Whole brain inference and prediction in neuroimaging

神经影像中的全脑推理和预测

• 批准号: 0906801
• 负责人: Jonathan Taylor
• 金额: $ 20万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0906801&HistoricalAwards=false
• 关键词: Whole; brain; inference; prediction; neuroimaging

The investigators in this proposal study two different aspects of noise in neuroimaging data: structure of non-Gaussian random fields and their applications to neuroimaging; along with interpretable predictive modelling of fMRI. The structure of non-Gaussian random fields is expected to shed light on how useful Random Field Theory (RFT), presently used for controlling Family Wise Error Rate (FWER) in neuroimaging studies, can be expected to be for truly non-Gaussian fields. The predictive models proposed by the investigators place much emphasis on interpretability and will allow comparison with the usual approaches based on detecting correlation between experimental stimuli and neuroimaging data while controlling FWER or False Discovery Rate (FDR).This project is motivated by the need for flexible and valid statistical procedures to interpret neuroimaging data and to confirm neuroscientific hypotheses derived from previous work. Examples of such neuroimaging data can be found in fields like social neuroscience, which seeks to understand and model human social behaviour based on the activation and interactions of various regions of the human brain; neuroeconomics, which seeks to interpret and model some of the decision-making processes of humans based on models of the human brain; and disease models such as schizophrenia in which the goal is to understand how the brains of schizophrenic patients differ in anatomy and function from those of non-schizophrenic patients. Virtually all neuroimaging data is what is known as "high-throughput" which means that huge amounts of data are recorded, typically for only a small group of individuals. Statistical tools are needed to produce consistent and reproducible results from such data. The investigators in this proposal will develop tools that can be used to confirm existing hypotheses about neuroimaging data, as well as generate hypotheses via interpretable predictive models of decision making processes.

该提案中的研究人员研究了神经影像数据中噪声的两个不同方面：非高斯随机场的结构及其在神经影像中的应用；以及可解释的功能磁共振成像预测模型。非高斯随机场的结构有望揭示随机场理论 (RFT)（目前用于控制神经影像研究中的家庭错误率 (FWER)）对于真正的非高斯场的有用性。研究人员提出的预测模型非常强调可解释性，并且可以与基于检测实验刺激和神经影像数据之间的相关性同时控制 FWER 或错误发现率 (FDR) 的常用方法进行比较。该项目的动机是需要灵活有效的统计程序来解释神经影像数据并确认从先前工作中得出的神经科学假设。此类神经影像数据的例子可以在社会神经科学等领域找到，该领域致力于根据人脑各个区域的激活和相互作用来理解和建模人类社会行为；神经经济学，旨在根据人脑模型解释和模拟人类的一些决策过程；以及精神分裂症等疾病模型，其目标是了解精神分裂症患者的大脑在解剖结构和功能上与非精神分裂症患者的大脑有何不同。事实上，所有神经影像数据都是所谓的“高通量”，这意味着记录了大量数据，通常仅针对一小部分人。需要统计工具从这些数据中产生一致且可重复的结果。该提案中的研究人员将开发可用于确认有关神经影像数据的现有假设的工具，以及通过决策过程的可解释预测模型生成假设。