FUNCTIONAL MRI DATA AQUISITION AND ANALYSIS

功能 MRI 数据采集和分析

基本信息

  • 批准号:
    8171700
  • 负责人:
  • 金额:
    $ 30.18万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2001
  • 资助国家:
    美国
  • 起止时间:
    2001-09-30 至 2011-08-31
  • 项目状态:
    已结题

项目摘要

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.
该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金, 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说,它不一定是研究者的机构。 该资源在功能磁共振成像方面的总体目标一直是并且仍然是为了增强特异性和 通过改进数据采集和分析来提高人体功能 MRI 的灵敏度。在之前的补助金中 在此期间,该 TRD 的目标集中于开发基于灌注的 fMRI 采集方法和 基于独立成分分析 (ICA) 的 fMRI 分析方法。 作为第一个目标的一部分,我们开发了 使用脑血容量 (CBV) 依赖性对比进行功能磁共振成像的新方法,称为“血管空间” 占用率 (VASO)”fMRI。这种方法不需要使用造影剂,但已被证明具有 与血氧水平依赖性 (BOLD) fMRI 相比,空间特异性更高。为了第二个目标,我们 开发了新的独立成分分析(ICA)方法来进行功能磁共振成像数据分析,并证明 ICA 可以发现标准方法忽视的大脑激活,即使在时间安排上也可以产生可靠的结果 大脑激活的情况与研究者的预期并不完全一致。 在此更新应用程序中,该 TRD 将重点关注功能磁共振成像采集和分析开发,以解决 我们的合作者面临的几个问题(见下页表 1)。 例如,我们的儿科合作者 研究多种发育障碍,如多动症、自闭症、阅读障碍和创伤性障碍 功能缺陷必须应对合规性降低的问题,并希望扫描速度更快。类似的情况也适用于 痴呆症和精神病患者。此外,这些研究人员和研究人员研究的效果 研究记忆功能和注意力通常包括在更鲁棒的信号调制之上的非常小的信号调制 信号激活(视觉、运动)。一些研究人员希望获得更高的空间分辨率,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 可以提高信噪比。 我们的合作者面临的另一个问题是传统的功能磁共振成像数据分析仅限于预先设想的情况 血流动力学反应,这可能会错过重要的潜在大脑活动。我们已经解决了这个问题 独立成分分析的“数据驱动”方法,该方法揭示了一些额外的内容 激活组件。然而,这引发了有关这些内容的含义和起源的根本问题。 标准功能磁共振成像数据分析中不存在的“额外”激活成分。因此,这需要评估 fMRI 结果的此类 ICA 的特异性。最后,我们的许多合作者正在儿童中进行功能磁共振成像研究 以及患有神经退行性疾病的患者。 这些数据分析起来可能存在问题,因为此类研究 参与者可能表现出对实验范式的遵从性较差。最终的例子可能是 克里斯滕森医生的昏迷患者。 ICA 允许使用范式进行研究,从而减少对 合规性,包括所谓的丰富的自然行为,例如玩电子游戏或看电影,或者 昏迷的情况下,只是听亲戚说话。 因此,我们未来一段时间的总体目标是提高功能磁共振成像的敏感性,解决实验问题 与 fMRI-ICA 特异性相关,并开发用于基础和临床研究的功能性脑图谱方法 这减少了对参与者合规性的要求。具体目标是: 目标 1. 优化 7.0 特斯拉的 fMRI 数据采集。 我们将在并行成像加速因子、匀场、TE 方面优化 7.0 T 的 fMRI 采集 TR 选择和切片数量,具体取决于我们神经科学合作者的个人需求。 目标 2. 表征 fMRI 数据的独立成分。 为了表征功能磁共振成像数据的独立组成部分,我们将获取额外的图像数据,包括 BOLD 以更高的时间和空间分辨率进行 fMRI 采集,使用不同对比度进行 fMRI 采集,即 VASO 和动脉自旋标记 (ASL),以及结构成像,包括 MP-RAGE 和 MR 血管造影。我们将使用 在三个场强(1.5、3.0 和 7.0 特斯拉)下采集的数据,以评估中发现的独立组件 大胆的功能磁共振成像数据。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。 目标 3. 开发来自丰富自然行为的 fMRI 数据的 ICA 方法。 我们将开发先进的 ICA 方法来分析从事丰富自然主义研究的人员的 fMRI 数据 行为,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 工作相结合 用于连接功能评估。

项目成果

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JAMES J. PEKAR其他文献

JAMES J. PEKAR的其他文献

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{{ truncateString('JAMES J. PEKAR', 18)}}的其他基金

7/24 Healthy Brain and Child Development National Consortium
7/24 健康大脑和儿童发展国家联盟
  • 批准号:
    10494267
  • 财政年份:
    2021
  • 资助金额:
    $ 30.18万
  • 项目类别:
7/24 Healthy Brain and Child Development National Consortium
7/24 健康大脑和儿童发展国家联盟
  • 批准号:
    10665811
  • 财政年份:
    2021
  • 资助金额:
    $ 30.18万
  • 项目类别:
7/24 Healthy Brain and Child Development National Consortium
7/24 健康大脑和儿童发展国家联盟
  • 批准号:
    10750125
  • 财政年份:
    2021
  • 资助金额:
    $ 30.18万
  • 项目类别:
Gastric Electrical Slow Wave Functional MRI of the Human Brain
人脑胃电慢波功能 MRI
  • 批准号:
    10039901
  • 财政年份:
    2020
  • 资助金额:
    $ 30.18万
  • 项目类别:
FUNCTIONAL MRI DATA AQUISITION AND ANALYSIS
功能 MRI 数据采集和分析
  • 批准号:
    7602569
  • 财政年份:
    2007
  • 资助金额:
    $ 30.18万
  • 项目类别:
TESLA MRI SCANNER: SLEEP EYE MOVEMENT
特斯拉 MRI 扫描仪:睡眠眼动
  • 批准号:
    7335119
  • 财政年份:
    2006
  • 资助金额:
    $ 30.18万
  • 项目类别:
TESLA MRI SCANNER: SCHIZOPHRENIA, ADHD, BRAIN TUMOR, RETT SYNDROME
特斯拉 MRI 扫描仪:精神分裂症、多动症、脑肿瘤、RETT 综合征
  • 批准号:
    7335116
  • 财政年份:
    2006
  • 资助金额:
    $ 30.18万
  • 项目类别:
QUANTITATIVE PHYSIOLOGY & FUNCTIONAL MRI
定量生理学
  • 批准号:
    7420409
  • 财政年份:
    2006
  • 资助金额:
    $ 30.18万
  • 项目类别:
TESLA MRI SCANNER: PEROXISOMAL DISORDERS, MULTIPLE SCLEROSIS
特斯拉 MRI 扫描仪:过氧化物酶体疾病、多发性硬化症
  • 批准号:
    7335118
  • 财政年份:
    2006
  • 资助金额:
    $ 30.18万
  • 项目类别:
TESLA MRI SCANNER: NEURAL DISORDERS
特斯拉 MRI 扫描仪:神经疾病
  • 批准号:
    7335115
  • 财政年份:
    2006
  • 资助金额:
    $ 30.18万
  • 项目类别:

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