Automated Assessment of Structural Changes & Functional Recovery Post Spinal Inju

描述（由申请人提供）：建立结构-功能相关性是理解信息如何在中枢神经系统（CNS）中处理的基础。轴突连接是中枢神经系统内促进信息传递和接收的关键关系。最近，扩散加权磁共振成像（DW-MRI）方法已被证明可以提供观察结构连通性所需的基本信息，并可以在体内可视化中枢神经系统中的纤维束。在这个项目中，我们建议开发从高角分辨率扩散加权图像（HARDI）中提取和分析这些模式的方法，这些图像已知比扩散张量成像（DTI）具有更好的分辨率。为此，我们选择了一种具有生物学相关性和临床重要性的模型来研究完好和损伤脊髓中纤维组织的变化。我们的假设是，解剖基质几何特性的变化，识别损伤区域和远端脊髓节段的神经可塑性变化，与脊髓损伤（SCI）后不同程度的损伤和运动恢复水平相关。在假设检验之前，我们将对HARDI数据进行降噪，然后构建正常的寰椎索。在正常寰椎和受损脊髓之间进行形变登记和张量形态测定，为与脊髓损伤相关的每种类型的行为恢复提供独特的特征。在获得HARDI数据后，将通过对脐带样本进行系统的组织学分析来验证该假设。脊髓将被切割并用纤维和细胞染色，以验证因脊髓挫伤（在人类中也很常见）而导致的解剖组织变化。从组织学和HARDI分析中获得的解剖特征之间的比较将为图像分析和假设提供验证。根据规范的损伤装置参数，将产生三种严重程度的脊髓损伤（轻、轻、中度挫伤）。然后将识别这些标记数据子集的结构签名。然后使用大余量分类器实现新颖和受伤脐带HARDI数据集的自动分类。最后，随着时间的推移，将分析HARDI数据，以便通过研究随时间的结构变化和开发相应于所选类别的结构转换的动态模型来学习和预测运动恢复水平。我们将在特征空间中使用自回归模型来跟踪和预测脊髓损伤的结构变化，并将其与功能恢复联系起来。

DESCRIPTION (provided by applicant): Establishing structure-function correlations is fundamental to understanding how information is processed in the central nervous system (CNS). Axonal connectivity is a key relationship that facilitates information transmission and reception within the CNS. Recently, diffusion weighted magnetic resonance imaging (DW-MRI) methods have been shown to provide fundamental information required for viewing structural connectivity and have allowed visualization of fiber bundles in the CNS in vivo. In this project, we propose to develop methods for extraction and analysis of these patterns from high angular resolution diffusion weighted images (HARDI) that is known to have better resolving power over diffusion tensor imaging (DTI). To this end, a biologically relevant and clinically important model has been chosen to study changes in the organization of fibers in the intact and injured spinal cord. Our hypothesis is that, changes in geometrical properties of the anatomical substrate, identifying the region of injury and neuroplastic changes in distant spinal segments, correlate with different magnitudes of injury and levels of locomotor recovery following spinal cord injury (SCI). Prior to hypothesis testing, we will denoise the HARDI data and then construct a normal atlas cord. Deformable registration and tensor morphometry between a normal atlas and an injured cord would be performed to provide a distinct signature for each type of behavior recovery associated with the SCI substrate. Validation of the hypothesis will be performed through systematic histological analysis of cord samples following acquisition of the HARDI data. Spinal cords will be cut and stained with fiber and cell stains to verify changes in anatomical organization that result from contusive injury (common in humans as well) to the spinal cord. A comparison between anatomical characteristics obtained from histological versus HARDI analysis will provide validation for the image analysis and the hypothesis. Three severities of spinal cord injuries will be produced (light, mild and moderate contusions) based upon normed injury device parameters. The structural signatures of these labeled data subsets will then be identified. Automatic classification of novel & injured cord HARDI data sets will then be achieved using a large margin classifier. Finally, HARDI data acquired over time will be analyzed in order to learn and predict the level of locomotor recovery by studying the structural changes over time and developing a dynamic model of structural transformations corresponding to each chosen class. We will use an auto-regressive model in the feature space to track and predict structural changes in SCI and correlate it to functional recovery. PUBLIC HEALTH RELEVANCE: This project involves the development of automated methods to extract morphological signatures that characterize changes in spinal cord injury (SCI) substrate estimated from Diffusion MRI scans of rats, and predict the functional recovery by correlating to behavioral studies. Although the various algorithms developed here are for analysis of SCI, they can be used in other applications such as traumatic brain injury, in tracking and predicting developmental changes etc. from diffusion MRI scans.