Multi-group semi-blind ICA of fMRI

fMRI多组半盲ICA

• 批准号: 6728259
• 负责人: VINCE D CALHOUN
• 金额: $ 29.96万
• 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 2007-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6728259
• 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，用于分析多组（或多范式）功能磁共振成像 (fMRI) 数据。我们将使用提出的合成/分析模型[1,2]（以及随后开发的扩展）来检查 ICA 在视觉、听觉和运动范例的 fMRI 中的应用，以仔细研究 ICA 在应用于 fMRI 数据时的属性并纳入额外的先验信息。我们最近发布了一种将独立成分分析 (ICA) 应用于受试者组的方法 [3,4]。我们将扩展此方法以实现多个组的比较。在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时，使用 ICA 等灵活方法尤其重要，精神分裂症是一种弥漫性疾病，影响大脑功能的许多方面。我们将应用我们的方法重新分析之前用传统分析方法分析的数据，这些数据是在正常人和精神分裂症受试者执行听觉奇怪范式时获得的。拟议研究的具体目标是 1) 开发组间/范式间推理方法，其中 a) 纳入有关大脑源的先验信息，b) 提供受试者之间和受试者之间源大小的概率，c) 利用事后参数化来获取例如延迟值，d) 在预测先验时间过程或激活位置时启用假设检验。 2) 使用“基本”视觉/运动/听觉范式优化和验证 fMRI ICA，使用我们的 fMRI ICA 模型进行真实模拟，包含已知来源的“混合”数据集，并与假设血流动力学响应已知形式的 SPM99 方法进行比较。 3）应用我们的方法来分析正常和精神分裂症受试者执行听觉奇怪任务之前获得的和明确表征的数据，以及4）公开使用实施我们方法的Matlab软件包，该软件包可以单独使用，也可以作为广泛使用的fMRI分析包SPM的插件使用。