FUNCTIONAL MRI DATA AQUISITION AND ANALYSIS

功能 MRI 数据采集和分析

• 批准号: 7957321
• 负责人: JAMES J. PEKAR
• 金额: $ 30.45万
• 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 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7957321
• 关键词: Acceleration; Address; Angiography; Area; Attention; Attention deficit hyperactivity disorder; Autistic Disorder; Behavior; Blood Vessels; Blood Volume; Brain; Brain Mapping; Cerebrum; Child; Childhood; Clinical Research; Coma; Computer Retrieval of Information on Scientific Projects Database; Contrast Media; Data; Data Analyses; Degenerative Disorder; Dementia; Development; Functional Magnetic Resonance Imaging; Funding; Goals; Grant; Human; Image; Individual; Institution; Joints; Memory; Methodology; Methods; Neurosciences; Noise; Participant; Patients; Perfusion; Persons; Play; Psychotic Disorders; Reading Disabilities; Relative (related person); Research; 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; data acquisition; developmental disease; hemodynamics; independent component analysis; magnetic field; movie; novel; response; visual motor; Acceleration; Address; Angiography; Area; Attention; Attention deficit hyperactivity disorder; Autistic Disorder; Behavior; Blood Vessels; Blood Volume; Brain; Brain Mapping; Cerebrum; Child; Childhood; Clinical Research; Coma; Computer Retrieval of Information on Scientific Projects Database; Contrast Media; Data; Data Analyses; Degenerative Disorder; Dementia; Development; Functional Magnetic Resonance Imaging; Funding; Goals; Grant; Human; Image; Individual; Institution; Joints; Memory; Methodology; Methods; Neurosciences; Noise; Participant; Patients; Perfusion; Persons; Play; Psychotic Disorders; Reading Disabilities; Relative (related person); Research; 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; 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 关于fMRI，该资源的总体目标已经并且仍然是为了增强特异性和 通过改进数据获取和分析，人类功能MRI的敏感性。在上一个赠款中 时期，该TRD的目的专注于基于灌注fMRI获取方法的发展， 基于fMRI分析的基于独立组件分析（ICA）的方法。 作为第一个目标的一部分，我们开发了 使用脑血体积（CBV）依赖性对比度进行fMRI的新方法，称为“血管空间 占用（vaso）“ fMRI。这种方法不需要使用造影剂，已显示为 与血液氧合水平依赖性（BOLD）fMRI相比，空间特异性提高。对于第二个目标，我们 开发了新的独立组件分析（ICA）来fMRI数据分析，并证明了这一点 ICA可以找到标准方法忽略的大脑激活，即使时间安排也可以产生强大的结果 大脑的激活与研究者预期的不完全匹配。 在此续订应用程序中，此TRD将重点放在fMRI获取和分析发展上，以解决 我们的合作者面临的几个问题（请参见表1，下一页）。 例如，我们的儿科合作者 研究多种发育障碍，例如ADHD，自闭症，阅读障碍和基于创伤 功能不足必须处理降低的合规性，并希望更快地扫描。类似的情况是正确的 痴呆和精神病患者。此外，这些研究者和研究人员研究的效果 研究记忆功能和注意力通常由更健壮的基于非常小的信号调制组成 信号激活（视觉，电动机）。一些研究人员希望更高的空间分辨率更好地研究小型 皮质区域。 所有这些问题都可以通过进入更高的磁场强度（7.0t）来减少 可以增加信号到噪声。 我们合作者面临的另一个问题是常规fMRI数据分析的限制 血液动力学反应可能会错过重要的潜在大脑活动。我们已经通过去解决这个问题 独立组件分析的“数据驱动”方法，该方法揭示了几个其他 激活成分。但是，这导致了有关这些的含义和起源的基本问题 标准fMRI数据分析中不存在“额外”激活组件。因此，这需要评估 fMRI的此类ICA的特异性结果。最后，我们的许多合作者都在追求儿童的fMRI研究 以及神经脱生疾病的患者。 这些数据可以分析有问题，因为此类研究 参与者可能表现出依从性范式不佳。最终的例子可能是 克里斯滕森博士的昏迷患者。 ICA允许通过范式进行研究，以减少对 合规性，包括所谓的丰富自然主义行为，例如玩电子游戏或看电影或在 昏迷的情况，只是听亲戚说话。 因此，我们在未来时期的总体目标是提高fMRI的敏感性，以解决实验问题 与FMRI-KIA特异性有关，并开发用于基础研究和临床研究的功能性脑映射的方法 这减少了对参与者合规性的需求。具体目的是： AIM 1。优化7.0 Tesla的fMRI数据采集。 我们将在7.0 t方面优化fMRI获取，相对于平行成像加速度因子，张开，TE 以及TR选择和切片数字，具体取决于我们神经科学合作者的个人需求。 AIM 2。表征fMRI数据的独立组件。 为了表征fMRI数据的独立组件，我们将获取其他图像数据，包括粗体数据 在较高的时间和空间分辨率的fMRI获取，fMRI采集使用不同的对比度，即 血管和动脉自旋标记（ASL）以及包括MP-RAGE和MR血管造影的结构成像。我们将使用 以三个场强（1.5、3.0和7.0特斯拉）获取的数据，以评估在 大胆的fMRI数据。这些数据将使用包括基于功能的联合ICA在内的多种方法进行分析。 目标3。从丰富的自然主义行为中开发出用于fMRI数据的ICA方法。 我们将开发高级ICA方法，以分析从事富裕自然主义者的fMRI数据 行为，并与我们的合作者合作，将这些方法应用于其研究目的。这将是为 来自个人和群体的数据。这些方法最终将与TRD 3中的DTI努力相结合 用于连接功能评估。