaging space and gray matter parcels have been previously constructed. This study adds white matter tracts to the atlas. The new atlas includes 49 white matter (WM) tracts, defined using diffusion tensor imaging (DTI) in three animals and combines these data to define the anatomical locations of these tracks in a standardized coordinate system compatible with previous development. An anatomist reviewed the resulting tracts and the inter-animal reproducibility (i.e., the Dice index of each WM parcel across animals in common space) was assessed. The Dice indices range from 0.05 to 0.80 due to differences of local registration quality and the variation of WM tract position across individuals. However, the combined WM labels from the 3 animals represent the general locations of WM parcels, adding basic connectivity information to the atlas.

Extension of diffuse low-grade gliomas beyond radiological borders as shown by the coregistration of histopathological and magnetic resonance imaging data

OBJECTIVE Magnetic resonance imaging tends to underestimate the extent of diffuse low-grade gliomas (DLGGs). With the aim of studying the presence of tumor cells outside the radiological border, the authors developed a method of correlating MRI findings with histological data in patients with suspected DLGGs in whom en bloc resections were performed. METHODS Five patients with suspected DLGG suitable for en bloc resection were recruited from an ongoing prospective study. Sections of the entire tumor were immunostained with antibodies against mutated IDH1 protein (IDH1-R132H). Magnetic resonance images were coregistered with corresponding IDH1 images. The growth pattern of tumor cells in white and gray matter was assessed in comparison with signal changes on corresponding MRI slices. RESULTS Neuropathological assessment revealed DLGG in 4 patients and progression to WHO Grade III glioma in 1 patient. The tumor core consisted of a high density of IDH1-R132H-positive tumor cells and was located in both gray and white matter. Tumor cells infiltrated along the peripheral fibers of the white matter tracts. In all cases, tumor cells were found outside the radiological tumor border delineated on T2-FLAIR MRI sequences. CONCLUSIONS The authors present a new method for the coregistration of histological and radiological characteristics of en bloc-removed infiltrative brain tumors that discloses tumor invasion at the radiological tumor borders. This technique can be applied to evaluate the sensitivity of alternative imaging methods to detect scattered tumor cells at tumor borders. Accurate methods for detection of infiltrative tumor cells will improve the possibility of performing radical tumor resection. In future studies, the method could also be used for in vivo studies of tumor invasion.

Comparison of 3D orientation distribution functions measured with confocal microscopy and diffusion MRI

The ability of diffusion MRI (dMRI) fiber tractography to non-invasively map three-dimensional (3D) anatomical networks in the human brain has made it a valuable tool in both clinical and research settings. However, there are many assumptions inherent to any tractography algorithm that can limit the accuracy of the reconstructed fiber tracts. Among them is the assumption that the diffusion-weighted images accurately reflect the underlying fiber orientation distribution (FOD) in the MRI voxel. Consequently, validating dMRI's ability to assess the underlying fiber orientation in each voxel is critical for its use as a biomedical tool. Here, using post-mortem histology and confocal microscopy, we present a method to perform histological validation of orientation functions in 3D, which has previously been limited to two-dimensional analysis of tissue sections. We demonstrate the ability to extract the 3D FOD from confocal z-stacks, and quantify the agreement between the MRI estimates of orientation information obtained using constrained spherical deconvolution (CSD) and the true geometry of the fibers. We find an orientation error of approximately 6° in voxels containing nearly parallel fibers, and 10-11° in crossing fiber regions, and note that CSD was unable to resolve fibers crossing at angles below 60° in our dataset. This is the first time that the 3D white matter orientation distribution is calculated from histology and compared to dMRI. Thus, this technique serves as a gold standard for dMRI validation studies - providing the ability to determine the extent to which the dMRI signal is consistent with the histological FOD, and to establish how well different dMRI models can predict the ground truth FOD.

Three-Dimensional Histology Volume Reconstruction of Axonal Tract Tracing Data: Exploring Topographical Organization in Subcortical Projections from Rat Barrel Cortex

Topographical organization is a hallmark of the mammalian brain, and the spatial organization of axonal connections in different brain regions provides a structural framework accommodating specific patterns of neural activity. The presence, amount, and spatial distribution of axonal connections are typically studied in tract tracing experiments in which axons or neurons are labeled and examined in histological sections. Three-dimensional (3-D) reconstruction techniques are used to achieve more complete visualization and improved understanding of complex topographical relationships. 3-D reconstruction approaches based on manually or semi-automatically recorded spatial points representing axonal labeling have been successfully applied for investigation of smaller brain regions, but are not practically feasible for whole-brain analysis of multiple regions. We here reconstruct serial histological images from four whole brains (originally acquired for conventional microscopic analysis) into volumetric images that are spatially registered to a 3-D atlas template. The aims were firstly to evaluate the quality of the 3-D reconstructions and the usefulness of the approach, and secondly to investigate axonal projection patterns and topographical organization in rat corticostriatal and corticothalamic pathways. We demonstrate that even with the limitations of the original routine histological material, the 3-D reconstructed volumetric images allow efficient visualization of tracer injection sites and axonal labeling, facilitating detection of spatial distributions and across-case comparisons. Our results further show that clusters of S1 corticostriatal and corticothalamic projections are distributed within narrow, elongated or spherical subspaces extending across the entire striatum / thalamus. We conclude that histology volume reconstructions facilitate mapping of spatial distribution patterns and topographical organization. The reconstructed image volumes are shared via the Rodent Brain Workbench (www.rbwb.org).

Histological validation of high-resolution DTI in human post mortem tissue

Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects.

Integrating histology and MRI in the first digital brain of common squirrel monkey, Saimiri sciureus

This effort is a continuation of development of a digital brain atlas of the common squirrel monkey, Saimiri sciureus, a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. Here, we present the integration of histology with multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. The central concept of this work is to use block face photography to establish an intermediate common space in coordinate system which preserves the high resolution in-plane resolution of histology while enabling 3-D correspondence with MRI. In vivo MRI acquisitions include high resolution T2 structural imaging (300 µm isotropic) and low resolution diffusion tensor imaging (600 um isotropic). Ex vivo MRI acquisitions include high resolution T2 structural imaging and high resolution diffusion tensor imaging (both 300 µm isotropic). Cortical regions were manually annotated on the co-registered volumes based on published histological sections in-plane. We describe mapping of histology and MRI based data of the common squirrel monkey and construction of a viewing tool that enable online viewing of these datasets. The previously descried atlas MRI is used for its deformation to provide accurate conformation to the MRI, thus adding information at the histological level to the MRI volume. This paper presents the mapping of single 2D image slice in block face as a proof of concept and this can be extended to map the atlas space in 3D coordinate system as part of the future work and can be loaded to an XNAT system for further use.

Mapping mean axon diameter and axonal volume fraction by MRI using temporal diffusion spectroscopy

Mapping mean axon diameter and intra-axonal volume fraction may have significant clinical potential because nerve conduction velocity is directly dependent on axon diameter, and several neurodegenerative diseases affect axons of specific sizes and alter axon counts. Diffusion-weighted MRI methods based on the pulsed gradient spin echo (PGSE) sequence have been reported to be able to assess axon diameter and volume fraction non-invasively. However, due to the relatively long diffusion times used, e.g. >20ms, the sensitivity to small axons (diameter<2μm) is low, and the derived mean axon diameter has been reported to be overestimated. In the current study, oscillating gradient spin echo (OGSE) diffusion sequences with variable frequency gradients were used to assess rat spinal white matter tracts with relatively short effective diffusion times (1-5ms). In contrast to previous PGSE-based methods, the extra-axonal diffusion cannot be modeled as hindered (Gaussian) diffusion when short diffusion times are used. Appropriate frequency-dependent rates are therefore incorporated into our analysis and validated by histology-based computer simulation of water diffusion. OGSE data were analyzed to derive mean axon diameters and intra-axonal volume fractions of rat spinal white matter tracts (mean axon diameter of ~1.27-5.54μm). The estimated values were in good agreement with histology, including the small axon diameters (<2.5μm). This study establishes a framework for the quantification of nerve morphology using the OGSE method with high sensitivity to small axons.

Evaluation of coils for imaging histological slides: signal-to-noise ratio and filling factor

PURPOSE

To investigate the relative gain in sensitivity of five histology coils designed in-house to accommodate tissue sections of various sizes and compare with commercial mouse head coils.

METHODS

The coil set was tailored to house tissue sections ranging from 5 to1000 µm encased in either glass slides or coverslips.

RESULTS

Our simulations and experimental measurements demonstrated that although the sensitivity of this flat structure consistently underperforms relative to a birdcage head coil based on the gain expected from their respective filling factor ratios, our results demonstrate that it can still provide a remarkable gain in sensitivity. Our study also describes preparation protocols for freshly excised sections, as well as premounted tissue slides of both mouse and human specimens. Examples of the exceptional level of tissue detail and the near-perfect magnetic resonance imaging to light microscopic image coregistration are provided.

CONCLUSION

The increase in filling factor achieved by the histology radiofrequency (RF) probe overcomes the losses associated with electric leaks inherent to this structure, leading to a 6.7-fold improvement in performance for the smallest coil implemented. Alternatively, the largest histology coil design exhibited equal sensitivity to the mouse head coil while nearly doubling the RF planar area coverage.

A brain MRI atlas of the common squirrel monkey, Saimiri sciureus

The common squirrel monkey, Saimiri sciureus, is a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. It is one of the most commonly used South American primates in biomedical research. Unlike its Old World macaque cousins, no digital atlases have described the organization of the squirrel monkey brain. Here, we present a multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. In vivo MRI acquisitions include T2 structural imaging and diffusion tensor imaging. Ex vivo MRI acquisitions include T2 structural imaging and diffusion tensor imaging. Cortical regions were manually annotated on the co-registered volumes based on published histological sections.

Validation of DTI tractography-based measures of primary motor area connectivity in the squirrel monkey brain

Diffusion tensor imaging (DTI) tractography provides noninvasive measures of structural cortico-cortical connectivity of the brain. However, the agreement between DTI-tractography-based measures and histological 'ground truth' has not been quantified. In this study, we reconstructed the 3D density distribution maps (DDM) of fibers labeled with an anatomical tracer, biotinylated dextran amine (BDA), as well as DTI tractography-derived streamlines connecting the primary motor (M1) cortex to other cortical regions in the squirrel monkey brain. We evaluated the agreement in M1-cortical connectivity between the fibers labeled in the brain tissue and DTI streamlines on a regional and voxel-by-voxel basis. We found that DTI tractography is capable of providing inter-regional connectivity comparable to the neuroanatomical connectivity, but is less reliable measuring voxel-to-voxel variations within regions.

Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI

Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200μm spacing and 5μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4Tesla MRI scan of the intact, whole hippocampal formation acquired with 160μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1cm-thick tissue sub-blocks acquired with 200μm isotropic resolution. These 1cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.