Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry

项目摘要 在过去的十年里，我们一直在构建，解析和争论系统， 来自高维磁共振（MR）的神经变性和神经发育生物标志物 1 mm 3比例尺的图像具有区别性。与此同时，大型复杂的数据集和网络 分段结构的研究团体越来越多地使用，例如Predict-HD， Track-HD、ADNI和SchizConnect。神经科学家和临床医生对追踪生物标志物感兴趣， 表征解剖网络中的萎缩率，通过 网络，以及由临床症状确定的转换风险预测。这些争吵和 建模方法新颖。我们的生物标志物是通过基于以下的大脑映射技术提取的： 形态测量学，通过解剖坐标的形态跟踪来研究形态变化 亚毫米级的系统。像体视学一样，微形态测量学发现了高维特征， 通过基于随机场的统计的紧密整合的信号传导神经变性和神经发育 通过高维排列计算的大偏差经验概率估计量的方法 试验.计算家庭明智的利率组比较，并已先进的变化点 建模使我们能够明确地估计解剖特征变化的进展通过 与神经变性相关的网络结构-阿尔茨海默病（AD）和亨廷顿病 (HD)和神经发育-精神分裂症（SZ）和注意力缺陷多动障碍（ADHD）。 这些工具将通过MriCloud进行传播和测试。 我们将实现三个具体目标。Aim 1将使用我们的MriCloud架构部署Multi-Atlas Brain 映射模块，用于将大约400个结构的本体映射到T1和DTI数据。体系结构 将支持许多地图集，这些地图集在从儿科到老年组的广泛年龄范围内匹配， 以及几种疾病。目标2将部署一个统计形状几何形态测量模块， 流水线，用于a）从横截面数据集的群体生成结构的模板，B）数据 简化为横截面和纵向测地线映射的模板，以及c）多重假设 基于顶点的测试程序，Laplace-Beltrami基函数和PCA基函数。用户 他们自己的皮质下结构的本体定义将能够生成群体模板， 在模板坐标中可视化统计数据。Aim 3将为用户生成一个门户网站， 目的1和2检查异常的结构网络，如纹状体，丘脑，杏仁核， 海马体。

Project Summary Over the past decade, we have been building, parsing and wrangling systems for extracting neurodegeneration and neurodevelopment biomarkers from high-dimensional magnetic resonance (MR) imagery at 1 mm3 scale which are discriminating. At the same time, large and complex data sets and networks of segmented structures are becoming increasingly available to the research community such as Predict-HD, Track-HD, ADNI, and SchizConnect. Neuroscientists and clinicians are interested in tracking biomarkers which characterize rates of atrophy in anatomical networks, onset of or changepoint times of spread through the networks, and prediction of risk to conversion as determined by clinical symptoms. These wrangling and modeling methods are novel. Our biomarkers are extracted via brain mapping technologies based on diffeomorphometry, the study of morphological change via diffeomorphic tracking of anatomical coordinate systems at the sub millimeter scale. Like stereology, diffeomorphometry discovers high-dimensional features signalling neurodegeneration and neurodevelopment via tight integration of random field based statistical methods via large deviation empirical probability estimators calculated via high-dimensional permutation testing. Family-wise rates are calculated for group comparisons, and have been advanced changepoint modelling allowing us to explicitly estimate the spread of progression of anatomical feature change through the networked structures associated to neurodegeneration - Alzheimer's Disease (AD) and Huntingdon's Disease (HD) and neurodevelopment - Schizophrenia (SZ) and Attention Deficit and Hyperactive Disorder (ADHD). These tools will be disseminated and tested via MriCloud. We will perform three specific aims. Aim 1 will use our MriCloud architecture to deploy a Multi-Atlas Brain Mapping module for mapping an ontology of approximately 400 structures to T1 and DTI data. The architecture will support many atlases which are matched across a broad range of age from pediatric to geriatric groups, and as well as several diseases. Aim 2 will deploy a Statistical Shape Diffeomorphometry module consisting of pipelines for a) generating templates of structures from populations of cross-sectional datasets, b) data reduction to templates for cross-sectional and longitudinal geodesic mappings, and c) multiple hypothesis testing procedures based on vertex, Laplace-Beltrami basis functions and PCA basis functions. Users with their own ontology definitions of the subcortical structures will be able to generate population templates and visualize the statistics in template coordinates. Aim 3 will generate a webportal for users to use modules from Aims 1 and 2 to examine abnormalities in networks of structures such as the striatum, thalamus, amygdala and hippocampus.