Validation of Structural/Functional MRI Localization

结构/功能 MRI 定位的验证

• 批准号: 7731120
• 负责人: PATRICK E BARTA
• 金额: $ 35.87万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-23 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7731120
• 关键词: Algorithms; Anatomy; Anterior; Area; Atlases; Attention; Basal Ganglia; Behavior; Behavioral; Biomedical Informatics Research Network; Brain; Brain region; Cell Nucleus; Cerebral Peduncle; Cognitive; Communities; Computer software; Confounding Factors (Epidemiology); Corpus striatum structure; Data; Diffusion Magnetic Resonance Imaging; Fiber; Figs - dietary; Functional Magnetic Resonance Imaging; Funding; Generations; Globus Pallidus; Goals; Grant; Hippocampus (Brain); Image; Individual; Internal Capsule; Label; Lead; Limb structure; Lobule; Location; Magnetic Resonance Imaging; Maps; Measures; Medial; Memory; Methods; Metric; Modeling; Motor; Movement; Neurodegenerative Disorders; Noise; Occipital lobe; Parietal; Parietal Lobe; Population; Prefrontal Cortex; Property; Research; Resolution; Scanning; Shapes; Signal Transduction; Solutions; Source; Structure; Substantia nigra structure; Superior temporal gyrus; Surface; System; Temporal Lobe; Testing; Thalamic structure; Validation; Vision; Visual; Visuospatial; Work; base; brain shape; cingulate cortex; cognitive control; computational anatomy; fovea centralis; frontal eye fields; improved; innovation; interest; intraparietal sulcus; morphometry; neuroimaging; public health relevance; reconstruction; response; retinotopic; shape analysis; success; thalamocortical tract; tool; visual memory; white matter

DESCRIPTION (provided by applicant): This proposal on "Validation of Structure and Function in Computational Functional Anatomy" requests four years of continued funding for grant 1R01-EB00975-01. The long-term goal continues to develop Computational Anatomy (CA) methods for assigning functional MRI (fMRI) activity to anatomical coordinates. A central issue in fMRI research is the problem of precisely localizing regions of activation and associating these regions with anatomical labels. fMRI data tend to have both a low signal-to-noise ratio and a low spatial resolution compared with structural MRI data. There is also considerable biologically-based individual variability in the shape of the brain that is a significant confounding variable in associating fMRI activity with a specific brain region. One solution to this problem of individual variability is to transform the functional scan coordinates within the individual's structural scan by constraining anatomically the activation for a given individual to that individual's high resolution cortical structure. In the previous grant, this was achieved via the Large Deformation Diffeomorphic Metric Image Mapping (LDDMM Image) algorithm which increased the statistical power of assigning fMRI signals in a region of interests (ROI) such as the medial temporal lobe and the occipital cortex in memory and visual tasks respectively. The first major focus of our proposal is on the direct assignment of functional signals to cortical coordinate systems via ROI-LDDMM by extending our previous work to memory and vision activity in multiple and connected structures. FreeSurfer has emerged as a powerful tool for parcellating and reconstructing multiple cortical and subcortical structures leading to the second major focus of integrating LDDMM with FreeSurfer parcellation. MRIStudio has also emerged as a powerful tool for analyzing white matter anatomy leading to the third focus of enabling LDDMM to register scalar images derived from diffusion tensor imaging (DTI) data to provide greater statistical power in quantification of white matter anatomy. By integrating these innovative CA tools, we propose to significantly expand upon our initial goals via the following interrelated specific aims. Aim 1 will validate ROI-LDDMM and integration of Free Surfer-LDDMM for studying shape and segmentation of multiple subcortical structures. This will permit mapping of subcortical structures such as the thalamus and basal ganglia and will be applied in Aim 3. Aim 2 will validate the integration of LDDMM in MRIStudio for quantifying white matter fiber tracts connecting subcortical and cortical ROIs via multi-channel LDDMM mapping of DTI data. This will permit reliable assessment of white matter integrity in fiber tracts between predefined ROIs and be applied in Aim 3. Aim 3 will validate the reliability of functionally defined ROIs and structural white matter properties between them using (a) visual retinotopic mapping, (b) cognitive tasks, and (c) white matter anatomy between these functionally defined regions using DTI. The validated tools will be disseminated to the neuroimaging community under the auspices of the Biomedical Informatics Research Network (BIRN) via the C portal for LDDMM and MRIStudio. PUBLIC HEALTH RELEVANCE: Accurate functional and structural parcellation of activated structures will permit precise analysis of functional activation in the brain. Thus accurate location of brain activation in visuospatial attention and cognitive control will permit neuroscientists and clinicians greater understanding of functional connectivity in neurodevelopmental and neurodegenerative disorders.

描述（由申请人提供）：这项关于“计算功能解剖学中结构和功能的验证”的提案要求持续资助四年，拨款号为 1R01-EB00975-01。长期目标继续开发计算解剖学 (CA) 方法，将功能 MRI (fMRI) 活动分配给解剖坐标。功能磁共振成像研究的一个中心问题是精确定位激活区域并将这些区域与解剖标签相关联的问题。与结构 MRI 数据相比，fMRI 数据往往具有较低的信噪比和较低的空间分辨率。大脑形状也存在相当大的基于生物学的个体差异，这是将功能磁共振成像活动与特定大脑区域联系起来的一个重要的混杂变量。解决个体变异性问题的一种方法是通过在解剖学上将给定个体的激活限制到该个体的高分辨率皮质结构来转换个体结构扫描内的功能扫描坐标。在之前的资助中，这是通过大变形微分形度量图像映射（LDDMM图像）算法实现的，该算法提高了在记忆和视觉任务中分别在内侧颞叶和枕叶皮层等感兴趣区域（ROI）分配fMRI信号的统计能力。我们提案的第一个主要焦点是通过 ROI-LDDMM 将功能信号直接分配到皮质坐标系，将我们之前的工作扩展到多个连接结构中的记忆和视觉活动。 FreeSurfer 已成为分割和重建多个皮质和皮质下结构的强大工具，这导致了将 LDDMM 与 FreeSurfer 分割相集成的第二个主要焦点。 MRIStudio 也已成为分析白质解剖结构的强大工具，从而使 LDDMM 能够注册源自扩散张量成像 (DTI) 数据的标量图像，从而在白质解剖结构的量化方面提供更大的统计能力。通过整合这些创新的 CA 工具，我们建议通过以下相互关联的具体目标来显着扩展我们的初始目标。目标 1 将验证 ROI-LDDMM 和 Free Surfer-LDDMM 的集成，以研究多个皮层下结构的形状和分割。这将允许绘制丘脑和基底神经节等皮层下结构的图，并将应用于目标 3。目标 2 将验证 LDDMM 在 MRIStudio 中的集成，以通过 DTI 数据的多通道 LDDMM 图来量化连接皮层下和皮层 ROI 的白质纤维束。这将允许对预定义 ROI 之间的纤维束中的白质完整性进行可靠评估，并应用于目标 3。目标 3 将使用 (a) 视觉视网膜专题图、(b) 认知任务和 (c) 使用 DTI 对这些功能定义区域之间的白质解剖来验证功能定义的 ROI 和它们之间的结构白质特性的可靠性。经过验证的工具将在生物医学信息学研究网络 (BIRN) 的支持下通过 LDDMM 和 MRIStudio 的 C 门户传播到神经影像界。公共健康相关性：激活结构的准确功能和结构分割将允许精确分析大脑的功能激活。因此，视觉空间注意力和认知控制中大脑激活的准确定位将使神经科学家和临床医生更好地了解神经发育和神经退行性疾病的功能连接。