Statistical Representations and Algorithms for Brain Connectivity

大脑连接的统计表示和算法

基本信息

  • 批准号:
    1228369
  • 负责人:
  • 金额:
    $ 49.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-09-01 至 2016-08-31
  • 项目状态:
    已结题

项目摘要

The statistical analysis of samples of images and in particular of fMRI brain images is a challenging problem, due to the high complexity of these data and their large size. Current methodology is mostly ad hoc, which limits the scope and quality of the analysis. This research addresses this situation by developing model-based statistical methodology, speci cally for the analysis of function-valued spatial stochastic processes, within a more general frame- work of object data analysis. Such data are encountered in spatio-temporal climatological studies and in resting state fMRI. The latter is used for task-free brain imaging in order to determine brain connectivity and is a main focus of this research. A key aspect is that the investigators view each brain as a sampling unit and develop statistical methods that utilize the entire sample of available brain images to infer common structures and variation in connectivity. The methods are generally applicable for the assessment of dependency structures for spatial processes. The investigators study both modeling of individual connectivity for a given realization of the spatial process, as well as connectivity at the population level. To model individuals, they investigate random covari- ance surfaces and their properties, adopting adequate metrics on the space of covariance functions. To model population connectivity, the investigators develop a decomposition for spatio-temporal covari- ance. For all proposed methods, they investigate theory, efficient computational implementations and applications to both brain and spatial data.The investigators develop advanced statistical methods for brain imaging data. Such data are routinely collected for many individuals in functional magnetic resonance imaging and are large and complex. Their analysis requires the development of sophisticated computational and statistical tools, which is the focus of this research. The methods are then applied to quantify and compare recurring patterns of connectivity of different parts of the brain for individuals and across populations. Besides characterizing the function of the brain, patterns of connectivity may include early indicators of pathology such as early signs of dementia. The investigators also study the broader impact and applicability of the new methodology.
由于这些数据的高度复杂性和它们的大尺寸,图像样本的统计分析,特别是fMRI脑图像的统计分析是一个具有挑战性的问题。目前的方法大多是特别的,这限制了分析的范围和质量。本研究通过开发基于模型的统计方法来解决这一问题,特别是在更一般的对象数据分析框架内分析函数值空间随机过程。这些数据在时空气候学研究和静息状态fMRI中都遇到过。后者用于无任务脑成像,以确定大脑连接,是本研究的主要焦点。一个关键的方面是,研究人员将每个大脑视为一个采样单元,并开发了统计方法,利用可用的大脑图像的整个样本来推断共同的结构和连接的变化。这些方法一般适用于空间过程依赖结构的评价。研究人员研究了给定空间过程实现的个体连通性建模,以及人口水平的连通性。为了对个体进行建模,他们研究随机协方差曲面及其性质,在协方差函数空间上采用适当的度量。为了模拟人口连通性,研究人员开发了时空协方差的分解。对于所有提出的方法,他们研究理论,有效的计算实现和应用到大脑和空间数据。研究人员开发了先进的脑成像数据统计方法。这些数据通常是在功能性磁共振成像中为许多个体收集的,而且数据庞大而复杂。他们的分析需要复杂的计算和统计工具的发展,这是本研究的重点。然后,这些方法被用于量化和比较个体和人群中大脑不同部分的反复出现的连接模式。除了表征大脑的功能,连接模式可能包括早期病理指标,如痴呆的早期迹象。研究人员还研究了新方法的广泛影响和适用性。

项目成果

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Hans-Georg Mueller其他文献

Hans-Georg Mueller的其他文献

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{{ truncateString('Hans-Georg Mueller', 18)}}的其他基金

Statistical Models and Methods for Complex Data in Metric Spaces
度量空间中复杂数据的统计模型和方法
  • 批准号:
    2310450
  • 财政年份:
    2023
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Standard Grant
Models for Complex Functional and Object Data
复杂功能和对象数据的模型
  • 批准号:
    2014626
  • 财政年份:
    2020
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Standard Grant
From Functional Data to Random Objects
从功能数据到随机对象
  • 批准号:
    1712864
  • 财政年份:
    2017
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Continuing Grant
Modeling Complex Functional Data
复杂功能数据建模
  • 批准号:
    1407852
  • 财政年份:
    2014
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Standard Grant
Nonlinear Models for Functional Data Analysis
函数数据分析的非线性模型
  • 批准号:
    1104426
  • 财政年份:
    2011
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Continuing Grant
Functional Models for Complex and High-Dimensional Data
复杂和高维数据的函数模型
  • 批准号:
    0806199
  • 财政年份:
    2008
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Continuing Grant
Nonparametric Methods for Functional Data
函数数据的非参数方法
  • 批准号:
    0505537
  • 财政年份:
    2005
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: FRG: New Development on Nonparametric Modeling and Inferences with Biological Applications
合作研究:FRG:非参数建模和生物学应用推论的新进展
  • 批准号:
    0354448
  • 财政年份:
    2004
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Standard Grant
Nonparametric and Semiparametric Models for High-Dimensional Data
高维数据的非参数和半参数模型
  • 批准号:
    0204869
  • 财政年份:
    2002
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Standard Grant
Nonparametric and Semiparametric Modelling for Data Analysis
数据分析的非参数和半参数建模
  • 批准号:
    9971602
  • 财政年份:
    1999
  • 资助金额:
    $ 49.5万
  • 项目类别:
    Continuing Grant

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