Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions

Knife-Edge Scanning Microscope Mouse Brain Atlas In Vector Graphics For Enhanced Performance

The microstructure of the brain at the cellular level provides crucial information for the understanding of the function of the brain. A large volume of high-resolution brain image data from 3D microscopy is an essential resource to study detailed microstructures of the brain. Accordingly, we have worked on obtaining high-resolution image data of entire mouse brains using the Knife-Edge Scanning Microscope (KESM). Furthermore, to disseminate these high-resolution whole mouse brain data sets to the neuroscience research community, we developed a web-based brain atlas, the KESM Brain Atlas (KESMBA). To visualize the data sets in 3D while using only a standard web browser, we employed distance attenuation and Google Maps API. The KESMBA is a powerful tool to analyze and share the KESM mouse brain data sets, but the image loading was slow because of the number of raster image (PNG) tiles and the file size. Moreover, since Google Maps API is governed by a commercial license, it does not provide enough flexibility for customization, extension, and mirroring. To solve these issues, we designed and developed a new KESM mouse brain atlas that uses a vector graphics format called Scalable Vector Graphics (SVG) instead of PNG, and OpenLayers API instead of Google Maps API. The SVG-based KESMBA using OpenLayers allows faster navigation and exploration of the KESM data, and more overlay of layers with the 4 times reduced file size compared to PNG tiles. Due to the reduced file size, the SVG-based KESMBA using OpenLayers is 2.45 times faster than the original atlas. By enhancing the performance, the users can more easily access the KESM data. We expect the SVG-based KESMBA to accelerate new discoveries in neuroscience

Exploration, Registration, and Analysis of High-Throughput 3D Microscopy Data from the Knife-Edge Scanning Microscope

Advances in high-throughput, high-volume microscopy techniques have enabled the acquisition of extremely detailed anatomical structures on human or animal organs. The Knife-Edge Scanning Microscope (KESM) is one of the first instruments to produce sub-micrometer resolution ( ~1 µm^(3)) data from whole small animal brains. We successfully imaged, using the KESM, entire mouse brains stained with Golgi (neuronal morphology), India ink (vascular network), and Nissl (soma distribution). Our data sets fill the gap of most existing data sets which have only partial organ coverage or have orders of magnitude lower resolution. However, even though we have such unprecedented data sets, we still do not have a suitable informatics platform to visualize and quantitatively analyze the data sets. This dissertation is designed to address three key gaps: (1) due to the large volume (several tera voxels) and the multiscale nature, visualization alone is a huge challenge, let alone quantitative connectivity analysis; (2) the size of the uncompressed KESM data exceeds a few terabytes and to compare and combine with other data sets from different imaging modalities, the KESM data must be registered to a standard coordinate space; and (3) quantitative analysis that seeks to count every neuron in our massive, growing, and sparsely labeled data is a serious challenge. The goals of my dissertation are as follows: (1) develop an online neuro-informatics framework for efficient visualization and analysis of the multiscale KESM data sets, (2) develop a robust landmark-based 3D registration method for mapping the KESM Nissl-stained entire mouse data into the Waxholm Space (a canonical coordinate system for the mouse brain), and (3) develop a scalable, incremental learning algorithm for cell detection in high-resolution KESM Nissl data. For the web-based neuroinformatics framework, I prepared multi-scale data sets at different zoom levels from the original data sets. And then I extended Google Maps API to develop atlas features such as scale bars, panel browsing, and transparent overlay for 3D rendering. Next, I adapted the OpenLayers API, which is a free mapping and layering API supporting similar functionality as the Google Maps API. Furthermore, I prepared multi-scale data sets in vector-graphics to improve page loading time by reducing the file size. To better appreciate the full 3D morphology of the objects embedded in the data volumes, I developed a WebGL-based approach that complements the web-based framework for interactive viewing. For the registration work, I adapted and customized a stable 2D rigid deformation method to map our data sets to the Waxholm Space. For the analysis of neuronal distribution, I designed and implemented a scalable, effective quantitative analysis method using supervised learning. I utilized Principal Components Analysis (PCA) in a supervised manner and implemented the algorithm using MapReduce parallelization. I expect my frameworks to enable effective exploration and analysis of our KESM data sets. In addition, I expect my approaches to be broadly applicable to the analysis of other high-throughput medical imaging data

Knife Edge Scanning Microscope Brain Atlas Interface for Tracing and Analysis of Vasculature Data

The study of the neurovascular network in the brain is important to understand brain functions as well as causes of several brain dysfunctions. Many techniques have been applied to acquire neurovascular data. The Knife-Edge Scanning Microscope (KESM), developed by the Brain Network Lab at Texas A&M University, can generate whole-brain-scale data at submicrometer resolution. The specimen can be stained with different stains, and depending on the type of stain used, the KESM can image different types of microstructures in the brain. The India ink stain allows the neurovascular network in the brain to be imaged. In order to visualize and analyze such large datasets (~ 1.5 TB per brain), a lightweight, web-based mouse brain atlas called the Knife-Edge Scanning Microscope Brain Atlas (KESMBA) was developed in the lab. The atlas serves several whole mouse brain data sets including India ink. The multi-section overlay technique used in the atlas enables 3D visualization of the structural information in the data. To solve the challenging issue of tracing micro-vessels in the brain, in this thesis a semi-automated tracing and analysis method is developed and integrated into the KESM brain atlas. Using the KESMBA interface developed in this thesis, the user can look at the 3D structure of the vessels on the brain atlas and can guide the tracing algorithm. To analyze the vasculature network traced by the user, a data analysis component is also added. This new KESMBA interface is expected to help in quickly tracing and analyzing the vascular network of the brain with minimal manual effort. In order to visualize and analyze such large data sets (~ 1.5 TB per brain), a light-weight, web-based mouse brain atlas called the Knife-Edge Scanning Microscope Brain Atlas (KESMBA) was developed in the lab. The atlas serves several whole mouse brain data sets including India ink. The multi-section overlay technique used in the atlas enables 3D visualization of the structural information in the data. To solve the challenging issue of tracing micro-vessels in the brain, in this thesis a semi-automated tracing and analysis method is developed and integrated into the KESM brain atlas. Using the KESMBA interface developed in this thesis, the user can look at the 3D structure of the vessels on the brain atlas and can guide the tracing algorithm. In order to analyze the vasculature network traced by the user, a data analysis component is also added. This new KESMBA interface is expected to help in quickly tracing and analyzing the vascular network of the brain with minimal manual effort

Automated Neurovascular Tracing and Analysis of the Knife-Edge Scanning Microscope India Ink Data Set

The 3D reconstruction of neurovascular network plays an important role in understanding the functions of the blood vessels in different brain regions. Many techniques have been applied to acquire microscopic neurovascular data. The Knife-Edge Scanning Microscope (KESM) is a physical sectioning microscopy instrument developed by the Brain Network Lab in Texas A&M University which enables imaging of an entire mouse brain at sub-micrometer resolution. With the KESM image data, we can trace the neurovascular structure of the whole mouse brain. For the large neurovascular volume like the KESM data set, complicated tracing algorithm with template matching process is not fast enough. Also, KESM imaging might involve gaps and noise in data when acquiring the large volume of data. To solve these issues, a novel automated neurovascular tracing and data analysis method with less processing time and high accuracy is developed in this thesis. First, an automated seed point selection algorithm was described in my approach. The seed points on every outer boundary surface of the volume were selected as the start points of tracing. Second, a vector-based tracing method was developed to trace vascular network in 3D space. Third, the properties of the extracted vascular network were analyzed. Finally, the accuracy of the tracing method was evaluated using synthetic data. This approach is expected to help explore the entire vascular network of KESM automatically without human assistance

Mapping the Connectome: Multi-Level Analysis of Brain Connectivity

Background and scope The brain contains vast numbers of interconnected neurons that constitute anatomical and functional networks. Structural descriptions of neuronal network elements and connections make up the “connectome ” of the brain (Hagmann, 2005; Sporns et al., 2005; Sporns, 2011), and are important for understanding normal brain function and disease-related dysfunction. A long-standing ambition of the neuroscience community has been to achieve complete connectome maps for the human brain as well as the brains of non-human primates, rodents, and other species (Bohland et al., 2009; Hagmann et al., 2010; Van Essen and Ugurbil, 2012). A wide repertoire of experimental tools is currently available to map neural connectivity at multiple levels, from the tracing of mesoscopic axonal connections and the delineation of white matter tracts (Saleem et al., 2002; Van der Linden et al., 2002; Sporns et al., 2005; Schmahmann et al., 2007; Hagmann et al., 2010), the mappin

Automated Neurovascular Tracing and Analysis of the Knife-Edge Scanning Microscope India Ink Data Set

The 3D reconstruction of neurovascular network plays an important role in understanding the functions of the blood vessels in different brain regions. Many techniques have been applied to acquire microscopic neurovascular data. The Knife-Edge Scanning Microscope (KESM) is a physical sectioning microscopy instrument developed by the Brain Network Lab in Texas A&M University which enables imaging of an entire mouse brain at sub-micrometer resolution. With the KESM image data, we can trace the neurovascular structure of the whole mouse brain. For the large neurovascular volume like the KESM data set, complicated tracing algorithm with template matching process is not fast enough. Also, KESM imaging might involve gaps and noise in data when acquiring the large volume of data. To solve these issues, a novel automated neurovascular tracing and data analysis method with less processing time and high accuracy is developed in this thesis. First, an automated seed point selection algorithm was described in my approach. The seed points on every outer boundary surface of the volume were selected as the start points of tracing. Second, a vector-based tracing method was developed to trace vascular network in 3D space. Third, the properties of the extracted vascular network were analyzed. Finally, the accuracy of the tracing method was evaluated using synthetic data. This approach is expected to help explore the entire vascular network of KESM automatically without human assistance

Skeletonization-Based Automated Tracing and Reconstruction of Neurovascular Networks in Knife-Edge Scanning Microscope Mouse Brain India Ink Dataset

The vascular architecture of the brain is very complex and reconstruction of this architecture can aid in understanding the functions of vessels in different regions of the brain. Advances in microscopy have enabled high-throughput imaging of massive volumes of biological microstructure at a very high resolution. The Knife Edge Scanning Microscope (KESM), developed by the Brain Network Laboratory at Texas A & M University, is one such instrument that enables imaging of whole small animal brains at sub-micrometer resolution. The KESM has been successfully used to acquire vasculature dataset from a mouse brain stained by India ink. However, manual tracing and reconstruction of vessels is not feasible due to the huge volume of the dataset. Therefore, developing efficient and robust automatic tracing methods is essential for analysis of the network. This thesis presents an efficient skeletonization based tracing algorithm to reconstruct the vascular structure in the KESM India ink data set. The skeleton is generated from the original volume by repetitively removing voxels from the object’s boundary such that the connectivity and topology in the original volume is preserved. The skeleton is then dilated to reconstruct the volume. The accuracy of this method is determined by comparing the reconstructed volume with the original volume. This method is expected to trace the entire vascular network of the brain quickly and with high accuracy without human assistance

Automated Reconstruction of Neurovascular Networks in Knife-Edge Scanning Microscope Mouse and Rat Brain Nissl Stained Data Sets

The Knife-Edge Scanning Microscope (KESM), developed at the Brain Network Laboratory at Texas A&M University, can image a whole small animal brain at sub- micrometer resolution. Nissl data from the KESM enable us to look into vasculatures and cell bodies at the same time. Hence, analyzing the images from KESM mouse and rat Nissl data can help understand interactions between cerebral blood flow and its surrounding tissue. However, analysis is difficult since the image data contain complex cellular features, as well as imaging artifacts, which make it hard to extract the geometry of the vasculature and the cells. In this project, we propose a novel approach to reconstructing the neurovascular networks from whole-brain mouse and partial rat Nissl data sets. The proposed method consists of (1) pre-processing, (2) thresholding, and (3) post-processing. Initially, we enhanced the raw image data in the pre-processing step. Next, we applied a dynamic global thresholding to ex-tract vessels in the thresholding step. Subsequently, in the post-processing step, we computed local properties of the connected components to remove various sources of noise and we applied artificial neural networks to extract vasculatures. Concurrently, the proposed method connected small and large discontinuities in the vascular traces. To validate the performance of the proposed method, we compared reconstruction results of the proposed method with an alternative method (Lim's method). The comparison shows that the proposed method significantly outperforms (nine times faster, and more robust to noise) Lim's method. As a consequence, the proposed method provides a framework that can be applied to other data sets, even when the images contain a large portion of low-contrast images across the image stacks. We expect that the proposed method will contribute to studies investigating the correlation between the soma of the cells and microvascular networks

Semi-Automated Reconstruction of Vascular Networks in Knife-Edge Scanning Microscope Mouse Brain Data

The KnifeEdge Scanning Microscope (KESM) enables imaging of an entire mouse brain at sub-micrometer resolution. The data from KESM can be used in the reconstruction of neuronal and vascular structures in the mouse brain. Tracing the vascular network of the brain and reconstructing the topology allows us to map the circulatory pathways inside the brain. Studying these cerebro-vascular networks is important to understand and measure the consumption and access to energy, oxygen and nutrients by different regions of the brain. Presently, there are both manual and automated methods to trace the vascular network from images of the brain. The manual methods are limited by the time consuming nature of the process and the extensive manual labor required. Today, vascular reconstruction techniques focus either on tracing vessels at the macro-level in a whole brain or tracing micro vessels in a small section of the brain. In this thesis, I attempt to develop a new, more targeted approach to semi-automatically trace a single blood vessel and its associated network of branches. In my approach, the user provides the algorithm with a single seed point of a vessel to start exploration and can guide the system towards specific sub-branches or sub-networks to explore. This new approach is expected to help quickly trace the vascular network of the brain as well as reduce the manual effort involved and save computing power by limiting the scope of the reconstruction to a smaller sub-network of blood vessels