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中文摘要
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描述(由申请人提供):该提案的总体目标是创建用于分析神经回路超微结构的信息学算法。确定大脑回路的详细连接是神经科学中一个尚未解决的基本问题。了解这种电路将使大脑科学家能够确认或反驳现有模型,开发新模型,并更进一步了解大脑的工作原理。早产儿的临床成像已确定灰质和白质正常发育的破坏是与长期不良神经发育结果频繁发生相关的主要危险因素。用于成像和分析神经元、轴突和突触的成熟和连接的技术的发展将为导致不良结果的损伤机制提供重要的新见解,并促进寻找成功的干预措施。 电子显微镜成像的最新进展现在提供了以前所未有的分辨率和极大的兴趣体积对大脑超微结构进行成像的能力。最近的工作正在继续将体素的分辨率提高到更小的尺寸,获取更多的体素,同时实现更大区域的自动捕获。然而,图像采集方面的这些进步尚未与能够进行神经电路分析的算法和实现的进步相匹配。最近的评论已确定开发适当的信息学工具是理解大脑连接性取得新成功的首要要求。 该提案的具体目标是通过以下方式促进神经超微结构的分析和解释:1.)从 2D 图像创建神经超微结构的 3D 体积,2.)从 2D 相机块创建神经超微结构的大型 2D 图像,以及 3.)神经超微结构的分割和检测。实现这些具体目标的研究涉及新型信息学算法的开发、实施和评估,这些算法是专门为满足神经超微结构高分辨率大数据采集电子显微镜的要求而设计的。
英文摘要
DESCRIPTION (provided by applicant): The overall objective of this proposal is the creation of informatics algorithms for analysis of neural circuitry ultrastructure. Determining the detailed connections in brain circuits is a fundamental unsolved problem in neuroscience. Understanding this circuitry will enable brain scientists to confirm or refute existing models, develop new ones, and come closer to an understanding of how the brain works. Clinical imaging of prematurely born infants has identified disruptions of the normal development of gray matter and white matter as the major risk factors associated with the frequent occurrence of long term adverse neurodevelopmental outcomes. The development of technology for imaging and analyzing the maturation and connectivity of neurons, axons and synapses will provide critical new insight into the mechanisms of injury that cause poor outcomes, and facilitate the search for successful interventions. Recent advances in electron microscopy imaging now provide the capability to image the ultrastructure of the brain at unprecedented resolution with extremely large volumes of interest. Recent work is continuing to increase the resolution of voxels to smaller sizes, acquiring even more voxels, while enabling automated capture of larger regions. However, these advances in image acquisition have not yet been matched by advances in algorithms and implementations that will be capable of enabling the analysis of neural circuitry. Recent reviews have identified the development of appropriate informatics tools as the primary requirement for new success in understanding the connectivity of the brain. The specific aims of this proposal are to facilitate the analysis and interpretation of neural ultrastructure by: 1.) Create 3D Volumes of Neural Ultrastructure from 2D Images, 2.) Create Large 2D Images of Neural Ultrastructure from 2D Camera Tiles, and 3.) Segmentation and Detection of Neural Ultrastructure. The research to achieve each of these specific aims involves the development, implementation and evaluation of novel informatics algorithms especially designed to meet the requirements of high resolution large data acquisition electron microscopy of neural ultrastructure.
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Motion Compensated fMRI for Pre-Surgical Planning in Epilepsy
  • 批准号:
    10659634
  • 项目类别:
  • 资助金额:
    $67.11万
  • 财政年份:
    2023
  • 负责人:
    SIMON K WARFIELD
  • 依托单位:
Machine learning algorithms to analyze large medical image datasets
  • 批准号:
    10434022
  • 项目类别:
  • 资助金额:
    $37.61万
  • 财政年份:
    2021
  • 负责人:
    SIMON K WARFIELD
  • 依托单位:
Machine learning algorithms to analyze large medical image datasets
  • 批准号:
    10182522
  • 项目类别:
  • 资助金额:
    $36.96万
  • 财政年份:
    2021
  • 负责人:
    SIMON K WARFIELD
  • 依托单位:
Machine learning algorithms to analyze large medical image datasets
  • 批准号:
    10584569
  • 项目类别:
  • 资助金额:
    $37.61万
  • 财政年份:
    2021
  • 负责人:
    SIMON K WARFIELD
  • 依托单位:
海外基金