Multi-group semi-blind ICA of fMRI

fMRI多组半盲ICA

• 批准号: 7328978
• 负责人: VINCE D CALHOUN
• 金额: $ 16.85万
• 依托单位: THE MIND RESEARCH NETWORK
• 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 2008-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7328978
• 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、评估和优化用于分析多组(或多范例)功能磁共振成像(FMRI)数据的半盲独立分量分析(SblCA)方法。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模型)来研究ICA在视觉、听觉和运动范例的fMRI中的应用，以仔细研究ICA的属性，因为它应用于fMRI数据并纳入额外的先验信息。我们最近发表了一种将独立分量分析(ICA)应用于受试组的方法[3，4]。我们将扩展此方法以实现多个组的比较。使用灵活的方法，如ICA，在研究涉及一组分布的大脑区域的认知任务或研究像精神分裂症这样的疾病时具有特别重要的意义，精神分裂症是一种广为人知的弥漫性疾病，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以前用传统分析方法分析的数据，这些数据是在正常受试者和精神分裂症受试者执行奇怪的听觉范式时获得的。所提出的研究的具体目标是1)开发用于组间/范式间推理的方法，其中a)结合关于脑源的先验信息，b)提供跨受试者和在受试者之间的源大小的概率，c)利用后自组织参数化来获得例如潜伏值，以及d)在预测先验时间进程或激活位置时启用假设检验。2)使用“基本”视觉/运动/听觉范例、使用我们的模型对fMRI的ICA进行逼真的模拟、包含已知源的“混合”数据集以及与采用已知形式的血流动力学响应的SPM99方法的比较，来优化和验证fMRI的ICA。3)应用我们的方法来分析执行听觉古怪任务的正常受试者和精神分裂症受试者先前获得的和特征良好的数据，以及4)使实现我们的方法的MatLab软件包公开可用，该软件包可以独立使用或作为广泛使用的fMRI分析软件包SPM的插件。