Automated segmentation of the actively stained mouse brain using multi-spectral MR microscopy

The Duke Mouse Brain Atlas: MRI and light sheet microscopy stereotaxic atlas of the mouse brain

Atlases of the brain are critical resources that make it possible to share data in a common reference frame. Unexpectedly, there is no three-dimensional (3D) stereotaxic atlas of the mouse brain that provides whole brain coverage at macro to single-cell levels. Diffusion tensor images from five perfusion-fixed (in skull) specimens were acquired at 15 micrometers, the highest resolution ever reported. Diffusion tensor imaging yields multiple 3D volumes, each of which highlights unique cytoarchitecture. The averages were mapped into micro-computed tomography of the mouse skull to create external landmarks (bregma and lambda). Light sheet images of the same brains were coregistered, providing cell maps in the same stereotaxic space. The Allen Reference Atlas was registered to the volume to correct the geometric distortion in that atlas and bring it into the stereotaxic space. The resulting multiscalar (13 terabytes) atlas provides a common spatial framework to anneal data across molecular, structural, and functional studies of mice.

Brain network fingerprints of Alzheimer's disease risk factors in mouse models with humanized APOE alleles

Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic and modifiable risk factors influence disease susceptibility are under intense investigation, with APOE being the major genetic risk factor for late onset AD. Yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors, including APOE genotype, age, sex, diet, and immunity we used a cross sectional design, leveraging mice expressing human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. We used network topological and GraphClass analyses of brain connectomes derived from accelerated diffusion-weighted MRI to assess the global and local impact of risk factors, in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles. Sparse Canonical Correlation Analysis (CCA) including spatial memory as a risk factor resulted in a network comprising 80 edges, showing significant overlap with risk-associated networks from GraphClass. The largest overlaps were observed with networks impacted by diet (47 edges), immunity (39 edges), APOE3 vs 4 (26 edges), sex (23 edges), and age (19 edges), the resulting networks supporting the use of sensory cues in spatial memory retrieval. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.

RS2-Net: An end-to-end deep learning framework for rodent skull stripping in multi-center brain MRI

Skull stripping is a crucial preprocessing step in magnetic resonance imaging (MRI), where experts manually create brain masks. This labor-intensive process heavily relies on the annotator's expertise, as automation faces challenges such as low tissue contrast, significant variations in image resolution, and blurred boundaries between the brain and surrounding tissues, particularly in rodents. In this study, we have developed a lightweight framework based on Swin-UNETR to automate the skull stripping process in MRI scans of mice and rats. The primary objective of this framework is to eliminate the need for preprocessing, reduce the workload, and provide an out-of-the-box solution capable of adapting to various MRI image resolutions. By employing a lightweight neural network, we aim to lower the performance requirements of the framework. To validate the effectiveness of our approach, we trained and evaluated the network using publicly available multi-center data, encompassing 1,037 rodents and 1,142 images from 89 centers, resulting in a preliminary mean Dice coefficient of 0.9914. The framework, data, and pre-trained models can be found on the following link: https://github.com/VitoLin21/Rodent-Skull-Stripping.

APOE, Immune Factors, Sex, and Diet Interact to Shape Brain Networks in Mouse Models of Aging

Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic, fixed and modifiable risk factors influence susceptibility to AD are under intense investigation, yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors including APOE genotype, age, sex, diet, and immunity we leveraged mice expressing the human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. Employing graph analyses of brain connectomes derived from accelerated diffusion-weighted MRI, we assessed the global and local impact of risk factors in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.

Significance Statement

Current interventions for Alzheimer's disease (AD) do not provide a cure, and are delivered years after neuropathological onset. Addressing the impact of risk factors on brain networks holds promises for early detection, prevention, and revealing putative therapeutic targets at preclinical stages. We utilized six mouse models to investigate the impact of factors, including APOE genotype, age, sex, immunity, and diet, on brain networks. Large structural connectomes were derived from high resolution compressed sensing diffusion MRI. A highly parallelized graph classification identified subnetworks associated with unique risk factors, revealing their network fingerprints, and a common network composed of 63 connections with shared vulnerability to all risk factors. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles.

Development and advancements in rodent MRI-based brain atlases

Rodents, particularly mice and rats, are extensively utilized in fundamental neuroscience research. Brain atlases have played a pivotal role in this field, evolving from traditional printed histology atlases to digital atlases incorporating diverse imaging datasets. Magnetic resonance imaging (MRI)-based brain atlases, also known as brain maps, have been employed in specific studies. However, the existence of numerous versions of MRI-based brain atlases has impeded their standardized application and widespread use, despite the consensus within the academic community regarding their significance in mice and rats. Furthermore, there is a dearth of comprehensive and systematic reviews on MRI-based brain atlases for rodents. This review aims to bridge this gap by providing a comprehensive overview of the advancements in MRI-based brain atlases for rodents, with a specific focus on mice and rats. It seeks to explore the advantages and disadvantages of histologically printed brain atlases in comparison to MRI brain atlases, delineate the standardized methods for creating MRI brain atlases, and summarize their primary applications in neuroscience research. Additionally, this review aims to assist researchers in selecting appropriate versions of MRI brain atlases for their studies or refining existing MRI brain atlas resources, thereby facilitating the development and widespread adoption of standardized MRI-based brain atlases in rodents.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

A multicontrast MR atlas of the Wistar rat brain

We describe a multi-contrast, multi-dimensional atlas of the Wistar rat acquired at microscopic spatial resolution using magnetic resonance histology (MRH). Diffusion weighted images, and associated scalar images were acquired of a single specimen with a fully sampled Fourier reconstruction, 61 angles and b=3000 s/mm2 yielding 50 um isotropic spatial resolution. The higher angular sampling allows use of the GQI algorithm improving the angular invariance of the scalar images and yielding an orientation distribution function to assist in delineating subtle boundaries where there are crossing fibers  and track density images providing insight into local fiber architecture.  A multigradient echo image of the same specimen was acquired at 25 um isotropic spatial resolution. A quantitative susceptibility map enhances fiber architecture relative to the magnitude images.  An accompanying multi-specimen atlas (n=6) was acquired with compressed sensing with the same diffusion protocol as used for the single specimen atlas.  An average was created using diffeomorphic mapping. Scalar volumes from the diffusion data, a T2* weighted volume, a quantitative susceptibility map, and a track density volume, all registered to the same space provide multiple contrasts to assist in anatomic delineation. The new template  provides significantly increased contrast in the scalar DTI images when compared to previous atlases. A compact interactive viewer based on 3D Slicer is provided to facilitate comparison among the contrasts in the multiple volumes. The single volume and average atlas with multiple 3D volumes provide an improved template for anatomic interrogation of the Wistar rat brain. The improved contrast to noise in the scalar DTI images and the addition of other volumes (eg. QA,QSM,TDI ) will facilitate automated label registration for MR histology and preclinical imaging.

Constructing the rodent stereotaxic brain atlas: a survey

The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience. Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxic brain atlasing: brain image, atlas, and stereotaxis. We also refine four technical indices for evaluating the construction of atlases: the quality of staining and labeling, the granularity of delineation, spatial resolution, and the precision of spatial location and orientation. Additionally, we discuss state-of-the-art technologies and their trends in the fields of image acquisition, stereotaxic coordinate construction, image processing, anatomical structure recognition, and publishing: the procedures of brain atlas illustration. We believe that the use of single-cell resolution and micron-level location precision will become a future trend in the study of the stereotaxic brain atlas, which will greatly benefit the development of neuroscience.

Microcephaly with altered cortical layering in GIT1 deficiency revealed by quantitative neuroimaging

G Protein-Coupled Receptor Kinase-Interacting Protein-1 (GIT1) regulates neuronal functions, including cell and axon migration and synapse formation and maintenance, and GIT1 knockout (KO) mice exhibit learning and memory deficits. We noted that male and female GIT1-KO mice exhibit neuroimaging phenotypes including microcephaly, and altered cortical layering, with a decrease in neuron density in cortical layer V. Micro-CT and magnetic resonance microscopy (MRM) were used to identify morphometric phenotypes for the skulls and throughout the GIT1-KO brains. High field MRM of actively-stained mouse brains from GIT1-KO and wild type (WT) controls (n = 6 per group) allowed segmenting 37 regions, based on co-registration to the Waxholm Space atlas. Overall brain size in GIT1-KO mice was ~32% smaller compared to WT controls. After correcting for brain size, several regions were significantly different in GIT1-KO mice relative to WT, including the gray matter of the ventral thalamic nuclei and the rest of the thalamus, the inferior colliculus, and pontine nuclei. GIT1-KO mice had reduced volume of white matter tracts, most notably in the anterior commissure (~26% smaller), but also in the cerebral peduncle, fornix, and spinal trigeminal tract. On the other hand, the basal ganglia appeared enlarged in GIT1-KO mice, including the globus pallidus, caudate putamen, and particularly the accumbens - supporting a possible vulnerability to addiction. Volume based morphometry based on high-resolution MRM (21.5 μm isotropic voxels) was effective in detecting overall, and local differences in brain volumes in GIT1-KO mice, including in white matter tracts. The reduced relative volume of specific brain regions suggests a critical, but not uniform, role for GIT1 in brain development, conducive to brain microcephaly, and aberrant connectivity.

Brain orchestration of pregnancy and maternal behavior in mice: A longitudinal morphometric study

Reproduction induces changes within the brain to prepare for gestation and motherhood. However, the dynamic of these central changes and their relationships with the development of maternal behavior remain poorly understood. Here, we describe a longitudinal morphometric neuroimaging study in female mice between pre-gestation and weaning, using new magnetic resonance imaging (MRI) resources comprising a high-resolution brain template, its associated tissue priors (60-µm isotropic resolution) and a corresponding mouse brain atlas (1320 regions of interest). Using these tools, we observed transient hypertrophies not only within key regions controlling gestation and maternal behavior (medial preoptic area, bed nucleus of the stria terminalis), but also in the amygdala, caudate nucleus and hippocampus. Additionally, unlike females exhibiting lower levels of maternal care, highly maternal females developed transient hypertrophies in somatosensory, entorhinal and retrosplenial cortices among other regions. Therefore, coordinated and transient brain modifications associated with maternal performance occurred during gestation and lactation.

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 s and no pre-processing requirements. We trained and validated MU-Net on 128 T2-weighted mouse MRI volumes as well as on the publicly available MRM NeAT dataset of 10 MRI volumes. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). Further, we explored the effectiveness of our network in the presence of different architectural features, including skip connections and recently proposed framing connections, and the effects of the age range of the training set animals. These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net.

Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net

Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans, because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved superior averaged Dice similarity coefficient to ground truth T2-weighted rapid acquisition with relaxation enhancement and T2∗-weighted echo planar imaging data in both rats and mice (all p < 0.05), demonstrating robust performance of our approach across various MRI protocols.

The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas

Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 μm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 μm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).

Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions

Network approaches provide sensitive biomarkers for neurological conditions, such as Alzheimer's disease (AD). Mouse models can help advance our understanding of underlying pathologies, by dissecting vulnerable circuits. While the mouse brain contains less white matter compared to the human brain, axonal diameters compare relatively well (e.g., ~0.6 μm in the mouse and ~0.65-1.05 μm in the human corpus callosum). This makes the mouse an attractive test bed for novel diffusion models and imaging protocols. Remaining questions on the accuracy and uncertainty of connectomes have prompted us to evaluate diffusion imaging protocols with various spatial and angular resolutions. We have derived structural connectomes by extracting gradient subsets from a high-spatial, high-angular resolution diffusion acquisition (120 directions, 43-μm-size voxels). We have simulated protocols with 12, 15, 20, 30, 45, 60, 80, 100, and 120 angles and at 43, 86, or 172-μm voxel sizes. The rotational stability of these schemes increased with angular resolution. The minimum condition number was achieved for 120 directions, followed by 60 and 45 directions. The percentage of voxels containing one dyad was exceeded by those with two dyads after 45 directions, and for the highest spatial resolution protocols. For the 86- or 172-μm resolutions, these ratios converged toward 55% for one and 39% for two dyads, respectively, with <7% from voxels with three dyads. Tractography errors, estimated through dyad dispersion, decreased most with angular resolution. Spatial resolution effects became noticeable at 172 μm. Smaller tracts, e.g., the fornix, were affected more than larger ones, e.g., the fimbria. We observed an inflection point for 45 directions, and an asymptotic behavior after 60 directions, corresponding to similar projection density maps. Spatially downsampling to 86 μm, while maintaining the angular resolution, achieved a subgraph similarity of 96% relative to the reference. Using 60 directions with 86- or 172-μm voxels resulted in 94% similarity. Node similarity metrics indicated that major white matter tracts were more robust to downsampling relative to cortical regions. Our study provides guidelines for new protocols in mouse models of neurological conditions, so as to achieve similar connectomes, while increasing efficiency.

Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation

Brain volume measurements extracted from structural MRI data sets are a widely accepted neuroimaging biomarker to study mouse models of neurodegeneration. Whether to acquire and analyze data in vivo or ex vivo is a crucial decision during the phase of experimental designs, as well as data analysis. In this work, we extracted the brain structures for both longitudinal in vivo and single-time-point ex vivo MRI acquired from the same animals using accurate automatic multi-atlas structural parcellation, and compared the corresponding statistical and classification analysis. We found that most gray matter structures volumes decrease from in vivo to ex vivo, while most white matter structures volume increase. The level of structural volume change also varies between different genetic strains and treatment. In addition, we showed superior statistical and classification power of ex vivo data compared to the in vivo data, even after resampled to the same level of resolution. We further demonstrated that the classification power of the in vivo data can be improved by incorporating longitudinal information, which is not possible for ex vivo data. In conclusion, this paper demonstrates the tissue-specific changes, as well as the difference in statistical and classification power, between the volumetric analysis based on the in vivo and ex vivo structural MRI data. Our results emphasize the importance of longitudinal analysis for in vivo data analysis.

An automated mapping method for Nissl-stained mouse brain histologic sections

BACKGROUND

Histologic evaluation of the central nervous system is often a critical endpoint in in vivo efficacy studies, and is considered the essential component of neurotoxicity assessment in safety studies. Automated image analysis is a powerful tool that can radically reduce the workload associated with evaluating brain histologic sections.

NEW METHOD

We developed an automated brain mapping method that identifies neuroanatomic structures in mouse histologic coronal brain sections. The method utilizes the publicly available Allen Brain Atlas to map brain regions on digitized Nissl-stained sections.

RESULTS

The method's accuracy was first assessed by comparing the mapping results to structure delineations from the Franklin and Paxinos (FP) mouse brain atlas. Brain regions mapped from FP Nissl-stained sections and calculated volumes were similar to structure delineations and volumes derived from corresponding FP illustrations. We subsequently applied our method to mouse brain sections from an in vivo study where the hippocampus was the structure of interest. Nissl-stained sections were mapped and hippocampal boundaries transferred to adjacent immunohistochemically stained sections. Optical density quantification results were comparable to those from time-consuming, manually drawn hippocampal delineations on the IHC-stained sections.

COMPARISON WITH EXISTING METHODS

Compared to other published methods, our method requires less manual input, and has been validated comprehensively using a secondary atlas, as well as manually annotated brain IHC sections from 68 study mice.

CONCLUSIONS

We propose that our automated brain mapping method enables greater efficiency and consistency in mouse neuropathologic assessments.

An ultra-high field strength MR image-guided robotic needle delivery system for in-bore small animal interventions

PURPOSE

The purpose of this study was to develop and validate an image-guided robotic needle delivery system for accurate and repeatable needle targeting procedures in mouse brains inside the 12 cm inner diameter gradient coil insert of a 9.4 T MR scanner. Many preclinical research techniques require the use of accurate needle deliveries to soft tissues, including brain tissue. Soft tissues are optimally visualized in MR images, which offer high-soft tissue contrast, as well as a range of unique imaging techniques, including functional, spectroscopy and thermal imaging, however, there are currently no solutions for delivering needles to small animal brains inside the bore of an ultra-high field MR scanner. This paper describes the mechatronic design, evaluation of MR compatibility, registration technique, mechanical calibration, the quantitative validation of the in-bore image-guided needle targeting accuracy and repeatability, and demonstrated the system's ability to deliver needles in situ.

METHODS

Our six degree-of-freedom, MR compatible, mechatronic system was designed to fit inside the bore of a 9.4 T MR scanner and is actuated using a combination of piezoelectric and hydraulic mechanisms. The MR compatibility and targeting accuracy of the needle delivery system are evaluated to ensure that the system is precisely calibrated to perform the needle targeting procedures. A semi-automated image registration is performed to link the robot coordinates to the MR coordinate system. Soft tissue targets can be accurately localized in MR images, followed by automatic alignment of the needle trajectory to the target. Intra-procedure visualization of the needle target location and the needle were confirmed through MR images after needle insertion.

RESULTS

The effects of geometric distortions and signal noise were found to be below threshold that would have an impact on the accuracy of the system. The system was found to have negligible effect on the MR image signal noise and geometric distortion. The system was mechanically calibrated and the mean image-guided needle targeting and needle trajectory accuracies were quantified in an image-guided tissue mimicking phantom experiment to be 178 ± 54 μm and 0.27 ± 0.65°, respectively.

CONCLUSIONS

An MR image-guided system for in-bore needle deliveries to soft tissue targets in small animal models has been developed. The results of the needle targeting accuracy experiments in phantoms indicate that this system has the potential to deliver needles to the smallest soft tissue structures relevant in preclinical studies, at a wide variety of needle trajectories. Future work in the form of a fully-automated needle driver with precise depth control would benefit this system in terms of its applicability to a wider range of animal models and organ targets.

A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data

Interest in structural brain connectivity has grown with the understanding that abnormal neural connections may play a role in neurologic and psychiatric diseases. Small animal connectivity mapping techniques are particularly important for identifying aberrant connectivity in disease models. Diffusion magnetic resonance imaging tractography can provide nondestructive, 3D, brain-wide connectivity maps, but has historically been limited by low spatial resolution, low signal-to-noise ratio, and the difficulty in estimating multiple fiber orientations within a single image voxel. Small animal diffusion tractography can be substantially improved through the combination of ex vivo MRI with exogenous contrast agents, advanced diffusion acquisition and reconstruction techniques, and probabilistic fiber tracking. Here, we present a comprehensive, probabilistic tractography connectome of the mouse brain at microscopic resolution, and a comparison of these data with a neuronal tracer-based connectivity data from the Allen Brain Atlas. This work serves as a reference database for future tractography studies in the mouse brain, and demonstrates the fundamental differences between tractography and neuronal tracer data.

Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.

AtlasGuide: software for stereotaxic guidance using 3D CT/MRI hybrid atlases of developing mouse brains

Stereotaxic operations of the mouse brain are critically important for various types of neuroscience research studies, which include electrical recording of neural activities or site-targeted injection of stem cells, chemical tracers, and vectors, to name a few. To guide such operations, two-dimensional histology-based mouse brain atlases, such as the Paxinos and Franklin atlas, are widely used. Recently, computed tomography (CT) and magnetic resonance imaging (MRI) based hybrid three-dimensional (3D) atlases of developing mouse brains have been introduced. In this study, a new stereotaxic guidance software, called AtlasGuide, is introduced, which was developed to fully utilize the benefits of the 3D atlases for high-precision stereotaxic targeting. The AtlasGuide software provides functions to visualize oblique needle paths in 2D and 3D views, which allow investigators to simultaneously examine brain structures that could be damaged by the needle path and optimize the injection angles for high-precision trajectory selection through critical neural tissue. It allows reorientation and scaling of the atlases dynamically to match the orientation of the animal brain prepared for surgery, thereby eliminating the need to manually align the subject to the atlas, a procedure which is essential while using conventional 2D atlases. In addition, the software enables loading user-defined atlases when researchers need image-based guidance for different age groups, strains, or species. The software with integrated 3D stereotaxic mouse atlases is available for download at the http://lbam.med.jhmi.edu website.

Structural layers of ex vivo rat hippocampus at 7T MRI

Magnetic resonance imaging (MRI) applied to the hippocampus is challenging in studies of the neurophysiology of memory and the physiopathology of numerous diseases such as epilepsy, Alzheimer’s disease, ischemia, and depression. The hippocampus is a well-delineated cerebral structure with a multi-layered organization. Imaging of hippocampus layers is limited to a few studies and requires high magnetic field and gradient strength. We performed one conventional MRI sequence on a 7T MRI in order to visualize and to delineate the multi-layered hippocampal structure ex vivo in rat brains. We optimized a volumic three-dimensional T2 Rapid Acquisition Relaxation Enhancement (RARE) sequence and quantified the volume of the hippocampus and one of its thinnest layers, the stratum granulare of the dentate gyrus. Additionally, we tested passive staining by gadolinium with the aim of decreasing the acquisition time and increasing image contrast. Using appropriated settings, six discrete layers were differentiated within the hippocampus in rats. In the hippocampus proper or Ammon’s Horn (AH): the stratum oriens, the stratum pyramidale of, the stratum radiatum, and the stratum lacunosum moleculare of the CA1 were differentiated. In the dentate gyrus: the stratum moleculare and the stratum granulare layer were seen distinctly. Passive staining of one brain with gadolinium decreased the acquisition time by four and improved the differentiation between the layers. A conventional sequence optimized on a 7T MRI with a standard receiver surface coil will allow us to study structural layers (signal and volume) of hippocampus in various rat models of neuropathology (anxiety, epilepsia, neurodegeneration).

Automated segmentation of mouse brain images using multi-atlas multi-ROI deformation and label fusion

We propose an automated multi-atlas and multi-ROI based segmentation method for both skull-stripping of mouse brain and the ROI-labeling of mouse brain structures from the three dimensional (3D) magnetic resonance images (MRI). Three main steps are involved in our method. First, a region of interest (ROI) guided warping algorithm is designed to register multi-atlas images to the subject space, by considering more on the matching of image contents around the ROI boundaries which are more important for ROI labeling. Then, a multi-atlas and multi-ROI based deformable segmentation method is adopted to refine the ROI labeling result by deforming each ROI surface via boundary recognizers (i.e., SVM classifiers) trained on local surface patches. Finally, a local-mutual-information (MI) based multi-label fusion technique is proposed for allowing the atlases with better local image similarity with the subject to have more contributions in label fusion. The experimental results show that our method works better than the conventional methods on both in vitro and in vivo mouse brain datasets.

A segmentation protocol and MRI atlas of the C57BL/6J mouse neocortex

The neocortex is the largest component of the mammalian cerebral cortex. It integrates sensory inputs with experiences and memory to produce sophisticated responses to an organism's internal and external environment. While areal patterning of the mouse neocortex has been mapped using histological techniques, the neocortex has not been comprehensively segmented in magnetic resonance images. This study presents a method for systematic segmentation of the C57BL/6J mouse neocortex. We created a minimum deformation atlas, which was hierarchically segmented into 74 neocortical and cortical-related regions, making it the most detailed atlas of the mouse neocortex currently available. In addition, we provide mean volumes and relative intensities for each structure as well as a nomenclature comparison between the two most cited histological atlases of the mouse brain. This MR atlas is available for download, and it should enable researchers to perform automated segmentation in genetic models of cortical disorders.

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.

Segmentation propagation using a 3D embryo atlas for high-throughput MRI phenotyping: comparison and validation with manual segmentation

Effective methods for high-throughput screening and morphometric analysis are crucial for phenotyping the increasing number of mouse mutants that are being generated. Automated segmentation propagation for embryo phenotyping is an emerging application that enables noninvasive and rapid quantification of substructure volumetric data for morphometric analysis. We present a study to assess and validate the accuracy of brain and kidney volumes generated via segmentation propagation in an ex vivo mouse embryo MRI atlas comprising three different groups against the current "gold standard"--manual segmentation. Morphometric assessment showed good agreement between automatically and manually segmented volumes, demonstrating that it is possible to assess volumes for phenotyping a population of embryos using segmentation propagation with the same variation as manual segmentation. As part of this study, we have made our average atlas and segmented volumes freely available to the community for use in mouse embryo phenotyping studies. These MRI datasets and automated methods of analyses will be essential for meeting the challenge of high-throughput, automated embryo phenotyping.

3-dimensional imaging modalities for phenotyping genetically engineered mice

A variety of 3-dimensional (3D) digital imaging modalities are available for whole-body assessment of genetically engineered mice: magnetic resonance microscopy (MRM), X-ray microcomputed tomography (microCT), optical projection tomography (OPT), episcopic and cryoimaging, and ultrasound biomicroscopy (UBM). Embryo and adult mouse phenotyping can be accomplished at microscopy or near microscopy spatial resolutions using these modalities. MRM and microCT are particularly well-suited for evaluating structural information at the organ level, whereas episcopic and OPT imaging provide structural and functional information from molecular fluorescence imaging at the cellular level. UBM can be used to monitor embryonic development longitudinally in utero. Specimens are not significantly altered during preparation, and structures can be viewed in their native orientations. Technologies for rapid automated data acquisition and high-throughput phenotyping have been developed and continually improve as this exciting field evolves.

Distortion in formalin-fixed brains: using geometric morphometrics to quantify the worst-case scenario in mice

Although morphometric studies of fixed mammalian brains are an integral part of neuroscience, the nature of fixation-related morphometric artifacts is not well understood beyond assessments of size changes over fixation time. This study is the first to quantitatively co-evaluate the effects of the most common brain tissue fixative--formalin--on brain shape, size, and weight, using two-dimensional landmark analysis of mouse brains fixed in unbuffered, non-saline formalin from fresh specimens up to 213 days of preservation. The brains show a typical swelling reaction with subsequent decline in size and weight. Weight initially under- and later over-estimates size, so that the practice of using weight to estimate volume can be problematic. Time to recovery of original size resembled that of much larger brained mammals, suggesting that the slow reaction of formalin with tissue components mainly determines recovery times. Non-size related (anisotropic) distortion of different brain areas accounted for around a quarter of overall change suggesting that the use of "all-brain" fixation correction factors can introduce considerable error. Distortion occurs mostly after the first day of fixation, and extended fixation times impact mostly on size, not shape. Fixation effects relatively wider and stouter brain dimensions, except the cerebellum whose shape changes less. Evidence from the literature suggests that this pattern may be common to mammals due to structural commonalities.

A prior feature SVM-MRF based method for mouse brain segmentation

We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders.

Morphological studies of the murine heart based on probabilistic and statistical atlases

This study directly compares morphological features of the mouse heart in its end-relaxed state based on constructed morphometric maps and atlases using principal component analysis in C57BL/6J (n=8) and DBA (n=5) mice. In probabilistic atlases, a gradient probability exists for both strains in longitudinal locations from base to apex. Based on the statistical atlases, differences in size (49.8%), apical direction (15.6%), basal ventricular blood pool size (13.2%), and papillary muscle shape and position (17.2%) account for the most significant modes of shape variability for the left ventricle of the C57BL/6J mice. For DBA mice, differences in left ventricular size and direction (67.4%), basal size (15.7%), and position of papillary muscles (16.8%) account for significant variability.

Identifying human disease genes through cross-species gene mapping of evolutionary conserved processes

Background

Understanding complex networks that modulate development in humans is hampered by genetic and phenotypic heterogeneity within and between populations. Here we present a method that exploits natural variation in highly diverse mouse genetic reference panels in which genetic and environmental factors can be tightly controlled. The aim of our study is to test a cross-species genetic mapping strategy, which compares data of gene mapping in human patients with functional data obtained by QTL mapping in recombinant inbred mouse strains in order to prioritize human disease candidate genes.

Methodology

We exploit evolutionary conservation of developmental phenotypes to discover gene variants that influence brain development in humans. We studied corpus callosum volume in a recombinant inbred mouse panel (C57BL/6J×DBA/2J, BXD strains) using high-field strength MRI technology. We aligned mouse mapping results for this neuro-anatomical phenotype with genetic data from patients with abnormal corpus callosum (ACC) development.

Principal Findings

From the 61 syndromes which involve an ACC, 51 human candidate genes have been identified. Through interval mapping, we identified a single significant QTL on mouse chromosome 7 for corpus callosum volume with a QTL peak located between 25.5 and 26.7 Mb. Comparing the genes in this mouse QTL region with those associated with human syndromes (involving ACC) and those covered by copy number variations (CNV) yielded a single overlap, namely HNRPU in humans and Hnrpul1 in mice. Further analysis of corpus callosum volume in BXD strains revealed that the corpus callosum was significantly larger in BXD mice with a B genotype at the Hnrpul1 locus than in BXD mice with a D genotype at Hnrpul1 (F = 22.48, p<9.87*10⁻⁵).

Conclusion

This approach that exploits highly diverse mouse strains provides an efficient and effective translational bridge to study the etiology of human developmental disorders, such as autism and schizophrenia.

A combination of atlas-based and voxel-wise approaches to analyze metabolic changes in autoradiographic data from Alzheimer's mice

Murine models are commonly used in neuroscience research to improve our knowledge of disease processes and to test drug effects. To accurately study brain glucose metabolism in these animals, ex vivo autoradiography remains the gold standard. The analysis of 3D-reconstructed autoradiographic volumes using a voxel-wise approach allows clusters of voxels representing metabolic differences between groups to be revealed. However, the spatial localization of these clusters requires careful visual identification by a neuroanatomist, a time-consuming task that is often subject to misinterpretation. Moreover, the large number of voxels to be computed in autoradiographic rodent images leads to many false positives. Here, we proposed an original automated indexation of the results of a voxel-wise approach using an MRI-based 3D digital atlas, followed by the restriction of the statistical analysis using atlas-based segmentation, thus taking advantage of the specific and complementary strengths of these two approaches. In a preliminary study of transgenic Alzheimer's mice (APP/PS1), and control littermates (PS1), we were able to achieve prompt and direct anatomical indexation of metabolic changes detected between the two groups, revealing both hypo- and hypermetabolism in the brain of APP/PS1 mice. Furthermore, statistical results were refined using atlas-based segmentation: most interesting results were obtained for the hippocampus. We thus confirmed and extended our previous results by identifying the brain structures affected in this pathological model and demonstrating modified glucose uptake in structures like the olfactory bulb. Our combined approach thus paves the way for a complete and accurate examination of functional data from cerebral structures involved in models of neurodegenerative diseases.

Automated indexation of metabolic changes in Alzheimer's mice using a voxel-wise approach combined to an MRI-based 3D digital atlas

Brain glucose uptake was examined in transgenic mice relevant to Alzheimer's disease (APP/PS1) and their control littermates (PS1). Glucose distribution in the brain of the resting animals was measured using 3D-reconstructed autoradiography and analysed by a voxel-wise approach using SPMMouse combined to an MRI-based 3D digital atlas. Prompt and direct indexation of metabolic changes between the two groups was achieved, showing both hypo- and hypermetabolism of glucose in the brain of APP/PS1 mice. We confirm and extend previous study, since we identified brain structures affected in this pathological model and demonstrate glucose uptake changes in structures like the olfactory bulb. Our results pave the way to complete and accurate examination of functional data from cerebral structures involved in models of neurodegenerative diseases.

A symmetrical Waxholm canonical mouse brain for NeuroMaps

NeuroMaps (2010) is a Web-based application that enables investigators to map data from macaque studies to a canonical atlas of the macaque brain. It currently serves as an image processor enabling them to create figures suitable for publication, presentation and archival purposes. Eventually it will enable investigators studying any of several species to analyze the overlap between their data and multimodality data mapped by others. The purpose of the current project was to incorporate the Waxholm canonical mouse brain (Harwylycz, 2009) into NeuroMaps. An enhanced gradient echo (T2*) magnetic resonance image (MRI) of the Waxholm canonical brain (Johnson et al., 2010) was warped to bring the irregular biological midplane of the MRI into line with the mathematically flat midsagittal plane of the Waxholm space. The left hemisphere was deleted and the right hemisphere reflected to produce a symmetrical 3D MR image. The symmetrical T2* image was imported into NeuroMaps. The map executing this warp was applied to four other voxellated volumes based on the same canonical specimen and maintained at the Center for In-Vitro Microscopy (CIVM): a T2-weighted MRI, a T1-weighted MRI, a segmented image and an image reconstructed from Nissl-stained histological sections of the specimen. Symmetric versions of those images were returned to the CIVM repository where they are made available to other laboratories. Utility of the symmetric atlas was demonstrated by mapping and comparing a number of cortical areas as illustrated in three conventional mouse brain atlases. The symmetric Waxholm mouse brain atlas is now accessible in NeuroMaps where investigators can map image data to standard templates over the Web and process them for publication, presentation and archival purposes: http://braininfo.rprc.washington.edu/MapViewData.aspx.

Magnetic resonance imaging-based mouse brain atlas and its applications

In this chapter, we introduce modern magnetic resonance imaging (MRI)-based mouse brain atlases. Although unable to match the resolution and specificity of their histology-based counterparts, MRI-based mouse brain atlases feature higher anatomical fidelity and can facilitate high-throughput computer-assisted analysis of certain brain phenotypes. This chapter discusses several technical aspects of MRI-based mouse brain atlases, which are important to understand the usefulness as well as limitations of existing atlases. We focus on a novel MRI technique, diffusion tensor imaging (DTI), which provides rich tissue contrasts and is uniquely suited for studying white matter structures and immature mouse brains. The chapter then demonstrates several applications of MRI-based mouse brain atlases in anatomical phenotyping and guiding stereotaxic operations.

Segmentation of small animal PET/CT mouse brain scans using an MRI-based 3D digital atlas

The work reported in this paper aimed at developing and testing an automated method to calculate the biodistribution of a specific PET tracer in mouse brain PET/CT images using an MRI-based 3D digital atlas. Surface-based registration strategy and affine transformation estimation were considered. Such an approach allowed overcoming the lack of anatomical information in the inner regions of PET/CT brain scans. Promising results were obtained in one mouse (on two scans) and will be extended to a neuroinflammation mouse model to characterize the pathology and its evolution. Major improvements are expected regarding automation, time computation, robustness and reproducibility of mouse brain segmentation. Due to its generic implementation, this method could be successfully applied to PET/CT brain scans of other species (rat, primate) for which 3D digital atlases are available.

Validation of MRI-based 3D digital atlas registration with histological and autoradiographic volumes: an anatomofunctional transgenic mouse brain imaging study

Murine models are commonly used in neuroscience to improve our knowledge of disease processes and to test drug effects. To accurately study neuroanatomy and brain function in small animals, histological staining and ex vivo autoradiography remain the gold standards to date. These analyses are classically performed by manually tracing regions of interest, which is time-consuming. For this reason, only a few 2D tissue sections are usually processed, resulting in a loss of information. We therefore proposed to match a 3D digital atlas with previously 3D-reconstructed post mortem data to automatically evaluate morphology and function in mouse brain structures. We used a freely available MRI-based 3D digital atlas derived from C57Bl/6J mouse brain scans (9.4T). The histological and autoradiographic volumes used were obtained from a preliminary study in APP(SL)/PS1(M146L) transgenic mice, models of Alzheimer's disease, and their control littermates (PS1(M146L)). We first deformed the original 3D MR images to match our experimental volumes. We then applied deformation parameters to warp the 3D digital atlas to match the data to be studied. The reliability of our method was qualitatively and quantitatively assessed by comparing atlas-based and manual segmentations in 3D. Our approach yields faster and more robust results than standard methods in the investigation of post mortem mouse data sets at the level of brain structures. It also constitutes an original method for the validation of an MRI-based atlas using histology and autoradiography as anatomical and functional references, respectively.

Remote sites of structural atrophy predict later amyloid formation in a mouse model of Alzheimer's disease

Magnetic resonance (MR) imaging can provide a longitudinal view of neurological disease through repeated imaging of patients at successive stages of impairment. Until recently, the difficulty of manual delineation has limited volumetric analyses of MR data sets to a few select regions and a small number of subjects. Increased throughput offered by faster imaging methods, automated segmentation, and deformation-based morphometry have recently been applied to overcome this limitation with mouse models of neurological conditions. We use automated analyses to produce an unbiased view of volumetric changes in a transgenic mouse model for Alzheimer's disease (AD) at two points in the progression of disease: immediately before and shortly after the onset of amyloid formation. In addition to the cortex and hippocampus, where atrophy has been well documented in AD patients, we identify volumetric losses in the pons and substantia nigra where neurodegeneration has not been carefully examined. We find that deficits in cortical volume precede amyloid formation in this mouse model, similar to presymptomatic atrophy seen in patients with familial AD. Unexpectedly, volumetric losses identified by MR outside of the forebrain predict locations of future amyloid formation, such as the inferior colliculus and spinal nuclei, which develop pathology at very late stages of disease. Our work provides proof-of-principle that MR microscopy can expand our view of AD by offering a complete and unbiased examination of volumetric changes that guide us in revisiting the canonical neuropathology.

Longitudinal characterization of brain atrophy of a Huntington's disease mouse model by automated morphological analyses of magnetic resonance images

Mouse models of human diseases play crucial roles in understanding disease mechanisms and developing therapeutic measures. Huntington's disease (HD) is characterized by striatal atrophy that begins long before the onset of motor symptoms. In symptomatic HD, striatal volumes decline predictably with disease course. Thus, imaging based volumetric measures have been proposed as outcomes for presymptomatic as well as symptomatic clinical trials of HD. Magnetic resonance imaging of the mouse brain structures is becoming widely available and has been proposed as one of the biomarkers of disease progression and drug efficacy testing. However, three-dimensional and quantitative morphological analyses of the brains are not straightforward. In this paper, we describe a tool for automated segmentation and voxel-based morphological analyses of the mouse brains. This tool was applied to a well-established mouse model of Huntington's disease, the R6/2 transgenic mouse strain. Comparison between the automated and manual segmentation results showed excellent agreement in most brain regions. The automated method was able to sensitively detect atrophy as early as 4 weeks of age and accurately follow disease progression. Comparison between ex vivo and in vivo MRI suggests that the ex vivo end-point measurement of brain morphology is also a valid approach except for the morphology of the ventricles. This is the first report of longitudinal characterization of brain atrophy in a mouse model of Huntington's disease by using automatic morphological analysis.

Micro MRI of the mouse brain using a novel 400 MHz cryogenic quadrature RF probe

The increasing number of mouse models of human disease used in biomedical research applications has led to an enhanced interest in non-invasive imaging of mice, e.g. using MRI for phenotyping. However, MRI of small rodents puts high demands on the sensitivity of data acquisition. This requirement can be addressed by using cryogenic radio-frequency (RF) detection devices. The aim of this work was to investigate the in vivo performance of a 400 MHz cryogenic transmit/receive RF probe (CryoProbe) designed for MRI of the mouse brain. To characterize this novel probe, MR data sets were acquired with both the CryoProbe and a matched conventional receive-only surface coil operating at room temperature (RT) using conventional acquisition protocols (gradient and spin echo) with identical parameter settings. Quantitative comparisons in phantom and in vivo experiments revealed gains in the signal-to-noise ratio (SNR) of 2.4 and 2.5, respectively. The increased sensitivity of the CryoProbe was invested to enhance the image quality of high resolution structural images acquired in scan times compatible with routine operation (< 45 min). In high resolution (30 x 30 x 300 microm(3)) structural images of the mouse cerebellum, anatomical details such as Purkinje cell and molecular layers could be identified. Similarly, isotropic (60 x 60 x 60 microm(3)) imaging of mouse cortical and subcortical areas revealed anatomical structures smaller than 100 microm. Finally, 3D MR angiography (52 x 80 x 80 microm(3)) of the brain vasculature enabled the detailed reconstruction of intracranial vessels (anterior and middle cerebral artery). In conclusion, this low temperature detection device represents an attractive option to increase the performance of small animal MR systems operating at 9.4 Tesla.

Magnetic resonance imaging and micro-computed tomography combined atlas of developing and adult mouse brains for stereotaxic surgery

Stereotaxic atlases of the mouse brain are important in neuroscience research for targeting of specific internal brain structures during surgical operations. The effectiveness of stereotaxic surgery depends on accurate mapping of the brain structures relative to landmarks on the skull. During postnatal development in the mouse, rapid growth-related changes in the brain occur concurrently with growth of bony plates at the cranial sutures, therefore adult mouse brain atlases cannot be used to precisely guide stereotaxis in developing brains. In this study, three-dimensional stereotaxic atlases of C57BL/6J mouse brains at six postnatal developmental stages: postnatal day (P) 7, P14, P21, P28, P63 and in adults (P140-P160) were developed, using diffusion tensor imaging (DTI) and micro-computed tomography (CT). At present, most widely-used stereotaxic atlases of the mouse brain are based on histology, but the anatomical fidelity of ex vivo atlases to in vivo mouse brains has not been evaluated previously. To account for ex vivo tissue distortion due to fixation as well as individual variability in the brain, we developed a population-averaged in vivo magnetic resonance imaging adult mouse brain stereotaxic atlas, and a distortion-corrected DTI atlas was generated by nonlinearly warping ex vivo data to the population-averaged in vivo atlas. These atlas resources were developed and made available through a new software user-interface with the objective of improving the accuracy of targeting brain structures during stereotaxic surgery in developing and adult C57BL/6J mouse brains.

Genetic dissection of the mouse CNS using magnetic resonance microscopy

PURPOSE OF REVIEW

Advances in magnetic resonance microscopy (MRM) make it practical to map gene variants responsible for structural variation in brains of many species, including mice and humans. We review results of a systematic genetic analysis of MRM data using as a case study a family of well characterized lines of mice.

RECENT ADVANCES

MRM has matured to the point that we can generate high contrast, high-resolution images even for species as small as a mouse, with a brain merely 1/3000th the size of humans. We generated 21.5-micron data sets for a diverse panel of BXD mouse strains to gauge the extent of genetic variation, and as a prelude to comprehensive genetic and genomic analyses. Here we review MRM capabilities and image segmentation methods; heritability of brain variation; covariation of the sizes of brain regions; and correlations between MRM and classical histological data sets.

SUMMARY

The combination of high throughput MRM and genomics will improve our understanding of the genetic basis of structure-function correlations. Sophisticated mouse models will be critical in converting correlations into mechanisms and in determining genetic and epigenetic causes of differences in disease susceptibility.

Genetic dissection of the mouse brain using high-field magnetic resonance microscopy

Magnetic resonance (MR) imaging has demonstrated that variation in brain structure is associated with differences in behavior and disease state. However, it has rarely been practical to prospectively test causal models that link anatomical and functional differences in humans. In the present study we have combined classical mouse genetics with high-field MR to systematically explore and test such structure-functional relations across multiple brain regions. We segmented 33 regions in two parental strains-C57BL/6J (B) and DBA/2J (D)-and in nine BXD recombinant inbred strains. All strains have been studied extensively for more than 20 years using a battery of genetic, functional, anatomical, and behavioral assays. We compared levels of variation within and between strains and sexes, by region, and by system. Average within-strain variation had a coefficient of variation (CV) of 1.6% for the whole brain; while the CV ranged from 2.3 to 3.6% for olfactory bulbs, cortex and cerebellum, and up to approximately 18% for septum and laterodorsal thalamic nucleus. Variation among strain averages ranged from 6.7% for cerebellum, 7.6% for whole brain, 9.0% for cortex, up to approximately 26% for the ventricles, laterodorsal thalamic nucleus, and the interpeduncular nucleus. Heritabilities averaged 0.60+/-0.18. Sex differences were not significant with the possible (and unexpected) exception of the pons ( approximately 20% larger in males). A correlation matrix of regional volumes revealed high correlations among functionally related parts of the CNS (e.g., components of the limbic system), and several high correlations between regions that are not anatomically connected, but that may nonetheless be functionally or genetically coupled.

Automatic cropping of MRI rat brain volumes using pulse coupled neural networks

The Pulse Coupled Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this paper we show the use of the PCNN as an image segmentation strategy to crop MR images of rat brain volumes. We then show the use of the associated PCNN image 'signature' to automate the brain cropping process with a trained artificial neural network. We tested this novel algorithm on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes. The datasets included 40 ms, 48 ms and 53 ms effective TEs, acquisition field strengths of 4.7 T and 9.4 T, image resolutions from 64x64 to 256x256, slice locations ranging from +6 mm to -11 mm AP, two different surface coil manufacturers and imaging protocols. The results were compared against manually segmented gold standards and Brain Extraction Tool (BET) V2.1 results. The Jaccard similarity index was used for numerical evaluation of the proposed algorithm. Our novel PCNN cropping system averaged 0.93 compared to BET scores circa 0.84.

Performance measure characterization for evaluating neuroimage segmentation algorithms

Characterizing the performance of segmentation algorithms in brain images has been a persistent challenge due to the complexity of neuroanatomical structures, the quality of imagery and the requirement of accurate segmentation. There has been much interest in using the Jaccard and Dice similarity coefficients associated with Sensitivity and Specificity for evaluating the performance of segmentation algorithms. This paper addresses the essential characteristics of the fundamental performance measure coefficients adopted in evaluation frameworks. While exploring the properties of the Jaccard, Dice and Specificity coefficients, we propose new measure coefficients Conformity and Sensibility for evaluating image segmentation techniques. It is indicated that Conformity is more sensitive and rigorous than Jaccard and Dice in that it has better discrimination capabilities in detecting small variations in segmented images. Comparing to Specificity, Sensibility provides consistent and reliable evaluation scores without the incorporation of image background properties. The merits of the proposed coefficients are illustrated by extracting neuroanatomical structures in a wide variety of brain images using various segmentation techniques.

High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice

Detailed anatomical atlases can provide considerable interpretive power in studies of both human and rodent neuroanatomy. Here we describe a three-dimensional atlas of the mouse brain, manually segmented into 62 structures, based on an average of 32 mum isotropic resolution T(2)-weighted, within skull images of forty 12 week old C57Bl/6J mice, scanned on a 7 T scanner. Individual scans were normalized, registered, and averaged into one volume. Structures within the cerebrum, cerebellum, and brainstem were painted on each slice of the average MR image while using simultaneous viewing of the coronal, sagittal and horizontal orientations. The final product, which will be freely available to the research community, provides the most detailed MR-based, three-dimensional neuroanatomical atlas of the whole brain yet created. The atlas is furthermore accompanied by ancillary detailed descriptions of boundaries for each structure and provides high quality neuroanatomical details pertinent to MR studies using mouse models in research.