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

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.

项目总结