Advanced Methods for the Statistical Analysis of Functional Magnetic Resonance Imaging Data
功能磁共振成像数据统计分析的先进方法
基本信息
- 批准号:9705034
- 负责人:
- 金额:$ 30万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:1997
- 资助国家:美国
- 起止时间:1997-08-15 至 2000-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Eddy, Genovese, & Lazar 9705034 Functional Magnetic Resonance Imaging (fMRI) is a powerful new tool for understanding the brain. With fMRI, it is possible to study the human brain in action and trace its processing in unprecedented detail. During an fMRI experiment, a subject performs a carefully planned sequence of cognitive tasks while magnetic resonance images of the brain are acquired. The tasks are designed to exercise specific cognitive processes and the measured signal contains information about the nature and location of the resulting neural activity. Neuroscientists use these data to help identify the neural processes underlying cognition and to build and test theoretical models of cognitive function. This is inherently a problem of statistical inference, yet the statistical methods for fMRI are still undeveloped. In this project, the statistical methodology for these large and complex data sets is advanced on three fronts: dealing with model response variation, developing better registration and acquisition methods, and analyzing spatial activation patterns. Functional Magnetic Resonance Imaging (fMRI) is a new tool that is currently being used to study the brain and the way it functions. Very large amounts of data, with considerable noise, are collected on neural activity while specific cognitive tasks are being performed. In this way, cognitive scientists hope to understand the processes underlying the way humans think. Statistical inference is a natural way of approaching this question. However, the complex nature of the data means that standard methods are not applicable and the methodologies used in fMRI for data analysis are still relatively undeveloped. The current project advances the statistical methodology for fMRI data by working in three directions. Brain response to a given task varies not only by location, but also in different replications of the same experiment. This source of variability is not taken into account by the models now in use. The first direction of the project incorporates this source of variation, resulting in more precise inferences. Subject motion during fMRI scanning is the focus of the second direction, while the third direction involves quantifying how spatial patterns of activation change over time. This allows the comparison of different individuals and groups.
功能磁共振成像(fMRI)是了解大脑的一个强大的新工具。有了功能磁共振成像,就有可能研究人类大脑的活动,并以前所未有的细节追踪其处理过程。在fMRI实验中,受试者在获得大脑磁共振图像的同时,执行一系列精心计划的认知任务。这些任务旨在锻炼特定的认知过程,测量到的信号包含有关神经活动的性质和位置的信息。神经科学家利用这些数据来帮助识别认知背后的神经过程,并建立和测试认知功能的理论模型。这本质上是一个统计推断的问题,然而功能磁共振成像的统计方法仍然没有发展起来。在本项目中,这些大型复杂数据集的统计方法在三个方面得到了改进:处理模型响应变化,开发更好的配准和获取方法,以及分析空间激活模式。功能磁共振成像(fMRI)是目前用于研究大脑及其功能的新工具。在执行特定的认知任务时,收集了大量的神经活动数据,其中有相当大的噪声。通过这种方式,认知科学家希望了解人类思维方式背后的过程。统计推断是处理这个问题的自然方法。然而,数据的复杂性意味着标准方法不适用,fMRI中用于数据分析的方法仍然相对不发达。目前的项目通过三个方向的工作来推进fMRI数据的统计方法。大脑对给定任务的反应不仅因地点而异,而且因同一实验的不同重复而异。现在使用的模型没有考虑到这种变率的来源。项目的第一个方向包含了这种变化的来源,从而产生更精确的推断。fMRI扫描过程中的受试者运动是第二个方向的重点,而第三个方向涉及量化激活的空间模式如何随时间变化。这允许对不同的个人和群体进行比较。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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William Eddy其他文献
Flatness-induced phase transition in Lyapunov spectrum for unimodal maps
单峰图李亚普诺夫谱中平坦度诱导的相变
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Zhigang Yao;Zengyan Fan;Masahito Hayashi;William Eddy;高橋 博樹 - 通讯作者:
高橋 博樹
William Eddy的其他文献
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{{ truncateString('William Eddy', 18)}}的其他基金
Workshop on Statistical Analysis of Neuroimaging Data for Social and Behavioral Science Research
社会和行为科学研究神经影像数据统计分析研讨会
- 批准号:
1045665 - 财政年份:2011
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
NCRN-MN: Data Integration, Online Data Collection, and Privacy Protection for Census 2020
NCRN-MN:2020 年人口普查的数据集成、在线数据收集和隐私保护
- 批准号:
1130706 - 财政年份:2011
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Magnetoencephalography - Analysis of Very Noisy Spatial and Temporal Varying Fields
脑磁图 - 非常嘈杂的空间和时间变化场的分析
- 批准号:
0527141 - 财政年份:2005
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
VIGRE: Vertical and Horizontal Integration of Research and Education in Statistics and Mathematical Sciences at Carnegie Mellon
VIGRE:卡内基梅隆大学统计和数学科学研究与教育的纵向和横向整合
- 批准号:
9819950 - 财政年份:1999
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
Mathematical Sciences Computing Research Environments
数学科学计算研究环境
- 批准号:
9707768 - 财政年份:1997
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Mathematical Sciences/GIG: "A Training Program in Cross- Disciplinary Research and Teaching"
数学科学/GIG:“跨学科研究和教学培训计划”
- 批准号:
9631248 - 财政年份:1996
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Statistical Methods for the Analysis of Functional Magnetic Resonance Imaging Data
功能磁共振成像数据分析的统计方法
- 批准号:
9505007 - 财政年份:1995
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Mathematical Sciences Computing Research Environments
数学科学计算研究环境
- 批准号:
9508427 - 财政年份:1995
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Mathematical Sciences Computing Research Environments
数学科学计算研究环境
- 批准号:
9305732 - 财政年份:1993
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Parallel Computing in Bayesian Inference
贝叶斯推理中的并行计算
- 批准号:
8805676 - 财政年份:1988
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
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