FUNCTIONAL MRI DATA AQUISITION AND ANALYSIS

功能 MRI 数据采集和分析

• 批准号: 8364122
• 负责人: JAMES J. PEKAR
• 金额: $ 35.58万
• 依托单位: HUGO W. MOSER RES INST KENNEDY KRIEGER
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-30 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8364122
• 关键词: Acceleration; Address; Angiography; Area; Attention; Attention deficit hyperactivity disorder; Autistic Disorder; Basic Science; Behavior; Blood Vessels; Blood Volume; Brain; Brain Mapping; Cerebrum; Child; Childhood; Clinical Research; Coma; Contrast Media; Data; Data Analyses; Degenerative Disorder; Dementia; Development; Functional Magnetic Resonance Imaging; Funding; Goals; Grant; Human; Image; Individual; Joints; Memory; Methodology; Methods; National Center for Research Resources; Neurosciences; Noise; Participant; Patients; Perfusion; Persons; Play; Principal Investigator; Psychotic Disorders; Reading Disabilities; Relative (related person); Research; Research Infrastructure; Research Personnel; Resolution; Resources; Scanning; Sensitivity and Specificity; Signal Transduction; Slice; Source; Specificity; Spin Labels; Time; Trauma; United States National Institutes of Health; Video Games; Work; base; blood oxygen level dependent; cost; data acquisition; developmental disease; hemodynamics; independent component analysis; magnetic field; movie; novel; response; visual motor

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The overall goals of this resource, with respect to fMRI, have been and remain to enhance the specificity and sensitivity of human functional MRI through improvements in data acquisition and analysis. In the previous grant period, the aims of this TRD were focused on the development of perfusion-based fMRI acquisition methods and Independent Component Analysis (ICA) based methods for fMRI analysis. As part of the first aim, we developed a novel method for performing fMRI using cerebral blood volume (CBV) dependent contrast, dubbed "Vascular Space Occupancy (VASO)" fMRI. This approach, which does not require the use of a contrast agent, was shown to have increased spatial specificity compared to Blood Oxygenation Level Dependent (BOLD) fMRI. For the second aim, we developed new Independent Component Analysis (ICA) approaches to fMRI data analysis, and demonstrated that ICA can find brain activation overlooked by standard approaches and can yield robust results even when the timing of brain activation does not precisely match that anticipated by the investigator. In this renewal application, this TRD will focus on fMRI acquisition and analysis developments that will address several issues confronting our collaborators (see Table 1, next page). For instance, our pediatric collaborators studying a variety of developmental disorders such as ADHD, autism, reading disability, and trauma-based functional deficits have to deal with reduced compliance and would like to scan faster. A similar situation is true for patients with dementia and psychosis. In addition, the effects studied by these investigators and by researchers studying memory function and attention often consist of very small signal modulations on top of more robust signal activations (visual, motor). Some investigators would like higher spatial resolution to better study small cortical areas. All of these problems can be reduced by going to higher magnetic field strength (7.0T), where increased signal to noise is available. Another issue confronting our collaborators is the limitation of conventional fMRI data analysis to pre-conceived hemodynamic responses, which may miss important underlying brain activities. We have addressed this by going to the "data-driven" methodology of independent component analysis, which has revealed several additional activation components. However, this has led to fundamental questions about the meaning and origin of these "extra" activation components not present in standard fMRI data analyses. This therefore requires assessment of the specificity of such ICA of fMRI results. Finally, many of our collaborators are pursuing fMRI studies in children and in patients with neuro-degenerative disease. These data can be problematic to analyze, as such research participants may show poor compliance with experimental paradigms. The ultimate example of this may be the coma patients of Dr. Christensen. ICA allows studies to be performed with paradigms that reduce demands on compliance, including such so-called rich naturalistic behaviors as playing a video game or watching a movie or, in the case of coma, just listening to a relative talking. Our overall goals in the coming period are therefore to enhance fMRI sensitivity, to address experimental questions related to fMRI-ICA specificity, and to develop approaches to functional brain mapping for basic and clinical research which reduce demands on participant compliance. The specific aims are: AIM 1. Optimize fMRI data acquisition at 7.0 Tesla. We will optimize fMRI acquisitions at 7.0 T with respect to parallel imaging acceleration factor, shimming, TE and TR choice, and slice number, depending on the individual needs for our neuroscience collaborators. AIM 2. Characterize the independent components of fMRI data. To characterize the independent components of fMRI data, we will acquire additional image data, including BOLD fMRI acquisitions at higher temporal and spatial resolution, fMRI acquisitions using different contrasts, namely VASO and arterial spin labeling (ASL), and structural imaging including MP-RAGE and MR angiography. We will use data acquired at three field strengths (1.5, 3.0, and 7.0 Tesla), to assess the independent components found in BOLD fMRI data. These data will be analyzed using multiple approaches including feature-based joint ICA. AIM 3. Develop ICA methods for fMRI data from rich naturalistic behaviors. We will develop advanced ICA methods for analysis of fMRI data from persons engaged in rich naturalistic behaviors, and work with our collaborators to apply these approaches to their research aims. This will be done for data from both individuals and groups. These approaches will ultimately be combined with the DTI efforts in TRD 3 for connectivity-function assessment.

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 关于功能磁共振成像，这一资源的总体目标一直是并将继续增强特异性和 通过改进数据采集和分析来提高人体功能磁共振成像的灵敏度。在上一次拨款中 期间，TRD的目标集中在基于灌注的fMRI采集方法的发展和 基于独立分量分析(ICA)的fMRI分析方法。作为第一个目标的一部分，我们开发了一个 使用脑血容量(CBV)依赖的对比度进行功能磁共振成像的新方法，称为“血管空间” 这种方法，不需要使用造影剂，被证明具有 与依赖血氧水平(BOLD)的fMRI相比，空间特异性增强。对于第二个目标，我们 开发了用于功能磁共振数据分析的新的独立分量分析(ICA)方法，并证明了 ICA可以发现标准方法忽略的大脑激活，即使在计时时也可以产生稳健的结果 大脑激活的结果并不完全符合研究人员的预期。 在此续订申请中，此TRD将专注于fMRI采集和分析开发，这些开发将解决 我们的合作者面临的几个问题(请参见下一页的表1)。例如，我们的儿科合作者 研究各种发育障碍，如ADHD、自闭症、阅读障碍和基于创伤的 功能缺陷必须解决合规性降低的问题，并希望扫描得更快。类似的情况也适用于 痴呆症和精神病患者。此外，这些调查人员和研究人员研究的影响 研究记忆功能和注意力通常由非常小的信号调制和更健壮的信号组成 信号激活(视觉、运动)。一些研究人员希望更高的空间分辨率，以便更好地研究小 皮质区域。所有这些问题都可以通过提高磁场强度(7.0T)来减少，其中 可提高信噪比。 我们的合作者面临的另一个问题是传统的fMRI数据分析仅限于预先设想的 血液动力学反应，这可能会错过重要的潜在大脑活动。我们已经通过以下方式解决了这个问题 “数据驱动”的独立成分分析方法，它揭示了其他几个 激活组件。然而，这导致了关于这些词的含义和起源的根本问题 标准的fMRI数据分析中不存在“额外的”激活成分。因此，这需要评估 这种ICA对fMRI结果的特异性。最后，我们的许多合作者正在进行儿童功能磁共振研究 以及患有神经退行性疾病的患者。分析这些数据可能会有问题，比如这样的研究 参与者可能会表现出对实验范式的较差遵从性。这方面的终极示例可能是 克里斯滕森博士的昏迷病人。ICA允许使用范式执行研究，以减少对 合规，包括所谓的丰富的自然主义行为，如玩电子游戏或看电影，或者在 昏迷的情况下，只是听亲戚说话。 因此，我们在未来一段时间的总体目标是提高fmri的灵敏度，解决实验问题。 与fMRI-ICA特异性相关，并为基础和临床研究开发脑功能图谱的方法 这降低了对参与者合规性的要求。具体目标是： 目的1.优化7.0特斯拉fMRI数据采集。 我们将针对并行成像加速系数、垫片、TE来优化7.0 T的fMRI采集 根据我们的神经科学合作者的个人需求，选择TR和切片数量。 目的2.表征fMRI数据的独立成分。 为了表征fMRI数据的独立分量，我们将获取其他图像数据，包括粗体 时间和空间分辨率更高的fMRI采集，使用不同对比度的fMRI采集，即 VASO和动脉自旋标记(ASL)，结构成像，包括MP-RAGE和MR血管成像。我们将使用 在三种场强(1.5、3.0和7.0特斯拉)下获取的数据，以评估 大胆的功能磁共振数据。这些数据将使用多种方法进行分析，包括基于特征的联合ICA。 目的3.建立从丰富的自然行为中提取的fMRI数据的独立成分分析方法。 我们将开发先进的ICA方法来分析来自从事丰富自然主义者的fMRI数据 并与我们的合作者合作，将这些方法应用于他们的研究目标。这将是为了 来自个人和团体的数据。这些方法最终将与TRD 3中的DTI努力结合起来 用于连接功能评估。