A Toolkit for Analysis and Visualization of Preclinical Rodent Neuroimaging Experiments

用于临床前啮齿动物神经影像实验分析和可视化的工具包

• 批准号: 10454707
• 负责人: Allan James MacKenzie-Graham
• 金额: $ 62.65万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10454707
• 关键词: Address; Aging; Anatomy; Animal Disease Models; Animals; Area; Atlases; Automobile Driving; Axon; Brain; Brain Diseases; Brain imaging; Cell Count; Classification; Collection; Communities; Computer software; Computing Methodologies; Confocal Microscopy; Data; Data Analyses; Data Set; Dendritic Spines; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Documentation; Drug or chemical Tissue Distribution; Ensure; Evaluation; Experimental Autoimmune Encephalomyelitis; Feedback; Functional Magnetic Resonance Imaging; Generations; Goals; Head; Human; Image; Image Analysis; Imagery; Imaging Device; Informatics; Investigation; Label; Licensing; Magnetic Resonance Imaging; Manuals; Maps; Measures; Methods; Microscopy; Modality; Modeling; Multimodal Imaging; Multiple Sclerosis; Mus; Nerve Degeneration; Neurons; Neurosciences; Optics; Performance; Play; Process; Public Health; Reporting; Reproducibility; Research; Research Personnel; Resort; Resources; Rodent; Rodent Model; Role; Scanning; Shapes; Software Tools; Statistical Data Interpretation; Structural Models; Structure; Techniques; Testing; Tissue Sample; Tissues; Training; Visualization; Visualization software; Work; animal imaging; brain magnetic resonance imaging; brain tissue; cohesion; community engagement; computerized data processing; data visualization; deep learning; experience; experimental study; graphical user interface; human data; image processing; imaging modality; imaging study; improved; insight; interoperability; learning strategy; microscopic imaging; mouse model; multidimensional data; multimodality; nervous system disorder; neuroimaging; novel; open source; open source tool; optical imaging; pre-clinical; preclinical study; recruit; software development; statistics; tool; tractography; web site

Project Summary Rodent models remain an important in preclinical studies of brain disease and disorders, as well as basic neuroscience investigations. Rodent imaging data, acquired through techniques including MRI and microscopy, play a critical role in many of these studies. While a great deal of effort has gone into the development of software tools for analyzing MRI of the brain, much of this work has been focused largely on human data, and investigators studying animal models of disease must often resort to adapting these tools for their research. In this project, we will address this need by developing a dedicated suite of open source software tools for processing, analyzing, and visualizing neuroimaging data acquired from rodent brains. These tools will operate on structural diffusion, and functional MRI, as well as optical microscopy of optically cleared serially sectioned tissue samples. These tools will build upon our decades of experience developing software for analyzing human and mouse imaging data, our experience in developing multimodal atlases of the mouse brain, and our active efforts in community engagement and dissemination while applying these resources in neuroscientific studies. Where suitable, we will make use of deep learning methods to produce powerful segmentation and registration networks trained on manually annotated and delineated data. We will also develop easy-to-use interfaces that will facilitate data processing and provide advanced visualization capabilities of datasets with sizes on the order of one terapixel. The project has five specific aims. Aim 1 will develop MRI processing tools, which will include intrasubject co- registration of MRI modalities, extraction of brain tissue from whole head scans, tissue classification, and processing of diffusion and functional MRI data. Aim 2 will develop tools for processing microscopy of cleared and sectioned tissue, with the major goal of aligning these data to a reference atlas generated from either optical microscopy or MRI. These tools will perform cell counting in automatically segmented regions; axon following; dendritic arborization; and dendritic spine counting. In Aim 3, we will develop a statistical analysis toolbox, which will perform statistical inference for neuroimaging measures from microscopy and MRI data analyzed using methods from Aims 1 and 2. In Aim 4, we will integrate the components from Aims 1-3 into an informatics platform that will provide command line tools for easy scripting, interoperability with related imaging tools, and a graphical interface for visualizing data across different scales. In Aim 5, we will perform evaluation of our software tools using two studies: imaging an experimental autoimmune encephalomyelitis (EAE) mouse model of multiple sclerosis; and imaging a mouse model of normative aging. We will also work with a network of small animal imaging experts external to the project, who will use and evaluate the software. These driving projects will serve as testbeds to ensure the practical utility of the software in a research setting, providing direction for the development of our research platform. We will distribute the software freely under an open source license, and provide user support through our website.

项目摘要 啮齿动物模型在脑疾病和障碍的临床前研究中仍然是重要的， 神经科学研究啮齿动物成像数据，通过包括MRI和显微镜技术获得， 在许多研究中扮演着重要的角色。虽然大量的努力已经投入到软件的开发中， 作为分析大脑MRI的工具，这项工作的大部分主要集中在人类数据上， 研究疾病的动物模型必须经常采用这些工具进行研究。本课题 将通过开发一套专用的开源软件工具来处理，分析， 以及可视化从啮齿动物大脑中获取的神经成像数据。这些工具将在结构扩散， 和功能性MRI，以及光学透明连续切片组织样本的光学显微镜检查。这些 这些工具将建立在我们数十年来开发用于分析人类和小鼠成像的软件的经验之上 数据，我们在开发小鼠大脑多模态图谱方面的经验，以及我们在社区中的积极努力， 参与和传播，同时将这些资源应用于神经科学研究。如果合适，我们 将利用深度学习方法来生成强大的分割和配准网络， 手动注释和描绘的数据。我们还将开发易于使用的界面， 处理和提供先进的可视化功能的数据集的大小在一个terapixel的顺序。 该项目有五个具体目标。Aim 1将开发MRI处理工具，其中将包括受试者内合作， MRI模态的配准，从全头部扫描中提取脑组织，组织分类，以及 扩散和功能MRI数据的处理。目标2将开发用于处理清除的 主要目标是将这些数据与从光学或光学显微镜产生的参考图谱进行比对， 显微镜或MRI。这些工具将在自动分割的区域进行细胞计数;轴突跟踪; 树突分枝;和树突棘计数。在目标3中，我们将开发统计分析工具箱， 将根据显微镜和MRI数据分析，对神经影像学指标进行统计推断， 目标1和2的方法。在目标4中，我们将把目标1-3的组成部分整合到一个信息学平台中 它将提供命令行工具，以便轻松编写脚本，与相关成像工具进行互操作，并提供图形化 用于跨不同尺度可视化数据的界面。在目标5中，我们将评估我们的软件工具 使用两项研究：成像实验性自身免疫性脑脊髓炎（EAE）小鼠模型， 硬化症;以及对正常老化的小鼠模型进行成像。我们还将与一个网络的小动物 项目外部的成像专家，他们将使用和评估该软件。这些驱动项目将服务于 作为测试平台，以确保软件在研究环境中的实用性，为 发展我们的研究平台。我们将在开放源代码许可证下免费分发软件， 通过我们的网站提供用户支持。