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

Eddy，Genovese，Lazar 9705034 功能性磁共振成像（fMRI）是了解大脑的强大新工具。 有了功能性磁共振成像，就有可能研究人脑的活动，并以前所未有的细节追踪其处理过程。 在功能磁共振成像实验中，受试者在获得大脑磁共振图像的同时执行精心计划的认知任务序列。 这些任务旨在锻炼特定的认知过程，所测量的信号包含有关所产生的神经活动的性质和位置的信息。 神经科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，但fMRI的统计方法仍然没有发展。 在这个项目中，这些大型和复杂的数据集的统计方法是先进的三个方面：处理模型响应变化，开发更好的注册和采集方法，并分析空间激活模式。 功能性磁共振成像（fMRI）是一种新的工具，目前正用于研究大脑及其功能。 在执行特定的认知任务时，收集了关于神经活动的非常大量的数据，这些数据具有相当大的噪声。 通过这种方式，认知科学家希望了解人类思考方式的潜在过程。 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并不适用，并且用于fMRI数据分析的方法仍然相对不发达。 目前的项目通过在三个方向上的工作推进功能磁共振成像数据的统计方法。 大脑对特定任务的反应不仅因位置而异，而且在同一实验的不同重复中也会有所不同。 目前使用的模型没有考虑到这种可变性的来源。 该项目的第一个方向结合了这一变异源，从而产生更精确的推论。 在功能磁共振成像扫描过程中的主题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。 这使得不同的个人和群体的比较。

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.