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: 9355187
• 负责人: MICHAEL I MILLER
• 金额: $ 68.88万
• 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 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9355187
• 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 reduction; entorhinal cortex; high dimensionality; indexing; interest; medical schools; millimeter; morphometry; multidisciplinary; neurodevelopment; neuroimaging; novel; open source; population based; putamen; shape analysis; software as a service; statistics; tool; vector; web portal; 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 reduction; entorhinal cortex; high dimensionality; indexing; interest; medical schools; millimeter; morphometry; 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，我们一直在建造，解析和争吵的系统来提取 高维磁共振（MR）的神经变性和神经发育生物标志物 图像为1 mm3量表，这是有区别的。同时，大而复杂的数据集和网络 细分结构正在越来越多地为研究社区（例如预测-HD）提供 Track-HD，Adni和SchizConnect。神经科学家和临床医生有兴趣跟踪生物标志物 表征解剖网络中萎缩的速率，通过 网络，以及由临床症状确定的转化风险的预测。这些争吵和 建模方法是新颖的。我们的生物标志物是通过基于大脑映射技术提取的 差异法，通过解剖坐标的差异跟踪对形态变化的研究 亚毫米量表的系统。像立体学一样，差异计量学发现了高维特征 通过紧密整合基于场的统计，信号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器的方法 测试。根据家庭比较计算家庭率，并且已经是高级更改点 建模使我们能够明确估计解剖特征进展的传播。 与神经变性相关的网络结构 - 阿尔茨海默氏病（AD）和亨廷顿氏病 （HD）和神经发育 - 精神分裂症（SZ）以及注意力缺陷和多动症（ADHD）。 这些工具将通过Mricloud进行传播和测试。 我们将执行三个具体目标。 AIM 1将使用我们的Mricloud体系结构部署多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。体系结构 将支持许多与小儿到老年群体的广泛年龄相匹配的地图集， 以及几种疾病。 AIM 2将部署一个统计形状的差异图模块，该模块由 a）从横截面数据集中生成结构模板的管道，b）数据 还原到模板的横截面和纵向地球映射，c）多个假设 基于顶点，Laplace-Beltrami基函数和PCA基函数的测试程序。用户 他们自己对皮层结构的本体定义将能够产生人口模板和 可视化模板坐标中的统计信息。 AIM 3将生成一个网页供用户使用模块 目的1和2检查纹状体，丘脑，杏仁核等结构网络中的异常 海马。