Merged magnetic resonance and light sheet microscopy of the whole mouse brain.

We have developed workflows to align 3D magnetic resonance histology (MRH) of the mouse brain with light sheet microscopy (LSM) and 3D delineations of the same specimen. We start with MRH of the brain in the skull with gradient echo and diffusion tensor imaging (DTI) at 15 µm isotropic resolution which is ~ 1,000 times higher than that of most preclinical MRI. Connectomes are generated with superresolution tract density images of ~5 µm. Brains are cleared, stained for selected proteins, and imaged by LSM at 1.8 µm/pixel. LSM data are registered into the reference MRH space with labels derived from the ABA common coordinate framework. The result is a high-dimensional integrated volume with registration (HiDiver) with alignment precision better than 50 µm. Throughput is sufficiently high that HiDiver is being used in quantitative studies of the impact of gene variants and aging on mouse brain cytoarchitecture and connectomics.

A time-course study of actively stained mouse brains: Diffusion tensor imaging parameters and connectomic stability over 1 year.

While the application of diffusion tensor imaging (DTI), tractography, and connectomics to fixed tissue is a common practice today, there have been limited studies examining the effects of fixation on brain microstructure over extended periods. This mouse model time-course study reports the changes of regional brain volumes and diffusion scalar parameters, such as fractional anisotropy, across 12 representative brain regions as measures of brain structural stability. The scalar DTI parameters and regional volumes were highly variable over the first 2 weeks after fixation. The same parameters were consistent over a 2-8-week window after fixation, which means confounds from tissue stability over that scanning window were minimal. Quantitative connectomes were analyzed over the same time with extension out to 1 year. While there was some change in the scalar metrics at 1 year after fixation, these changes were sufficiently small, particularly in white matter, to support reproducible connectomes over a period ranging from 2-weeks to 1-year post-fixation. These findings delineate a scanning period, during which brain volumes, diffusion scalar metrics, and connectomes are remarkably consistent.

A high-resolution interactive atlas of the human brainstem using magnetic resonance imaging.

Conventional atlases of the human brainstem are limited by the inflexible, sparsely-sampled, two-dimensional nature of histology, or the low spatial resolution of conventional magnetic resonance imaging (MRI). Postmortem high-resolution MRI circumvents the challenges associated with both modalities. A single human brainstem specimen extending from the rostral diencephalon through the caudal medulla was prepared for imaging after the brain was removed from a 65-year-old male within 24 h of death. The specimen was formalin-fixed for two weeks, then rehydrated and placed in a custom-made MRI compatible tube and immersed in liquid fluorocarbon. MRI was performed in a 7-Tesla scanner with 120 unique diffusion directions. Acquisition time for anatomic and diffusion images were 14 h and 208 h, respectively. Segmentation was performed manually. Deterministic fiber tractography was done using strategically chosen regions of interest and avoidance, with manual editing using expert knowledge of human neuroanatomy. Anatomic and diffusion images were rendered with isotropic resolutions of 50 µm and 200 µm, respectively. Ninety different structures were segmented and labeled, and 11 different fiber bundles were rendered with tractography. The complete atlas is available online for interactive use at https://www.civmvoxport.vm.duke.edu/voxbase/login.php?return_url=%2Fvoxbase%2F. This atlas presents multiple contrasting datasets and selected tract reconstruction with unprecedented resolution for MR imaging of the human brainstem. There are immediate applications in neuroanatomical education, with the potential to serve future applications for neuroanatomical research and enhanced neurosurgical planning through "safe" zones of entry into the human brainstem.

An Open Resource: MR and light sheet microscopy stereotaxic atlas of the mouse brain.

We have combined MR histology and light sheet microscopy (LSM) of five postmortem C57BL/6J mouse brains in a stereotaxic space based on micro-CT yielding a multimodal 3D atlas with the highest spatial and contrast resolution yet reported. Brains were imaged in situ with multi gradient echo (mGRE) and diffusion tensor imaging (DTI) at 15 µm resolution (∼ 2.4 million times that of clinical MRI). Scalar images derived from the average DTI and mGRE provide unprecedented contrast in 14 complementary 3D volumes, each highlighting distinct histologic features. The same tissues scanned with LSM and registered into the stereotaxic space provide 17 different molecular cell type stains. The common coordinate framework labels (CCFv3) complete the multimodal atlas. The atlas has been used to correct distortions in the Allen Brain Atlas and harmonize it with Franklin Paxinos. It provides a unique resource for stereotaxic labeling of mouse brain images from many sources.

Quantitative mouse brain phenotyping based on single and multispectral MR protocols.

Sophisticated image analysis methods have been developed for the human brain, but such tools still need to be adapted and optimized for quantitative small animal imaging. We propose a framework for quantitative anatomical phenotyping in mouse models of neurological and psychiatric conditions. The framework encompasses an atlas space, image acquisition protocols, and software tools to register images into this space. We show that a suite of segmentation tools (Avants, Epstein et al., 2008) designed for human neuroimaging can be incorporated into a pipeline for segmenting mouse brain images acquired with multispectral magnetic resonance imaging (MR) protocols. We present a flexible approach for segmenting such hyperimages, optimizing registration, and identifying optimal combinations of image channels for particular structures. Brain imaging with T1, T2* and T2 contrasts yielded accuracy in the range of 83% for hippocampus and caudate putamen (Hc and CPu), but only 54% in white matter tracts, and 44% for the ventricles. The addition of diffusion tensor parameter images improved accuracy for large gray matter structures (by >5%), white matter (10%), and ventricles (15%). The use of Markov random field segmentation further improved overall accuracy in the C57BL/6 strain by 6%; so Dice coefficients for Hc and CPu reached 93%, for white matter 79%, for ventricles 68%, and for substantia nigra 80%. We demonstrate the segmentation pipeline for the widely used C57BL/6 strain, and two test strains (BXD29, APP/TTA). This approach appears promising for characterizing temporal changes in mouse models of human neurological and psychiatric conditions, and may provide anatomical constraints for other preclinical imaging, e.g. fMRI and molecular imaging. This is the first demonstration that multiple MR imaging modalities combined with multivariate segmentation methods lead to significant improvements in anatomical segmentation in the mouse brain.

Restoring morphology of light sheet microscopy data based on magnetic resonance histology.

The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g., The Allen Brain Atlas (ABA), which have been generated from tissue that has been distorted by removal from the skull, fixation and physical handling. This limits the accuracy of the regional morphologic measurement. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same specimen to restore the morphology of the LSM images to the in-skull geometry. Our registration pipeline which maps 3D LSM big data (terabyte per dataset) to MRH of the same mouse brain provides registration with low displacement error in ∼10 h with limited manual input. The registration pipeline is optimized using multiple stages of transformation at multiple resolution scales. A three-step procedure including pointset initialization, automated ANTs registration with multiple optimized transformation stages, and finalized application of the transforms on high-resolution LSM data has been integrated into a simple, structured, and robust workflow. Excellent agreement has been seen between registered LSM data and reference MRH data both locally and globally. This workflow has been applied to a collection of datasets with varied combinations of MRH contrasts from diffusion tensor images and LSM with varied immunohistochemistry, providing a routine method for streamlined registration of LSM images to MRH. Lastly, the method maps a reduced set of the common coordinate framework (CCFv3) labels from the Allen Brain Atlas onto the geometrically corrected full resolution LSM data. The pipeline maintains the individual brain morphology and allows more accurate regional annotations and measurements of volumes and cell density.

Volumetric analysis of the aging auditory pathway using high resolution magnetic resonance histology.

Numerous shown consequences of age-related hearing loss have been unveiled; however, the relationship of the cortical and subcortical structures of the auditory pathway with aging is not well known. Investigations into neural structure analysis remain sparse due to difficulties of doing so in animal models; however, recent technological advances have been able to achieve a resolution adequate to perform such studies even in the small mouse. We utilize 12 members of the BXD family of recombinant inbred mice and aged separate cohorts. Utilizing novel magnetic resonance histology imaging techniques, we imaged these mice and generated high spatial resolution three dimensional images which were then comprehensively labeled. We completed volumetric analysis of 12 separate regions of interest specific to the auditory pathway brainstem nuclei and cortical areas with focus on the effect of aging upon said structures. Our results showed significant interstrain variation in the age-related effect on structure volume supporting a genetic influence in this interaction. Through multivariable modeling, we observed heterogenous effects of aging between different structures. Six of the 12 regions of interests demonstrated a significant age-related effect. The auditory cortex and ventral cochlear nucleus were found to decrease in volume with age, while the medial division of the medial geniculate nucleus, lateral lemniscus and its nucleus, and the inferior colliculus increased in size with age. Additionally, no sex-based differences were noted, and we observed a negative relationship between auditory cortex volume and mouse weight. This study is one of the first to perform comprehensive magnetic resonance imaging and quantitative analysis in the mouse brain auditory pathway cytoarchitecture, offering both novel insights into the neuroanatomical basis of age-related changes in hearing as well as evidence toward a genetic influence in this interaction. High resonance magnetic resonance imaging provides a promising efficacious avenue in future mouse model hearing loss investigations.

Small Animal Multivariate Brain Analysis (SAMBA) - a High Throughput Pipeline with a Validation Framework.

While many neuroscience questions aim to understand the human brain, much current knowledge has been gained using animal models, which replicate genetic, structural, and connectivity aspects of the human brain. While voxel-based analysis (VBA) of preclinical magnetic resonance images is widely-used, a thorough examination of the statistical robustness, stability, and error rates is hindered by high computational demands of processing large arrays, and the many parameters involved therein. Thus, workflows are often based on intuition or experience, while preclinical validation studies remain scarce. To increase throughput and reproducibility of quantitative small animal brain studies, we have developed a publicly shared, high throughput VBA pipeline in a high-performance computing environment, called SAMBA. The increased computational efficiency allowed large multidimensional arrays to be processed in 1-3 days-a task that previously took ~1 month. To quantify the variability and reliability of preclinical VBA in rodent models, we propose a validation framework consisting of morphological phantoms, and four metrics. This addresses several sources that impact VBA results, including registration and template construction strategies. We have used this framework to inform the VBA workflow parameters in a VBA study for a mouse model of epilepsy. We also present initial efforts towards standardizing small animal neuroimaging data in a similar fashion with human neuroimaging. We conclude that verifying the accuracy of VBA merits attention, and should be the focus of a broader effort within the community. The proposed framework promotes consistent quality assurance of VBA in preclinical neuroimaging, thus facilitating the creation and communication of robust results.