Optical Clearing of the Mouse Central Nervous System Using Passive CLARITY

Traditionally, tissue visualization has required that the tissue of interest be serially sectioned and imaged, subjecting each tissue section to unique ...

University of California, Los Angeles

Strains of mice, through breeding or the disruption of normal genetic pathways, are widely used to model human diseases. Atlases are an invaluable aid in understanding the impact of such manipulations by providing a standard for comparison. We have developed a digital atlas of the adult C57BL/6J mouse brain as a comprehensive framework for storing and accessing the myriad types of information about the mouse brain. Our implementation was constructed using several different imaging techniques: magnetic resonance microscopy, blockface imaging, classical histology and immunohistochemistry. Along with raw and annotated images, it contains database management systems and a set of tools for comparing information from different techniques. The framework allows facile correlation of results from different animals, investigators or laboratories by establishing a canonical representation of the mouse brain and providing the tools for the insertion of independent data into the same space as the atlas. This tool will aid in managing the increasingly complex and voluminous amounts of information about the mammalian brain. It provides a framework that encompasses genetic information in the context of anatomical imaging and holds tremendous promise for producing new insights into the relationship between genotype and phenotype. We describe a suite of tools that enables the independent entry of other types of data, facile retrieval of information and straightforward display of images. Thus, the atlas becomes a framework for managing complex genetic and epigenetic information about the mouse brain. The atlas and associated tools may be accessed at http://www.loni.ucla.edu/MAP.

A multimodal, multidimensional atlas of the C57BL/6J mouse brain.

The Mouse Atlas Project (MAP) aims to produce a framework for organizing and analyzing the large volumes of neuroscientific data produced by the proliferation of genetically modified animals. Atlases provide an invaluable aid in understanding the impact of genetic manipulations by providing a standard for comparison. We use a digital atlas as the hub of an informatics network, correlating imaging data, such as structural imaging and histology, with text-based data, such as nomenclature, connections, and references. We generated brain volumes using magnetic resonance microscopy (MRM), classical histology, and immunohistochemistry, and registered them into a common and defined coordinate system. Specially designed viewers were developed in order to visualize multiple datasets simultaneously and to coordinate between textual and image data. Researchers can navigate through the brain interchangeably, in either a text-based or image-based representation that automatically updates information as they move. The atlas also allows the independent entry of other types of data, the facile retrieval of information, and the straight-forward display of images. In conjunction with centralized servers, image and text data can be kept current and can decrease the burden on individual researchers' computers. A comprehensive framework that encompasses many forms of information in the context of anatomic imaging holds tremendous promise for producing new insights. The atlas and associated tools can be found at http://www.loni.ucla.edu/MAP.

The informatics of a C57BL/6J mouse brain atlas.

Over the past decade, the use of informatics to solve complex neuroscientific problems has increased dramatically. Many of these research endeavors involve examining large amounts of imaging, behavioral, genetic, neurobiological, and neuropsychiatric data. Superimposing, processing, visualizing, or interpreting such a complex cohort of datasets frequently becomes a challenge. We developed a new software environment that allows investigators to integrate multimodal imaging data, hierarchical brain ontology systems, on-line genetic and phylogenic databases, and 3D virtual data reconstruction models. The Laboratory of Neuro Imaging visualization environment (LONI Viz) consists of the following components: a sectional viewer for imaging data, an interactive 3D display for surface and volume rendering of imaging data, a brain ontology viewer, and an external database query system. The synchronization of all components according to stereotaxic coordinates, region name, hierarchical ontology, and genetic labels is achieved via a comprehensive BrainMapper functionality, which directly maps between position, structure name, database, and functional connectivity information. This environment is freely available, portable, and extensible, and may prove very useful for neurobiologists, neurogenetisists, brain mappers, and for other clinical, pedagogical, and research endeavors.

LONI visualization environment.

Brain atrophy measured by MRI is an important correlate with clinical disability and disease duration in multiple sclerosis (MS). Unfortunately, neuropathologic mechanisms which lead to this grey matter atrophy remain unknown. The objective of this study was to determine whether brain atrophy occurs in the mouse model, experimental autoimmune encephalomyelitis (EAE). Postmortem high-resolution T2-weighted magnetic resonance microscopy (MRM) images from 32 mouse brains (21 EAE and 11 control) were collected. A minimum deformation atlas was constructed and a deformable atlas approach was used to quantify volumetric changes in neuroanatomical structures. A significant decrease in the mean cerebellar cortex volume in mice with late EAE (48-56 days after disease induction) as compared to normal strain, gender, and age-matched controls was observed. There was a direct correlation between cerebellar cortical atrophy and disease duration. At an early time point in disease, 15 days after disease induction, cerebellar white matter lesions were detected by both histology and MRM. These data demonstrate that myelin-specific autoimmune responses can lead to grey matter atrophy in an otherwise normal CNS. The model described herein can now be used to investigate neuropathologic mechanisms that lead to the development of gray matter atrophy in this setting.

Cerebellar cortical atrophy in experimental autoimmune encephalomyelitis.

Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectation-maximization (EM) algorithm, if a principled initialization is not available. In this paper, we propose a new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms. Our specific contributions are two-fold: 1) we propose to use blended crossover in order to reduce the premature convergence problem to its minimum and 2) we introduce a completely new permutation operator specifically meant for the FMM parameter estimation. In addition to improving the optimization results, the permutation operator allows for imposing biologically meaningful constraints to the FMM parameter values. We also introduce a hybrid of the genetic algorithm and the EM algorithm for efficient solution of multidimensional FMM fitting problems. We compare our algorithm to the self-annealing EM-algorithm and a standard real coded genetic algorithm with the voxel classification tasks within the brain imaging. The algorithms are tested on synthetic data as well as real three-dimensional image data from human magnetic resonance imaging, positron emission tomography, and mouse brain MRI. The tissue classification results by our method are shown to be consistently more reliable and accurate than with the competing parameter estimation methods.

Genetic algorithms for finite mixture model based voxel classification in neuroimaging.

Naturally occurring mutants and genetically manipulated strains of mice are widely used to model a variety of human diseases. Atlases are an invaluable aid in understanding the impact of such manipulations by providing a standard for comparison and to facilitate the integration of anatomic, genetic, and physiologic observations from multiple subjects and experiments. We have developed digital atlases of the C57BL/6J mouse brain (adult and neonate) as comprehensive frameworks for storing and accessing the myriad types of information about the mouse brain. Along with raw and annotated images, these contain database management systems and a set of tools for comparing information from different techniques and different animals. Each atlas establishes a canonical representation of the mouse brain and provides the tools for the manipulation and analysis of new data. We describe both these atlases and discuss how they may be put to use in organizing and analyzing data from mouse models of epilepsy.

Multimodal, multidimensional models of mouse brain.

Quantifying the effect of a genetic manipulation or disease is a complicated process in a population of animals. Probabilistic brain atlases can capture population variability and be used to quantify those variations in anatomy as measured by structural imaging. Minimum deformation atlases (MDAs), a subclass of probabilistic atlases, are intensity-based averages of a collection of scans in a common space unbiased by selection of a single target image. Here, we describe a method for generating an MDA from a set of magnetic resonance microscopy images. First, the images are segmented to remove any non-brain tissue and bias field corrected to remove field inhomogeneities. The corrected images are then linearly aligned to a representative scan, the geometric mean of all the transformations is calculated, and a minimum deformation target (MDT) is produced by averaging the volumes in this new space. The brains are then non-linearly aligned to the MDT to produce the MDA. Finally, the images are linearly aligned to the MDA using a full-affine transformation to spatially and intensity normalize them, removing global differences in size, shape, and position but retaining anatomically significant differences.

Brain atlases and neuroanatomic imaging.

Provenance, the description of the history of a set of data, has grown more important with the proliferation of research consortia-related efforts in neuroimaging. Knowledge about the origin and history of an image is crucial for establishing data and results quality; detailed information about how it was processed, including the specific software routines and operating systems that were used, is necessary for proper interpretation, high fidelity replication and re-use. We have drafted a mechanism for describing provenance in a simple and easy to use environment, alleviating the burden of documentation from the user while still providing a rich description of an image's provenance. This combination of ease of use and highly descriptive metadata should greatly facilitate the collection of provenance and subsequent sharing of data.

Provenance in neuroimaging.

Gray matter atrophy observed by brain MRI is an important correlate to clinical disability and disease duration in multiple sclerosis. The objective of this study was to link brain atrophy visualized by neuroimaging to its underlying neuropathology using the MS model, experimental autoimmune encephalomyelitis (EAE). Volumetric changes in brains of EAE mice, as well as matched healthy normal controls, were quantified by collecting post-mortem high-resolution T2-weighted magnetic resonance microscopy and actively stained magnetic resonance histology images. Anatomical delineations demonstrated a significant decrease in the volume of the whole cerebellum, cerebellar cortex, and molecular layer of the cerebellar cortex in EAE as compared to normal controls. The pro-apoptotic marker caspase-3 was detected in Purkinje cells and a significant decrease in Purkinje cell number was found in EAE. Cross modality and temporal correlations revealed a significant association between Purkinje cell loss on neuropathology and atrophy of the molecular layer of the cerebellar cortex by neuroimaging. These results demonstrate the power of using combined population atlasing and neuropathology approaches to discern novel insights underlying gray matter atrophy in animal models of neurodegenerative disease.

Purkinje cell loss in experimental autoimmune encephalomyelitis.

The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols (Rex et al., 2003). It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides three layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available online (http://Pipeline.loni.ucla.edu).

Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline.

There are strong correlations between cortical atrophy observed by MRI and clinical disability and disease duration in multiple sclerosis (MS). The objective of this study was to evaluate the progression of cortical atrophy over time in vivo in experimental autoimmune encephalomyelitis (EAE), the most commonly used animal model for MS. Volumetric changes in brains of EAE mice and matched healthy controls were quantified by collecting high-resolution T2-weighted magnetic resonance images in vivo and labeling anatomical structures on the images. In vivo scanning permitted us to evaluate brain structure volumes in individual animals over time and we observed that though brain atrophy progressed differently in each individual animal, all mice with EAE demonstrated significant atrophy in whole brain, cerebral cortex, and whole cerebellum compared to normal controls. Furthermore, we found a strong correlation between cerebellar atrophy and cumulative disease score in mice with EAE. Ex vivo MRI showed a significant decrease in brain and cerebellar volume and a trend that did not reach significance in cerebral cortex volume in mice with EAE compared to controls. Cross modality correlations revealed a significant association between neuronal loss on neuropathology and in vivo atrophy of the cerebral cortex by neuroimaging. These results demonstrate that longitudinal in vivo imaging is more sensitive to changes that occur in neurodegenerative disease models than cross-sectional ex vivo imaging. This is the first report of progressive cortical atrophy in vivo in a mouse model of MS.

Cortical atrophy in experimental autoimmune encephalomyelitis: in vivo imaging.

Gray matter atrophy is an important correlate to clinical disability in multiple sclerosis (MS), and many treatment trials include atrophy as an outcome measure. Atrophy has been shown to occur in experimental autoimmune encephalomyelitis (EAE), the most commonly used animal model of MS. The clinical severity of EAE is reduced in estrogen-reated mice, but it remains unknown whether estrogen treatment can reduce gray matter atrophy in EAE. In this study, mice with EAE were treated with either estrogen receptor (ER)-α ligand or ER-β ligand, and diffusion tensor images (DTI) were collected and neuropathology was performed. DTI showed atrophy in the cerebellar gray matter of vehicle-treated EAE mice compared with healthy controls but not in ER-α or ER-β ligand-treated EAE mice. Neuropathology demonstrated that Purkinje cell numbers were decreased in vehicle-treated EAE mice, whereas neither ER ligand-treated EAE groups showed a decrease. This is the first report of a neuroprotective therapy in EAE that unambiguously prevents gray matter atrophy while sparing a major neuronal cell type. Fractional anisotropy (FA) in the cerebellar white matter was decreased in vehicle- and ER-β ligand-treated but not in ER-α ligand-treated EAE mice. Inflammatory cell infiltration was increased in vehicle- and ER-β ligand-treated but not in ER-α ligand-treated EAE mice. Myelin staining was decreased in vehicle-treated EAE mice and was spared in both ER ligand-treated groups. This is consistent with decreased FA as a potential biomarker for inflammation rather than myelination or axonal damage in the cerebellum in EAE.

Estrogen treatment prevents gray matter atrophy in experimental autoimmune encephalomyelitis.

In vivo vs. ex vivo Magnetic Resonance Imaging In Mice.

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease of the central nervous system. While current medication reduces relapses and inflammatory activity, it has only a modest effect on long-term disability and gray matter atrophy. Here, we have characterized the potential neuroprotective effects of testosterone on cerebral gray matter in a pilot clinical trial. Ten men with relapsing-remitting MS were included in this open-label phase II trial. Subjects were observed without treatment for 6 months, followed by testosterone treatment for another 12 months. Focal gray matter loss as a marker for neurodegeneration was assessed using voxel-based morphometry. During the non-treatment phase, significant voxel-wise gray matter decreases were widespread (p≤ 0.05 corrected). However, during testosterone treatment, gray matter loss was no longer evident. In fact, a significant gray matter increase in the right frontal cortex was observed (p≤ 0.05 corrected). These observations support the potential of testosterone treatment to stall (and perhaps even reverse) neurodegeneration associated with MS. Furthermore, they warrant the investigation of testosterone's neuroprotective effects in larger, placebo controlled MS trials as well as in other neurodegenerative diseases. This is the first report of gray matter increase as the result of treatment in MS.

Neuroprotective effects of testosterone treatment in men with multiple sclerosis.

Previous studies have revealed an increased fractional anisotropy and greater thickness in the anterior parts of the corpus callosum in meditation practitioners compared with control subjects. Altered callosal features may be associated with an altered inter-hemispheric integration and the degree of brain asymmetry may also be shifted in meditation practitioners. Therefore, we investigated differences in gray matter asymmetry as well as correlations between gray matter asymmetry and years of meditation practice in 50 long-term meditators and 50 controls. We detected a decreased rightward asymmetry in the precuneus in meditators compared with controls. In addition, we observed that a stronger leftward asymmetry near the posterior intraparietal sulcus was positively associated with the number of meditation practice years. In a further exploratory analysis, we observed that a stronger rightward asymmetry in the pregenual cingulate cortex was negatively associated with the number of practice years. The group difference within the precuneus, as well as the positive correlations with meditation years in the pregenual cingulate cortex, suggests an adaptation of the default mode network in meditators. The positive correlation between meditation practice years and asymmetry near the posterior intraparietal sulcus may suggest that meditation is accompanied by changes in attention processing.

Shifting brain asymmetry: the link between meditation and structural lateralization.

Gray matter atrophy has been shown to be a strong correlate to clinical disability in multiple sclerosis (MS) and its most commonly used animal model, experimental autoimmune encephalomyelitis (EAE). However, the relationship between gray mater atrophy and the spinal cord pathology often observed in EAE has never been established. Here EAE was induced in Thy1.1-YFP mice and their brains imaged using in vivo magnetic resonance imaging (MRI). The brains and spinal cords were subsequently optically cleared using Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging-compatible Tissue-hYdrogel (CLARITY). Axons were followed 5mm longitudinally in three dimensions in intact spinal cords revealing that 61% of the axons exhibited a mean of 22 axonal ovoids and 8% of the axons terminating in axonal end bulbs. In the cerebral cortex, we observed a decrease in the mean number of layer V pyramidal neurons and a decrease in the mean length of the apical dendrites of the remaining neurons, compared to healthy controls. MRI analysis demonstrated decreased cortical volumes in EAE. Cross-modality correlations revealed a direct relationship between cortical volume loss and axonal end bulb number in the spinal cord, but not ovoid number. This is the first report of the use of CLARITY in an animal model of disease and the first report of the use of both CLARITY and MRI.

Bringing CLARITY to gray matter atrophy.

Relapses of multiple sclerosis decrease during pregnancy, when the hormone estriol is increased. Estriol treatment is anti-inflammatory and neuroprotective in preclinical studies. In a small single-arm study of people with multiple sclerosis estriol reduced gadolinium-enhancing lesions and was favourably immunomodulatory. We assessed whether estriol treatment reduces multiple sclerosis relapses in women.

Estriol combined with glatiramer acetate for women with relapsing-remitting multiple sclerosis: a randomised, placebo-controlled, phase 2 trial.

Multiple sclerosis (MS) is characterized by progressive gray matter (GM) atrophy that strongly correlates with clinical disability. However, whether localized GM atrophy correlates with specific disabilities in patients with MS remains unknown.

Allan MacKenzie-Graham

Department of Neurology,
David Geffen School of Medicine,
Department of Neurology, David Geffen School of Medicine

University of California, Los Angeles

Optical Clearing of the Mouse Central Nervous System Using Passive CLARITY

Allan MacKenzie-Graham

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Neurology,David Geffen School of Medicine,Department of Neurology, David Geffen School of Medicine

University of California, Los Angeles

Optical Clearing of the Mouse Central Nervous System Using Passive CLARITY

Department of Neurology,
David Geffen School of Medicine,
Department of Neurology, David Geffen School of Medicine