Multi-group semi-blind ICA of fMRI

fMRI多组半盲ICA

• 批准号: 7008841
• 负责人: VINCE D CALHOUN
• 金额: $ 17.02万
• 依托单位: HARTFORD HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2006-10-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7008841
• 关键词: auditory stimulus; bioimaging /biomedical imaging; brain imaging /visualization /scanning; clinical research; computational neuroscience; computer data analysis; computer program /software; computer simulation; computer system design /evaluation; functional magnetic resonance imaging; human subject; method development; statistics /biometry

DESCRIPTION (provided by applicant): The overall goal of this proposed research is to develop, evaluate the performance of, and optimize semi-blind independent component analysis (sblCA) methods for the analysis of multi-group (or multi-paradigm) functional magnetic resonance imaging (fMRI) data. We will examine the application of ICA to fMRI of visual, auditory, and motor paradigms using a proposed synthesis/analysis model [1,2] (and subsequently developed extensions of it) to carefully study the properties of ICA as it is applied to fMRI data and incorporate additional a priori information. We recently published a method for applying independent component analysis (ICA) to groups of subjects [3,4]. We will extend this method to enable comparisons of multiple groups. The use of flexible methods, such as ICA, is of _articular importance when studying cognitive tasks involving a distributed set of brain regions or when studying a disease like schizophrenia, which is known to be a diffuse disorder, affecting many aspects of brain function. We will apply our methods to reanalyze data, previously analyzed with traditional analysis methods, acquired while normal and schizophrenic subjects performed an auditory oddball paradigm. The specific aims of the proposed research are 1) To develop methods for inter-group/inter-paradigm inference which a) incorporate a priori information about the brain sources, b) provide the probability of source magnitude across and between subjects, c) utilize post hoc parameterization to obtain, e.g., latency valued, and d) enable hypothesis testing when a priori time courses or activation locations are predicted. 2) To optimize and validate ICA of fMRI using "basic" visual/motor/auditory paradigms, realistic simulations using our model for ICA of fMRI, "hybrid" data sets containing known sources, and comparisons with SPM99 methods assuming a known form for the hemodynamic response. 3) To apply our methods to analyze previously acquired and well characterized data of normal and schizophrenic subjects performing an auditory oddball task, and 4) To make publicly available, a Matlab software package implementing our methods which can be used stand alone or as a plug-in for a widely used fMRI analysis package, SPM.

描述（由申请人提供）： 这项研究的总体目标是开发，评估性能，并优化半盲独立成分分析（sblCA）方法的多组（或多范式）功能磁共振成像（fMRI）数据的分析。我们将使用一个建议的合成/分析模型[1，2]（以及随后开发的扩展模型）来研究伊卡在视觉、听觉和运动范式的fMRI中的应用，以仔细研究伊卡在应用于fMRI数据时的特性，并结合额外的先验信息。我们最近发表了一种将独立成分分析（伊卡）应用于受试者组的方法[3，4]。我们将扩展此方法以支持多个组的比较。当研究涉及一组分布式大脑区域的认知任务或研究精神分裂症等疾病（已知精神分裂症是一种弥漫性疾病，影响大脑功能的许多方面）时，使用灵活的方法（例如伊卡）非常重要。我们将应用我们的方法重新分析数据，以前分析与传统的分析方法，获得而正常和精神分裂症患者进行听觉oddball范式。提出的研究的具体目标是1）开发用于组间/范式间推断的方法，其a）结合关于脑源的先验信息，B）提供跨主体和主体之间的源幅度的概率，c）利用事后参数化来获得，例如，延迟值，以及d）当预测了先验时间过程或激活位置时能够进行假设检验。2)为了优化和验证伊卡的功能磁共振成像使用“基本”的视觉/运动/听觉范例，逼真的模拟使用我们的模型伊卡的功能磁共振成像，“混合”数据集包含已知的来源，并与SPM 99方法假设一个已知的形式的血流动力学反应的比较。3)应用我们的方法来分析以前获得的和良好的表征数据的正常和精神分裂症的主题执行听觉oddball任务，和4）公开提供，Matlab软件包实现我们的方法，可以单独使用或作为插件的广泛使用的功能磁共振成像分析包，SPM。