Cell specificity of Manganese-enhanced MRI signal in the cerebellum.

Magnetic Resonance Imaging (MRI) resolution continues to improve, making it important to understand the cellular basis for different MRI contrast mechanisms. Manganese-enhanced MRI (MEMRI) produces layer-specific contrast throughout the brain enabling in vivo visualization of cellular cytoarchitecture, particularly in the cerebellum. Due to the unique geometry of the cerebellum, especially near the midline, 2D MEMRI images can be acquired from a relatively thick slice by averaging through areas of uniform morphology and cytoarchitecture to produce very high-resolution visualization of sagittal planes. In such images, MEMRI hyperintensity is uniform in thickness throughout the anterior-posterior axis of sagittal sections and is centrally located in the cerebellar cortex. These signal features suggested that the Purkinje cell layer, which houses the cell bodies of the Purkinje cells and the Bergmann glia, is the source of hyperintensity. Despite this circumstantial evidence, the cellular source of MRI contrast has been difficult to define. In this study, we quantified the effects of selective ablation of Purkinje cells or Bergmann glia on cerebellar MEMRI signal to determine whether signal could be assigned to one cell type. We found that the Purkinje cells, not the Bergmann glia, are the primary of source of the enhancement in the Purkinje cell layer. This cell-ablation strategy should be useful for determining the cell specificity of other MRI contrast mechanisms.

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.

Longitudinal in Utero Analysis of Engrailed-1 Knockout Mouse Embryonic Phenotypes Using High-Frequency Ultrasound.

Large-scale international efforts to generate and analyze loss-of-function mutations in each of the approximately 20,000 protein-encoding gene mutations are ongoing using the "knockout" mouse as a model organism. Because one-third of gene knockouts are expected to result in embryonic lethality, it is important to develop non-invasive in utero imaging methods to detect and monitor mutant phenotypes in mouse embryos. We describe the utility of 3-D high-frequency (40-MHz) ultrasound (HFU) for longitudinal in utero imaging of mouse embryos between embryonic days (E) 11.5 and E14.5, which represent critical stages of brain and organ development. Engrailed-1 knockout (En1-ko) mouse embryos and their normal control littermates were imaged with HFU in 3-D, enabling visualization of morphological phenotypes in the developing brains, limbs and heads of the En1-ko embryos. Recently developed deep learning approaches were used to automatically segment the embryonic brain ventricles and bodies from the 3-D HFU images, allowing quantitative volumetric analyses of the En1-ko brain phenotypes. Taken together, these results show great promise for the application of longitudinal 3-D HFU to analyze knockout mouse embryos in utero.

A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos.

Segmentation and mutant classification of high-frequency ultrasound (HFU) mouse embryo brain ventricle (BV) and body images can provide valuable information for developmental biologists. However, manual segmentation and identification of BV and body requires substantial time and expertise. This article proposes an accurate, efficient and explainable deep learning pipeline for automatic segmentation and classification of the BV and body. For segmentation, a two-stage framework is implemented. The first stage produces a low-resolution segmentation map, which is then used to crop a region of interest (ROI) around the target object and serve as the probability map of the autocontext input for the second-stage fine-resolution refinement network. The segmentation then becomes tractable on high-resolution 3-D images without time-consuming sliding windows. The proposed segmentation method significantly reduces inference time (102.36-0.09 s/volume ≈ 1000× faster) while maintaining high accuracy comparable to previous sliding-window approaches. Based on the BV and body segmentation map, a volumetric convolutional neural network (CNN) is trained to perform a mutant classification task. Through backpropagating the gradients of the predictions to the input BV and body segmentation map, the trained classifier is found to largely focus on the region where the Engrailed-1 (En1) mutation phenotype is known to manifest itself. This suggests that gradient backpropagation of deep learning classifiers may provide a powerful tool for automatically detecting unknown phenotypes associated with a known genetic mutation.

CSF1R inhibition depletes tumor-associated macrophages and attenuates tumor progression in a mouse sonic Hedgehog-Medulloblastoma model.

The immune microenvironment of tumors can play a critical role in promoting or inhibiting tumor progression depending on the context. We present evidence that tumor-associated macrophages/microglia (TAMs) can promote tumor progression in the sonic hedgehog subgroup of medulloblastoma (SHH-MB). By combining longitudinal manganese-enhanced magnetic resonance imaging (MEMRI) and immune profiling of a sporadic mouse model of SHH-MB, we found the density of TAMs is higher in the ~50% of tumors that progress to lethal disease. Furthermore, reducing regulatory T cells or eliminating B and T cells in Rag1 mutants does not alter SHH-MB tumor progression. As TAMs are a dominant immune component in tumors and are normally dependent on colony-stimulating factor 1 receptor (CSF1R), we treated mice with a CSF1R inhibitor, PLX5622. Significantly, PLX5622 reduces a subset of TAMs, prolongs mouse survival, and reduces the volume of most tumors within 4 weeks of treatment. Moreover, concomitant with a reduction in TAMs the percentage of infiltrating cytotoxic T cells is increased, indicating a change in the tumor environment. Our studies in an immunocompetent preclinical mouse model demonstrate TAMs can have a functional role in promoting SHH-MB progression. Thus, CSF1R inhibition could have therapeutic potential for a subset of SHH-MB patients.

DEEP MOUSE: AN END-TO-END AUTO-CONTEXT REFINEMENT FRAMEWORK FOR BRAIN VENTRICLE & BODY SEGMENTATION IN EMBRYONIC MICE ULTRASOUND VOLUMES.

The segmentation of the brain ventricle (BV) and body in embryonic mice high-frequency ultrasound (HFU) volumes can provide useful information for biological researchers. However, manual segmentation of the BV and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume ≈1000x faster) while slightly improves the segmentation accuracy over the previous methods using slide-window approaches.

Longitudinal MEMRI analysis of brain phenotypes in a mouse model of Niemann-Pick Type C disease.

Niemann-Pick Type C (NPC) is a rare genetic disorder characterized by progressive cell death in various tissues, particularly in the cerebellar Purkinje cells, with no known cure. Mouse models for human NPC have been generated and characterized histologically, behaviorally, and using longitudinal magnetic resonance imaging (MRI). Previous imaging studies revealed significant brain volume differences between mutant and wild-type animals, but stopped short of making volumetric comparisons of the cerebellar sub-regions. In this study, we present longitudinal manganese-enhanced MRI (MEMRI) data from cohorts of wild-type, heterozygote carrier, and homozygote mutant NPC mice, as well as deformation-based morphometry (DBM) driven brain volume comparisons across genotypes, including the cerebellar cortex, white matter, and nuclei. We also present the first comparisons of MEMRI signal intensities, reflecting brain and cerebellum sub-regional Mn2+-uptake over time and across genotypes.

MEMRI-based imaging pipeline for guiding preclinical studies in mouse models of sporadic medulloblastoma.

PURPOSE: Genetically engineered mouse models of sporadic cancers are critical for studying tumor biology and for preclinical testing of therapeutics. We present an MRI-based pipeline designed to produce high resolution, quantitative information about tumor progression and response to novel therapies in mouse models of medulloblastoma (MB). METHODS: Sporadic MB was modeled in mice by inducing expression of an activated form of the Smoothened gene (aSmo) in a small number of cerebellar granule cell precursors. aSmo mice were imaged and analyzed at defined time-points using a 3D manganese-enhanced MRI-based pipeline optimized for high-throughput. RESULTS: A semi-automated segmentation protocol was established that estimates tumor volume in a time-frame compatible with a high-throughput pipeline. Both an empirical, volume-based classifier and a linear discriminant analysis-based classifier were tested to distinguish progressing from nonprogressing lesions at early stages of tumorigenesis. Tumor centroids measured at early stages revealed that there is a very specific location of the probable origin of the aSmo MB tumors. The efficacy of the manganese-enhanced MRI pipeline was demonstrated with a small-scale experimental drug trial designed to reduce the number of tumor associated macrophages and microglia. CONCLUSION: Our results revealed a high level of heterogeneity between tumors within and between aSmo MB models, indicating that meaningful studies of sporadic tumor progression and response to therapy could not be conducted without an imaging-based pipeline approach.

Cerebellar folding is initiated by mechanical constraints on a fluid-like layer without a cellular pre-pattern.

Models based in differential expansion of elastic material, axonal constraints, directed growth, or multi-phasic combinations have been proposed to explain brain folding. However, the cellular and physical processes present during folding have not been defined. We used the murine cerebellum to challenge folding models with in vivo data. We show that at folding initiation differential expansion is created by the outer layer of proliferating progenitors expanding faster than the core. However, the stiffness differential, compressive forces, and emergent thickness variations required by elastic material models are not present. We find that folding occurs without an obvious cellular pre-pattern, that the outer layer expansion is uniform and fluid-like, and that the cerebellum is under radial and circumferential constraints. Lastly, we find that a multi-phase model incorporating differential expansion of a fluid outer layer and radial and circumferential constraints approximates the in vivo shape evolution observed during initiation of cerebellar folding.

Mouse MRI shows brain areas relatively larger in males emerge before those larger in females.

Sex differences exist in behaviors, disease and neuropsychiatric disorders. Sexual dimorphisms however, have yet to be studied across the whole brain and across a comprehensive time course of postnatal development. Here, we use manganese-enhanced MRI (MEMRI) to longitudinally image male and female C57BL/6J mice across 9 time points, beginning at postnatal day 3. We recapitulate findings on canonically dimorphic areas, demonstrating MEMRI's ability to study neuroanatomical sex differences. We discover, upon whole-brain volume correction, that neuroanatomical regions larger in males develop earlier than those larger in females. Groups of areas with shared sexually dimorphic developmental trajectories reflect behavioral and functional networks, and expression of genes involved with sex processes. Also, post-pubertal neuroanatomy is highly individualized, and individualization occurs earlier in males. Our results demonstrate the ability of MEMRI to reveal comprehensive developmental differences between male and female brains, which will improve our understanding of sex-specific predispositions to various neuropsychiatric disorders.

Lateral cerebellum is preferentially sensitive to high sonic hedgehog signaling and medulloblastoma formation.

The main cell of origin of the Sonic hedgehog (SHH) subgroup of medulloblastoma (MB) is granule cell precursors (GCPs), a SHH-dependent transient amplifying population in the developing cerebellum. SHH-MBs can be further subdivided based on molecular and clinical parameters, as well as location because SHH-MBs occur preferentially in the lateral cerebellum (hemispheres). Our analysis of adult patient data suggests that tumors with Smoothened (SMO) mutations form more specifically in the hemispheres than those with Patched 1 (PTCH1) mutations. Using sporadic mouse models of SHH-MB with the two mutations commonly seen in adult MB, constitutive activation of Smo (SmoM2) or loss-of-Ptch1, we found that regardless of timing of induction or type of mutation, tumors developed primarily in the hemispheres, with SmoM2-mutants indeed showing a stronger specificity. We further uncovered that GCPs in the hemispheres are more susceptible to high-level SHH signaling compared with GCPs in the medial cerebellum (vermis), as more SmoM2 or Ptch1-mutant hemisphere cells remain undifferentiated and show increased tumorigenicity when transplanted. Finally, we identified location-specific GCP gene-expression profiles, and found that deletion of the genes most highly expressed in the hemispheres (Nr2f2) or vermis (Engrailed1) showed opposing effects on GCP differentiation. Our studies thus provide insights into intrinsic differences within GCPs that impact on SHH-MB progression.

In Utero MRI of Mouse Embryos.

Genetically engineered mouse models are used extensively as models of human development and developmental diseases. Conventional histological approaches are static and two-dimensional, and do not provide a full understanding of the dynamic, spatiotemporal changes in developing mouse embryos. Magnetic resonance imaging (MRI) offers a noninvasive and longitudinal approach for three-dimensional in utero imaging of normal and mutant mouse embryos. In this chapter, we describe MRI approaches that have been developed for imaging the living embryonic mouse brain and vasculature. Details are provided on the animal preparation and setup, MRI equipment, acquisition and reconstruction methods that have been found to be most useful for in utero MRI, including examples of applications to fetal mouse neuroimaging.

AUTOMATIC BODY LOCALIZATION AND BRAIN VENTRICLE SEGMENTATION IN 3D HIGH FREQUENCY ULTRASOUND IMAGES OF MOUSE EMBRYOS.

This paper presents a fully automatic segmentation system for whole-body high-frequency ultrasound (HFU) images of mouse embryos that can simultaneously segment the body contour and the brain ventricles (BVs). Our system first locates a region of interest (ROI), which covers the interior of the uterus, by sub-surface analysis. Then, it segments the ROI into BVs, the body, the amniotic fluid, and the uterine wall, using nested graph cut. Simultaneously multilevel thresholding is applied to the whole-body image to propose candidate BV components. These candidates are further truncated by the embryo mask (body+BVs) to refine the BV candidates. Finally, subsets of all candidate BVs are compared with pre-trained spring models describing valid BV structures, to identify true BV components. The system can segment the body accurately in most cases based on visual inspection, and achieves average Dice similarity coefficient of 0.8924 ± 0.043 for the BVs on 36 HFU image volumes.

DEEP BV: A FULLY AUTOMATED SYSTEM FOR BRAIN VENTRICLE LOCALIZATION AND SEGMENTATION IN 3D ULTRASOUND IMAGES OF EMBRYONIC MICE.

Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from HFU volumes is tedious, time-consuming, and requires specialized expertise. In this paper, we propose a novel deep learning based BV segmentation system for whole-body HFU images of mouse embryos. Our fully automated system consists of two modules: localization and segmentation. It first applies a volumetric convolutional neural network on a 3D sliding window over the entire volume to identify a 3D bounding box containing the entire BV. It then employs a fully convolutional network to segment the detected bounding box into BV and background. The system achieves a Dice Similarity Coefficient (DSC) of 0.8956 for BV segmentation on an unseen 111 HFU volume test set surpassing the previous state-of-the-art method (DSC of 0.7119) by a margin of 25%.

High-speed, high-frequency ultrasound, in utero vector-flow imaging of mouse embryos.

Real-time imaging of the embryonic murine cardiovascular system is challenging due to the small size of the mouse embryo and rapid heart rate. High-frequency, linear-array ultrasound systems designed for small-animal imaging provide high-frame-rate and Doppler modes but are limited in regards to the field of view that can be imaged at fine-temporal and -spatial resolution. Here, a plane-wave imaging method was used to obtain high-speed image data from in utero mouse embryos and multi-angle, vector-flow algorithms were applied to the data to provide information on blood flow patterns in major organs. An 18-MHz linear array was used to acquire plane-wave data at absolute frame rates ≥10 kHz using a set of fixed transmission angles. After beamforming, vector-flow processing and image compounding, effective frame rates were on the order of 2 kHz. Data were acquired from the embryonic liver, heart and umbilical cord. Vector-flow results clearly revealed the complex nature of blood-flow patterns in the embryo with fine-temporal and -spatial resolution.

Multimodal Genetic Approach for Molecular Imaging of Vasculature in a Mouse Model of Melanoma.

PURPOSE: In this study, we evaluated a genetic approach for in vivo multimodal molecular imaging of vasculature in a mouse model of melanoma. PROCEDURES: We used a novel transgenic mouse, Ts-Biotag, that genetically biotinylates vascular endothelial cells. After inoculating these mice with B16 melanoma cells, we selectively targeted endothelial cells with (strept)avidinated contrast agents to achieve multimodal contrast enhancement of Tie2-expressing blood vessels during tumor progression. RESULTS: This genetic targeting system provided selective labeling of tumor vasculature and showed in vivo binding of avidinated probes with high specificity and sensitivity using microscopy, near infrared, ultrasound, and magnetic resonance imaging. We further demonstrated the feasibility of conducting longitudinal three-dimensional (3D) targeted imaging studies to dynamically assess changes in vascular Tie2 from early to advanced tumor stages. CONCLUSIONS: Our results validated the Ts-Biotag mouse as a multimodal targeted imaging system with the potential to provide spatio-temporal information about dynamic changes in vasculature during tumor progression.

A Mathematical Model of Granule Cell Generation During Mouse Cerebellum Development.

Determining the cellular basis of brain growth is an important problem in developmental neurobiology. In the mammalian brain, the cerebellum is particularly amenable to studies of growth because it contains only a few cell types, including the granule cells, which are the most numerous neuronal subtype. Furthermore, in the mouse cerebellum granule cells are generated from granule cell precursors (gcps) in the external granule layer (EGL), from 1 day before birth until about 2 weeks of age. The complexity of the underlying cellular processes (multiple cell behaviors, three spatial dimensions, time-dependent changes) requires a quantitative framework to be fully understood. In this paper, a differential equation-based model is presented, which can be used to estimate temporal changes in granule cell numbers in the EGL. The model includes the proliferation of gcps and their differentiation into granule cells, as well as the process by which granule cells leave the EGL. Parameters describing these biological processes were derived from fitting the model to histological data. This mathematical model should be useful for understanding altered gcp and granule cell behaviors in mouse mutants with abnormal cerebellar development and cerebellar cancers.

Cardiovascular Imaging in Mice.

The mouse is the mammalian model of choice for investigating cardiovascular biology, given our ability to manipulate it by genetic, pharmacologic, mechanical, and environmental means. Imaging is an important approach to phenotyping both function and structure of cardiac and vascular components. This review details commonly used imaging approaches, with a focus on echocardiography and magnetic resonance imaging and brief overviews of other imaging modalities. We also briefly outline emerging imaging approaches but caution that reliability and validity data may be lacking.

4D MEMRI atlas of neonatal FVB/N mouse brain development.

The widespread use of the mouse as a model system to study brain development has created the need for noninvasive neuroimaging methods that can be applied to early postnatal mice. The goal of this study was to optimize in vivo three- (3D) and four-dimensional (4D) manganese (Mn)-enhanced MRI (MEMRI) approaches for acquiring and analyzing data from the developing mouse brain. The combination of custom, stage-dependent holders and self-gated (motion-correcting) 3D MRI sequences enabled the acquisition of high-resolution (100-µm isotropic), motion artifact-free brain images with a high level of contrast due to Mn-enhancement of numerous brain regions and nuclei. We acquired high-quality longitudinal brain images from two groups of FVB/N strain mice, six mice per group, each mouse imaged on alternate odd or even days (6 3D MEMRI images at each day) covering the developmental stages between postnatal days 1 to 11. The effects of Mn-exposure, anesthesia and MRI were assessed, showing small but significant transient effects on body weight and brain volume, which recovered with time and did not result in significant morphological differences when compared to controls. Metrics derived from deformation-based morphometry (DBM) were used for quantitative analysis of changes in volume and position of a number of brain regions. The cerebellum, a brain region undergoing significant changes in size and patterning at early postnatal stages, was analyzed in detail to demonstrate the spatiotemporal characterization made possible by this new atlas of mouse brain development. These results show that MEMRI is a powerful tool for quantitative analysis of mouse brain development, with great potential for in vivo phenotype analysis in mouse models of neurodevelopmental diseases.

3D mapping of neuronal migration in the embryonic mouse brain with magnetic resonance microimaging.

A prominent feature of the developing mammalian brain is the widespread migration of neural progenitor (NP) cells during embryogenesis. A striking example is provided by NP cells born in the ventral forebrain of mid-gestation stage mice, which subsequently migrate long distances to their final positions in the cortex and olfactory bulb. Previous studies have used two-dimensional histological methods, making it difficult to analyze three-dimensional (3D) migration patterns. Unlike histology, magnetic resonance microimaging (micro-MRI) is a non-destructive, quantitative and inherently 3D imaging method for analyzing mouse embryos. To allow mapping of migrating NP cells with micro-MRI, cells were labeled in situ in the medial (MGE) and lateral (LGE) ganglionic eminences, using targeted in utero ultrasound-guided injection of micron-sized particles of iron-oxide (MPIO). Ex vivo micro-MRI and histology were then performed 5-6days after injection, demonstrating that the MPIO had magnetically labeled the migrating NP populations, which enabled 3D visualization and automated segmentation of the labeled cells. This approach was used to analyze the distinct patterns of migration from the MGE and LGE, and to construct rostral-caudal migration maps from each progenitor region. Furthermore, abnormal migratory phenotypes were observed in Nkx2.1(-/-) embryos, most notably a significant increase in cortical neurons derived from the Nkx2.1(-/-) LGE. Taken together, these results demonstrate that MPIO labeling and micro-MRI provide an efficient and powerful approach for analyzing 3D cell migration patterns in the normal and mutant mouse embryonic brain.