Validation of Structural/Functional MRI Localization

描述(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”的提案要求继续资助1R01-EB00975-01，为期四年。长期目标是继续开发计算解剖学(CA)方法，将功能磁共振(FMRI)活动分配到解剖坐标。功能磁共振成像研究中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记相关联。与结构性MRI数据相比，fMRI数据往往具有低信噪比和低空间分辨率。大脑的形状也有相当大的基于生物的个体变异性，这是将fMRI活动与特定大脑区域联系起来的一个重大混杂变量。个体可变性问题的一种解决方案是通过在解剖学上将给定个体的激活限制在该个体的高分辨率皮质结构上来转换个体结构扫描中的功能扫描坐标。在之前的拨款中，这是通过大变形微分度量图像映射(LDDMM Image)算法实现的，该算法增加了在记忆和视觉任务中分别在内侧颞叶和枕叶等感兴趣区(ROI)分配fMRI信号的统计能力。我们建议的第一个主要焦点是通过ROI-LDDMM将我们之前的工作扩展到多个相连结构中的记忆和视觉活动，从而将功能信号直接分配到皮质坐标系。Freesurfer已经成为分割和重建多个皮质和皮质下结构的强大工具，导致了将LDDMM与freesurfer分割相结合的第二个主要焦点。MRIStudio也已成为分析白质解剖的强大工具，导致了第三个重点，即使LDDMM能够记录来自扩散张量成像(DTI)数据的标量图像，以在白质解剖的量化方面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显著扩展我们的初始目标。AIM 1将验证ROI-LDDMM和Free Surfer-LDDMM的集成，以研究多个皮质下结构的形状和分割。这将允许绘制皮质下结构，如丘脑和基底节，并将应用于AIM 3。AIM 2将验证LDDMM在MRIStudio中的集成，通过DTI数据的多通道LDDMM映射，量化连接皮质下和皮质ROI的白质纤维束。这将允许对预定义的ROI之间的纤维束中的白质完整性进行可靠的评估，并应用于AIM 3。AIM 3将使用(A)视觉视网膜定位图、(B)认知任务和(C)使用DTI对这些功能定义的区域之间的白质解剖进行验证，以验证功能定义的ROI的可靠性和它们之间的结构白质属性。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门户网站分发给神经成像社区。与公共健康相关：对激活的结构进行准确的功能和结构分割将使精确分析大脑中的功能激活成为可能。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.