SHAPE OPTIMIZING DIFFEOMORPHISMS FOR COMPUTATIONAL BIOLOGY

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 这一建议将有助于在大规模计算环境中实现严格的时空医学图像分析的新的合作。该系统将大大提高神经影像学界对正常和病理性衰老及相关变量的定量理解。医学图像捕捉个体随时间发生的变化，并对人群寿命中可见的解剖和功能差异范围进行采样。我们的目标是将这些差异与原因联系起来，例如，先天群体变异性，损伤，病理学或基因型对表型的影响。最近提出的几何形态测量（DM）系统量化和这些变量的最佳时空坐标系。这项新技术允许图谱沿着人口的发展，以统计方式捕捉年龄、疾病或其他因素的影响。这个共同的，不断发展的映射空间提供了丰富的先验知识，允许人们建立描述形状和功能变化范围和类型的概率。然后可以研究和可视化这些聚集的人口属性，用于研究，以及教学和诊断。我们的DM方法的设计与对称性（算法必须是对称的）和特异性（分析应该是最佳的研究空间）的公理，并具有自动生成特定于数据库的地图集的能力。DM给出的变化的严格和对称的定义以最高的准确性、可重复性和高水平的细节捕捉神经解剖学的差异。因此，DM研究最大限度地提高了从神经影像学队列中提取的信息，特别是当与遗传或行为变量相关时。此外，DM还满足了神经成像领域迫切的研究需求：DM从任意大小的数据库中获取最佳地图集，并通过地标和统计指导对解剖对应进行大变形优化。加州大学洛杉矶分校计算生物学中心（CCB）的资源将允许这些方法应用于它们设计的大型数据集，并以前所未有的分辨率和规模应用。拟议的工作有三个独特的目标：合作，方法和临床评估/应用：实例化之间的合作，其中数据和算法通过计算生物学中心共享和传播;将生物形态学发展成为一个尖端的，大规模的，公开可用的计算工具;评估和完善已开发的方法，并与CCB脑映射工具进行比较，用于神经退行性疾病条件下结构-功能关联的神经影像学研究

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This proposal will contribute a new collaboration for implementing rigorous spatiotemporal medical image analysis in a large scale computing environment. The system will dramatically enhance the neuroimaging community's quantitative understanding of normal and pathological aging and correlated variables. Medical images capture the changes that occur in an individual over time and samples the range of anatomical and functional differences visible in a population lifespan. Our goal is to associate these differences with causes, for example, innate population variability, injury, pathology, or the effects of genotype on phenotype. The recently proposed Diffeomorphometry (DM) system quantifies and relates these variables to an optimal spatiotemporal coordinate system. This novel technique allows the atlas to evolve in time along with the population to statistically capture effects of age, disease or other factors. This common, evolving map space gives a wealth of prior knowledge, allowing one to build probabilities describing ranges and types of variation in shape and function. These aggregate population attributes may then be studied and visualized, used in research, as well as teaching and diagnosis. Our DM method is designed with the axioms of symmetry (the algorithms must be symmetric) and specificity (the analysis should be optimal in the study space) in mind and with the ability to automatically generate database-specific atlases. The rigorous and symmetric definition of change given by DM captures differences in neuroanatomy with superlative accuracy, reproducibility and high level of detail. Consequently, a DM study maximizes the information extracted from a neuroimaging cohort, especially when correlated with, for instance, genetic or behavioral variables. Furthermore, DM satisfies pressing research needs in neuroimaging: DM derives optimal atlases from arbitrarily sized databases and gives large deformation optimization of anatomical correspondence, through landmark and statistical guidance. The resources in the UCLA Center for Computational Biology (CCB) will allow these methods to be applied on the large datasets they were designed for and at an unprecedented resolution and scale. The proposed work has three distinctive aims: collaboration, methodology and clinical evaluation/application: Instantiate a collaboration between UPenn and UCLA, where data and algorithms are shared and disseminated via the Center for Computational Biology; Develop Diffeomorphometry into a cutting-edge, large-scale, publicly available computational tool; Evaluate and refine the developed methodology, as well as compare with CCB brain mapping tools, on neuroimaging studies of structure-function associations under neurodegenerative conditions