SHAPE OPTIMIZING DIFFEOMORPHISMS FOR COMPUTATIONAL BIOLOGY

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 这一建议将为在大规模计算环境中实现严格的时空医学图像分析贡献新的合作。该系统将极大地增强神经成像社区对正常和病理性衰老以及相关变量的量化理解。医学图像捕捉个体随时间发生的变化，并采样在人群寿命中可见的解剖和功能差异的范围。我们的目标是将这些差异与原因联系起来，例如，与生俱来的群体变异性、损伤、病理或基因对表型的影响。最近提出的微分形态测量(DM)系统将这些变量量化并与最优时空坐标系相关联。这项新技术使地图集能够随着人口的发展而及时演变，以统计年龄、疾病或其他因素的影响。这种常见的、不断演变的地图空间提供了丰富的先验知识，使人们能够建立描述形状和功能变化范围和类型的概率。这些聚集的人口属性然后可以被研究和可视化，用于研究以及教学和诊断。我们的DM方法的设计考虑了对称性(算法必须是对称的)和专一性(分析应该是研究空间中的最佳)的公理，并具有自动生成特定于数据库的地图集的能力。DM对变化的严格和对称的定义以最高的准确性、重复性和高水平的细节捕捉到了神经解剖学上的差异。因此，DM研究最大化了从神经成像队列中提取的信息，特别是当与例如遗传或行为变量相关时。此外，DM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得最佳图谱，并通过里程碑和统计指导提供解剖对应的大变形优化。加州大学洛杉矶分校计算生物学中心(CCB)的资源将使这些方法能够以前所未有的分辨率和规模应用于它们设计的大型数据集。拟议的工作有三个不同的目标：协作、方法论和临床评估/应用：实现加州大学宾夕法尼亚大学和加州大学洛杉矶分校之间的合作，其中数据和算法通过计算生物学中心共享和传播；将微分形态计量学发展成为尖端的、大规模的、公开可用的计算工具；评估和完善所开发的方法，并与CCB脑成像工具进行比较，以研究神经退行性疾病下结构-功能关联的神经成像研究

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