Validation of Structural/Functional MRI Localization

描述（由申请人提供）：这份关于“计算功能解剖学中结构和功能的验证”的提案请求持续四年的资助1R01-EB00975-01。长期目标是继续发展计算解剖学（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核心问题是精确定位激活区域并将这些区域与解剖学标签联系起来的问题。与结构MRI数据相比，功能磁共振成像数据往往具有低信噪比和低空间分辨率的特点。在将fMRI活动与特定脑区联系起来时，大脑形状也存在相当大的基于生物学的个体差异，这是一个重要的混淆变量。解决个体差异性问题的一种方法是，通过解剖学上限制特定个体的激活到该个体的高分辨率皮质结构，从而在个体的结构扫描中转换功能扫描坐标。在之前的研究中，这是通过大变形差分对称度量图像映射（LDDMM图像）算法实现的，该算法增加了在兴趣区域（ROI）分配fMRI信号的统计能力，如记忆和视觉任务中的内侧颞叶和枕叶皮层。我们建议的第一个主要重点是通过ROI-LDDMM将功能信号直接分配给皮质坐标系统，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已经成为一个强大的工具，可以对多个皮质和皮质下结构进行包切和重建，这导致了将LDDMM与FreeSurfer包切相结合的第二个主要焦点。MRIStudio也成为分析白质解剖的强大工具，导致LDDMM能够注册来自扩散张量成像（DTI）数据的标量图像，从而为白质解剖的量化提供更大的统计能力。通过集成这些创新的CA工具，我们建议通过以下相互关联的具体目标来显著扩展我们的初始目标。目的1将验证ROI-LDDMM和Free surf - lddmm的集成，用于研究多个皮质下结构的形状和分割。这将允许绘制皮层下结构，如丘脑和基底神经节，并将在Aim 3中应用。目的2将验证LDDMM在MRIStudio中的集成，通过DTI数据的多通道LDDMM映射来量化连接皮层下和皮层roi的白质纤维束。这将允许在预定义roi之间的纤维束中可靠地评估白质完整性，并应用于Aim 3。目的3将使用(a)视觉视网膜定位图，(b)认知任务，以及(c)使用DTI在这些功能定义区域之间进行白质解剖，验证功能定义roi和它们之间结构白质属性的可靠性。经过验证的工具将在生物医学信息学研究网络（BIRN）的主持下，通过LDDMM和MRIStudio的C门户网站传播给神经成像界。公共卫生相关性：激活结构的准确功能和结构分割将允许对大脑功能激活进行精确分析。因此，准确定位大脑在视觉空间注意和认知控制中的激活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。

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.