4D Shape Analysis for Modeling Spatiotemporal Change Trajectories in Huntington's

用于亨廷顿舞蹈症时空变化轨迹建模的 4D 形状分析

• 批准号: 8742009
• 负责人: GUIDO GERIG
• 金额: $ 41.35万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-30 至 2016-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8742009
• 关键词: Affect; Anatomic Models; Ancillary Study; Atrophic; Behavior; Biological; Biological Markers; Biometry; Brain; Calibration; Clinical; Cognitive; Collaborations; Communities; Complex; Computer Analysis; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Development; Diagnosis; Disease; Disease Progression; Ensure; Environment; Evolution; Functional disorder; Generations; Generic Drugs; Genetic; Growth; Hereditary Disease; Hippocampus (Brain); Huntington Disease; Image; Image Analysis; Imagery; Impaired cognition; Impairment; Individual; Inherited; Iowa; Joints; Lead; Longitudinal Studies; Magnetic Resonance Imaging; Measurement; Measures; Methodology; Methods; Modeling; Movement; Nature; Nerve Degeneration; Neurobiology; Neurodegenerative Disorders; North America; Onset of illness; Pathology; Patients; Pharmacotherapy; Phase; Phenotype; Procedures; Process; Research; Research Personnel; Resources; Risk; Sampling; Shapes; Site; Staging; Statistical Models; Structure; Surface; Symptoms; Techniques; Testing; Thalamic structure; Time; Training; Travel; Utah; Validation; Work; base; cerebral atrophy; cognitive change; computational anatomy; computer framework; computerized tools; computing resources; design; improved; innovation; insight; interest; longitudinal analysis; longitudinal database; longitudinal design; morphometry; neuroimaging; novel; pre-clinical; prevent; prototype; public health relevance; putamen; shape analysis; spatiotemporal; statistics; tool

DESCRIPTION (provided by applicant): Huntington's Disease (HD) is an inherited, neurodegenerative disorder. Major research findings describe the progressive nature of neurodegeneration and subtle changes very early during the preclinical phase, e.g. atrophy of the neurostriatum and other subcortical structures and observation of movement abnormalities up to 15 years before clinical symptoms are diagnosed. The need for improved understanding of the time-course of underlying neurobiological and cognitive changes during the prodromal stage, which is essential for the development of new therapies, motivated the longitudinal design of the multi-center PREDICT-HD study. Subjects at risk for HD are imaged and examined repeatedly to study patient-specific trajectories of brain structures and associated cognitive changes. Given the consortium's large database of longitudinal imaging data, there is a clear need to develop sensitive measures describing and characterizing the timing and nature of such changes. This proposal for an Ancillary Study in PREDICT-HD will provide newly developed computational anatomy tools specifically designed for the analysis of anatomical structures in longitudinal image data and for the statistical modeling of spatio-temporal trajectories of morphometric brain measurements. We will provide novel tools for spatio-temporal (4D) analysis of longitudinal neuroimage data, via a shareable computational environment with the PREDICT-HD consortium. The proposed methods are particularly innovative in various analytical and computational aspects: (1) Overcoming limitations of longitudinal studies with its inherent challenges of multiple non-uniformly spaced time points and missing data via continuous modeling; (2) Presenting efficient and robust 4D shape modeling without the need to compute corresponding landmarks across shape-groups; (3) Applying a mathematical concept that mimics biological growth to guarantee smooth 4D shape trajectories, and (4) The joint analysis of multi-object complexes where structures of interest are embedded in their anatomical context. This new resource will significantly enhance image-analysis capabilities of the PREDICT-HD consortium, as the tools will provide a modeling of the time course of pathophysiological processes affecting single or multi-object subcortical structures, offering researchers new insight into the time-course and progression of pathology. This project will provide optimal collaboration between MRI segmentation work at Iowa, our novel 4D shape modeling methodology as well as the expertise on statistical shape analysis and spatiotemporal shape modeling at Utah, combined with biostatistical excellence in longitudinal data analysis of the PREDICT-HD consortium at Iowa. This synergy includes both groups' strong expertise in providing shareable computational resources and training materials. Beyond providing tools, our collaborative efforts will process and analyze the large PREDICT-HD data- base, with up to 351 multi-time point MRI datasets. This will potentially lead to new biomarkers that are crucial to the development of new therapies to prevent onset or slow the progression of symptoms. This resource also serves the general scientific community since it is generic w.r.t. the application domain and freely distributed via NITRC.

描述（由申请人提供）：亨廷顿氏病（HD）是一种遗传性神经退行性疾病。主要研究结果描述了神经退行性变的渐进性和临床前阶段早期的细微变化，例如神经纹状体和其他皮质下结构的萎缩以及在临床症状诊断前长达15年的运动异常观察。需要更好地了解前驱期期间潜在神经生物学和认知变化的时间过程，这对于开发新疗法至关重要，这促使多中心PREDICT-HD研究的纵向设计。对有HD风险的受试者进行反复成像和检查，以研究患者特定的脑结构轨迹和相关的认知变化。鉴于该联盟的纵向成像数据的大型数据库，有一个明确的需要，制定敏感的措施，描述和表征这种变化的时间和性质。 该PREDICT-HD辅助研究提案将提供新开发的计算解剖学工具，专门用于纵向图像数据中的解剖结构分析和形态测量大脑测量时空轨迹的统计建模。我们将提供新的工具，纵向神经影像数据的时空（4D）分析，通过一个可共享的计算环境与预测高清联盟。所提出的方法在各种分析和计算方面特别具有创新性：（1）克服纵向研究的局限性，其固有的挑战是多个非均匀间隔的时间点和通过连续建模丢失数据;（2）呈现高效和鲁棒的4D形状建模，而不需要跨形状组计算相应的地标;（3）应用模拟生物生长的数学概念以保证平滑的4D形状轨迹，以及（4）多目标复合体的联合分析，其中感兴趣的结构嵌入其解剖背景中。 这一新资源将显著增强PREDICT-HD联盟的图像分析能力，因为这些工具将提供影响单个或多个对象皮层下结构的病理生理过程的时间过程的建模，为研究人员提供对病理学时间过程和进展的新见解。该项目将提供爱荷华州MRI分割工作之间的最佳合作，我们的新型4D形状建模方法以及犹他州统计形状分析和时空形状建模的专业知识，结合爱荷华州PREDICT-HD联盟纵向数据分析的生物统计学优势。这种协同作用包括两个小组在提供可共享的计算资源和培训材料方面的强大专业知识。除了提供工具，我们的合作努力将处理和分析大型PREDICT-HD数据库，多达351个多时间点MRI数据集。这可能会导致新的生物标志物，这是至关重要的， 开发新的治疗方法，以预防症状的发作或减缓症状的进展。这个资源也服务于一般的科学界，因为它是通用的w.r.t.并通过NITRC免费分发。