Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry

神经退行性和神经发育皮层下形状微形态测量

• 批准号: 9769057
• 负责人: MICHAEL I MILLER
• 金额: $ 66.67万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9769057
• 关键词: Age; Alzheimer' s Disease; Amygdaloid structure; Anatomy; Architecture; Atlases; Atrophic; Attention deficit hyperactivity disorder; Basal Ganglia; Biological Markers; Brain; Brain Mapping; Childhood; Classification; Client; Clinical; Communities; Complex; Computer software; Corpus striatum structure; Coupling; Data; Data Set; Detection; Development; Diagnosis; Dimensions; Disease; Documentation; Elderly; Evaluation; Family; Genetic Load; Hippocampus (Brain); Huntington Disease; Image; Imagery; Institutes; Internet; Investigation; Iowa; Laboratories; Libraries; Magnetic Resonance; Measures; Methods; Modeling; Morphology; Nerve Degeneration; Network-based; Neurocognitive; Neurologist; Onset of illness; Ontology; Patients; Population; Positioning Attribute; Probability; Procedures; Psychiatrist; Public Health; Research; Risk; Sampling; Schizophrenia; Shapes; Signal Transduction; Statistical Methods; Statistical Models; Structure; Symptoms; System; Technology; Testing; Thalamic structure; Time; Universities; Variant; base; boys; burden of illness; clinical biomarkers; comparison group; computational anatomy; computer science; data pipeline; data reduction; entorhinal cortex; high dimensionality; indexing; interest; medical schools; millimeter; morphometry; multidimensional data; multidisciplinary; neurodevelopment; neuroimaging; novel; open source; population based; putamen; shape analysis; software as a service; statistics; tool; vector; web portal

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.

项目摘要 在过去的十年中，我们一直在构建、解析和讨论提取 来自高维磁共振的神经退行性变和神经发育生物标记物 1mm3尺度的图像，具有辨别力。与此同时，大型且复杂的数据集和网络 分段结构的范围越来越多地可用于研究团体，例如预测HD， Track-HD、ADNI和SchizConnect。神经学家和临床医生对追踪生物标记物感兴趣 描述解剖网络中的萎缩率、通过 网络，以及根据临床症状确定的转化风险的预测。这些争吵和 建模方法新颖。我们的生物标志物是通过脑成像技术提取的，基于 微分形态计量学，通过解剖坐标的微分形态追踪来研究形态变化 亚毫米尺度的系统。像体视学一样，微分形态计量学发现高维特征 通过基于随机场的统计的紧密集成来传递神经退化和神经发育的信号 方法利用高维置换计算的大偏差经验概率估计 测试。按家庭计算的比率是为群体比较而计算的，并已成为高级变化点 模型允许我们明确地估计解剖特征变化的进展的传播通过 与神经退行性变相关的网络结构--阿尔茨海默病和亨廷顿病 (HD)和神经发育-精神分裂症(SZ)和注意力缺陷和多动障碍(ADHD)。 这些工具将通过MriCloud进行分发和测试。 我们将实现三个具体目标。AIM 1将使用我们的MriCloud架构来部署多Atlas大脑 映射模块，用于将大约400个结构的本体映射到T1和DTI数据。建筑 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差异测量模块，该模块包括 用于a)从横截面数据集的总体中生成结构模板的管道，b)数据 横截面和纵向测地线映射的模板约简，以及c)多重假设 基于顶点、Laplace-Beltrami基函数和PCA基函数的测试程序。用户具有 他们自己对皮质下结构的本体定义将能够生成种群模板和 在模板坐标中可视化统计数据。AIM 3将生成一个Web门户，供用户使用其中的模块 目标1和2检查结构网络的异常，如纹状体、丘脑、杏仁核和 海马体。