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
• 负责人: William Eddy
• 金额: $ 13万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-07-01 至 1997-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9505007&HistoricalAwards=false
• 关键词: Statistical; Methods; Analysis; Functional; Magnetic

Proposal: DMS 9505007 PI(s): William Eddy, Chris Genovese Institution: Carnegie Mellon University Title: Statisitcal Methods for the Analysis of Functional Magnetic Resonance Imaging Data Abstract: This research involves the development of new statistical methods for the analysis and interpretation of functional Magnetic Resonance Imaging (fMRI) data. Such data can be viewed as the realization of a spatio-temporal process with a very complicated distributional structure. Models in current use are grossly simplified for both mathematical and computational expediency. The statistical challenges in constructing more realistic models are difficult and manifold. Many revolve around understanding the nature of the noise in the measurements and its effect on successfully detecting regions of neural activation. Noise in the data shows significant spatial and temporal correlations that depend strongly on how the data are collected. Outliers are common, and there are strong sources of systematic variation such as the subject's respiratory and cardiac cycles. Variances in the images depend nonlinearly on the means, and the observed absolute levels of activation tend to shift between sessions because of subject movement. Moreover, all of these difficulties occur for data collected from a single subject; the situation becomes much more complicated if comparisons across subjects are attempted. This research focusses on three general problems in the statistical analysis of fMRI data: 1. The characterization of the response to an activating stimulus in the fMRI signal and the use of this information to build more realistic models and make more precise inferences; 2. The development of robust procedures for identifying active regions that account for the complexity of the underlying spatio-temporal process; and 3. The construction of functional maps within a specified system of the brain (e.g., the visual system) and the use these maps for making predictiv e inference across subjects. Functional Magnetic Resonance Imaging (fMRI) is an exciting new technique that uses advanced technology to obtain images of the active human brain. The technique is of particular interest to cognitive neuropsychologists because of the unique perspective it offers into high-level cognitive processing in humans: areas of the brain that are activated by a stimulus or cognitive task ``light up'' in an fMRI image. This technology will thus play a critical role in understanding how the brain works; however, before this potential can be realized, significant statistical challenges in the interpretation and analysis of fMRI data must be overcome. For example, there is substantial uncertainty in the identification of neural activity from these images and in the attribution of that activity to particular cognitive processes. Moreover, there is a need for new methods of making statistical inferences of scientific interest from these large and complex sets of data. This research focusses on three broad aspects of the general problem: 1. Constructing models for the systematic components of the process that generates the data, 2. Studying and modeling the properties of the noise in the measurements so that analysis and inference can be made more precise, and 3. Developing new methods of inference for addressing interesting scientific questions with massive sets of data that arise from measurements over space and time.

建议：DMS 9505007 Pi(S)：William Eddy，Chris Genovese研究所：卡内基梅隆大学标题：分析功能磁共振成像数据的统计方法摘要：这项研究涉及开发新的统计方法来分析和解释功能磁共振成像数据。这些数据可以被视为具有非常复杂的分布结构的时空过程的实现。为了数学和计算的方便，目前使用的模型都被大大简化了。在构建更现实的模型方面，统计学上的挑战是困难的，而且是多方面的。许多研究围绕着理解测量中噪声的性质以及它对成功检测神经激活区的影响。数据中的噪声表现出显著的空间和时间相关性，这在很大程度上取决于数据的收集方式。异常值很常见，而且有很强的系统性变异来源，如受试者的呼吸和心脏周期。图像中的差异与均值呈非线性关系，观察到的绝对激活水平往往会因为受试者的移动而在不同阶段发生变化。此外，所有这些困难都发生在从单一受试者收集的数据上；如果尝试跨受试者进行比较，情况会变得复杂得多。这项研究集中在fMRI数据统计分析中的三个一般问题：1.表征fMRI信号中对激活刺激的反应，并使用这些信息来建立更真实的模型和做出更精确的推断；2.开发健壮的程序来识别考虑潜在时空过程的复杂性的活动区域；以及3.在特定的大脑系统(例如视觉系统)内构建功能图，并使用这些图进行跨对象的预测性推理。功能磁共振成像(FMRI)是一项令人兴奋的新技术，它使用先进的技术来获得活跃的人脑图像。认知神经心理学家对这项技术特别感兴趣，因为它为人类的高级认知过程提供了独特的视角：在fMRI图像中，大脑中被刺激或认知任务激活的区域。因此，这项技术将在理解大脑如何工作方面发挥关键作用；然而，在实现这一潜力之前，必须克服在解释和分析功能磁共振数据方面的重大统计学挑战。例如，在从这些图像中识别神经活动以及将该活动归因于特定的认知过程方面存在很大的不确定性。此外，还需要新的方法，从这些庞大而复杂的数据集中做出具有科学意义的统计推断。这项研究集中在一般问题的三个广泛方面：1.为产生数据的过程的系统组件构建模型，2.研究和建模测量中的噪声特性，以便能够更精确地分析和推断，以及3.开发新的推理方法，以解决从空间和时间测量产生的大量数据集的有趣的科学问题。