Validation of Structural/Functional MRI Localization

描述（由申请人提供）：本提案“计算功能解剖学结构和功能的验证”要求四年继续资助1R01-EB 00975 - 01。长期目标是继续开发计算解剖学（CA）方法，用于将功能性MRI（fMRI）活动分配到解剖坐标。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数据倾向于具有低信噪比和低空间分辨率。大脑的形状也存在相当大的基于生物学的个体差异，这是将fMRI活动与特定大脑区域相关联的重要混淆变量。个体可变性的这个问题的一个解决方案是通过在解剖学上将给定个体的激活约束到该个体的高分辨率皮层结构来变换个体的结构扫描内的功能扫描坐标。在之前的资助中，这是通过大变形几何度量图像映射（LDDMM图像）算法实现的，该算法增加了在感兴趣区域（ROI）分配fMRI信号的统计能力，例如分别在记忆和视觉任务中的内侧颞叶和枕叶皮层。我们的建议的第一个主要重点是通过ROI-LDDMM将功能信号直接分配到皮层坐标系，将我们以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，用于分割和重建多个皮质和皮质下结构，导致第二个主要焦点是将LDDMM与FreeSurfer分割相结合。MRIStudio也已成为分析白色物质解剖结构的强大工具，这导致第三个焦点是使LDDMM能够配准从扩散张量成像（DTI）数据导出的标量图像，以在量化白色物质解剖结构方面提供更大的统计功效。通过整合这些创新的保护性耕作工具，我们建议通过以下相互关联的具体目标，大大扩展我们的初始目标。目的1将验证ROI-LDDMM和Free Surfer-LDDMM的集成，用于研究多个皮层下结构的形状和分割。这将允许映射皮质下结构，如丘脑和基底神经节，并将应用于目标3。目的2将验证LDDMM在MRIStudio中的集成，以通过DTI数据的多通道LDDMM映射量化连接皮质下和皮质ROI的白色物质纤维束。这将允许对预定义ROI之间的纤维束中的白色物质完整性进行可靠评估，并应用于目标3。目标3将使用（a）视觉视网膜定位图、（b）认知任务和（c）使用DTI在这些功能定义区域之间的白色物质解剖结构来验证功能定义ROI和它们之间的结构白色物质特性的可靠性。经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MRISudio的C门户传播给神经影像学界。公共卫生相关性：激活结构的精确功能和结构划分将允许精确分析大脑中的功能激活。因此，视觉空间注意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理解神经发育和神经退行性疾病中的功能连接。

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.