Validation of Structural/Functional MRI Localization

总结 关于“计算功能解剖学中结构和功能的验证”的建议要求四个 1 R 01-EB 00975 -01年的持续资助。长期目标持续发展 计算解剖学（CA）方法，用于将功能性MRI（fMRI）活动分配给解剖坐标。一 功能磁共振成像研究的中心问题是精确定位激活区域并将这些区域联系起来的问题 带有解剖标签的区域。fMRI数据往往具有低信噪比和低空间分辨率， 分辨率与结构MRI数据相比。也有相当多的生物基础的个人 大脑形状的变异性，这是一个重要的混淆变量，在关联功能磁共振成像活动与 特定的脑区个体差异性问题的一个解决方案是将功能扫描 通过在解剖学上约束针对给定结构的激活， 个体与个体高分辨率皮层结构的关系。在以前的赠款中，这是通过 大变形同构度量图像映射（LDDMM Image）算法， 在感兴趣区域（ROI）（如内侧颞叶）分配fMRI信号的统计功效， 枕叶皮层在记忆和视觉任务中的作用。 我们建议的第一个主要焦点是功能信号到皮层坐标的直接分配 系统通过ROI-LDDMM扩展我们以前的工作，以记忆和视觉活动在多个和 连接结构。FreeSurfer已经成为一个强大的工具，用于分割和重建多个 皮质和皮质下结构导致LDDMM与FreeSurfer集成的第二个主要焦点 包裹MRIStudio也已成为分析白色物质解剖结构的强大工具， 使LDDMM能够将从扩散张量成像（DTI）数据导出的标量图像配准到 在量化白色物质解剖结构中提供更大的统计功效。 通过集成这些创新的CA工具，我们建议通过 遵循相互关联的具体目标。目标1将确认ROI-LDDMM和Free Surfer-LDDMM的集成， 研究多个皮层下结构的形状和分割。这将允许映射皮层下 结构，如丘脑和基底神经节，并将应用于目标3。目标2将验证 将LDDMM集成到MRI Studio中，用于量化连接皮质下和皮质的白色纤维束 通过DTI数据的多通道LDDMM映射的ROI。这将允许可靠地评估白色物质 在预定义的ROI之间的纤维束的完整性，并应用于目标3。目标3将验证 功能上定义的ROI和它们之间的结构白色物质性质，使用（a）视觉视网膜定位 映射，（B）认知任务，和（c）这些功能定义的区域之间的白色物质解剖，使用 DTI。经验证的工具将在 生物医学信息学研究网络（BIRN）通过LDDMM和MRIStudio的CA门户网站。

Summary 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 CA portal for LDDMM and MRIStudio.