Advanced Methods for the Statistical Analysis of Functional Magnetic Resonance Imaging Data

Eddy，Genovese和Amp；Lazar 9705034功能磁共振成像(FMRI)是了解大脑的一种强大的新工具。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。在功能磁共振实验中，受试者在获取大脑磁共振图像的同时，执行一系列精心计划的认知任务。这些任务是为练习特定的认知过程而设计的，测量的信号包含有关由此产生的神经活动的性质和位置的信息。神经学家使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，然而用于功能磁共振成像的统计方法仍未开发出来。在这个项目中，针对这些庞大而复杂的数据集，从三个方面提出了统计方法：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)是一种新的工具，目前正被用于研究大脑及其功能方式。在执行特定的认知任务时，收集了大量关于神经活动的数据，其中包含相当大的噪音。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一种自然方式。然而，数据的复杂性意味着标准方法不适用，功能磁共振成像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推进了fMRI数据的统计方法。大脑对给定任务的反应不仅因地点不同而不同，而且在同一实验的不同重复中也不同。目前使用的模型没有考虑这种可变性的来源。该项目的第一个方向纳入了这一变异来源，从而产生了更准确的推断。第二个方向的焦点是fMRI扫描期间的受试者运动，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。

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.