BRAIN DATA CORRELATION

大脑数据关联

• 批准号: 6243276
• 负责人: JOHN C MASSIOTTA
• 金额: $ 32.94万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-01 至 1998-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6243276
• 关键词: artificial intelligence; brain imaging /visualization /scanning; brain mapping; computational neuroscience; computer program /software; digital imaging; human data; human tissue; image processing; magnetic resonance imaging; mathematical model; phantom model; positron emission tomography; postmortem; statistics /biometry

Project 1 of this consortium, Brain Data Correlation, will focus on the experimental and statistical operations necessary to understand the relationships between different structural or functional brain data sets. We will develop and evaluate a variety of approaches for mapping anatomic and physiologic data into a common probabilistic reference space. The proposed work will extend the atlas concept by providing a more adaptable and useful way of measuring and comparing neuroanatomic and neurophysiologic data. Rather than depend on a single representative data set, we will develop a probabilistic model that represents a population. There are six specific aims. First, we will acquire the appropriate data sets for the development and testing of algorithms for correlation and deformation, including phantoms, in vivo and postmortem primate anatomy and human MRI-PET with skull-based fiducials. Second, we will develop corrective schemes to minimize errors and variance introduced by the acquisition process itself. Third, reliable and accurate algorithms (positioning, scaling, affine transformations, linear and non-linear transformations) for placing data volumes within reference/coordinate systems will be defined. Approaches designed for one modality may not be applicable for others. Fourth, we will develop transformations that include local deformations to increase the degree of correlation between data sets. We will derive the procedures and mathematics to warp data sets to map one upon another, to map to a common probabilistic reference system and to retain the direction and degree of deformation in a format suitable for interacting with the probabilistic space. Fifth, we will develop approaches that will include the retention of variability. The focus of these efforts is to make more comprehensive the representation of neuroscientific information about structure and function as opposed to the traditional approach of using a single subject representation from which to base an atlas/reference system. In this way we plan to retain information about inherent brain variation that will help us understand its normal distribution. Sixth, software design and validity will be driven by our goals to quantify and optimize the tools for selecting the most ideal approach to acquisition, acquisition correction, alignment/registration, deformation and retention of variability.

该财团的项目1，大脑数据相关性，将重点放在 了解的实验和统计操作 不同结构或功能性脑数据集之间的关系。 我们将开发和评估各种映射解剖学的方法 和生理数据成一个常见的概率参考空间。 这 拟议的工作将通过提供更适应的方法来扩展Atlas概念 以及测量和比较神经解剖学和的有用方法 神经生理数据。 而不是依靠一个代表 数据集，我们将开发一个代表一个概率模型 人口。 有六个具体目标。 首先，我们将获得 适当的数据集用于开发和测试算法的 相关性和变形，包括幻象，体内和验尸后 灵长类动物解剖学和人类MRI-PET，具有基于颅骨的信托。 第二， 我们将制定纠正方案以最大程度地减少错误和差异 由收购过程本身引入。 第三，可靠和 精确算法（定位，缩放，仿射转换，线性 和非线性转换）用于将数据量放入 将定义参考/坐标系。 设计的方法 一种模式可能不适用于其他方式。 第四，我们将发展 转换包括局部变形以增加学位 数据集之间的相关性。 我们将得出程序， 数学到翘曲数据集以绘制一个绘制一个，以映射到一个共同的 概率参考系统，并保留方向和程度 适合与概率相互作用的格式的变形 空间。 第五，我们将开发将包括保留的方法 可变性。 这些努力的重点是使更全面 关于结构和的神经科学信息的表示 功能与使用单一主题的传统方法相反 代表以基于地图集/参考系统的代表。 这样 我们计划保留有关固有大脑变异的信息 帮助我们了解其正态分布。 第六，软件设计和 有效性将由我们的目标驱动，以量化和优化工具 用于选择最理想的获取方法，获取 校正，对齐/注册，变形和保留 可变性。