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: 8462842
• 负责人: GUIDO GERIG
• 金额: $ 43.03万
• 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 2015-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8462842
• 关键词: Affect; Anatomic Models; Ancillary Study; Atrophic; Behavior; Biological; Biological Markers; 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; 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. PUBLIC HEALTH RELEVANCE: Huntington's disease (HD) is a genetic, hereditary disease with 30,000 people in North America affected and150,000 at risk for illness. Previous neuroimaging research showed progressive brain atrophy that begins many years before symptoms are severe enough to ensure reliable diagnosis. In view of developing new drug therapies that may delay or even prevent disease onset or slow down disease progression, it is particularly important to develop sensitive objective biomarkers of such changes. Using longitudinal imaging data from the multi-site PREDICT-HD consortium, we propose new innovative spatio-temporal shape analysis methodology that provides a detailed characterization of the time course of brain changes of individuals. The toolkit and training materials will represent a new resource for the PREDICT-HD consortium but also for the scientific community.

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变性的进行性和早期临床前阶段的细微变化，例如神经纹状体和其他皮层下结构的萎缩，以及在临床症状诊断前长达15年的运动异常观察。为了更好地了解前驱期潜在的神经生物学和认知变化的时间过程，这对开发新疗法至关重要，促使了多中心PREDICT-HD研究的纵向设计。对有HD风险的受试者进行成像和反复检查，以研究患者特定的大脑结构轨迹和相关的认知变化。鉴于该联盟拥有庞大的纵向成像数据数据库，显然需要制定敏感的措施来描述和描述这些变化的时间和性质。这项在PREDICT-HD中辅助研究的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脑形态测量时空轨迹的统计建模。我们将通过与PREDICT-HD联盟共享的计算环境，为纵向神经图像数据的时空（4D）分析提供新颖的工具。所提出的方法在分析和计算方面具有特别的创新性：(1)通过连续建模克服了纵向研究的局限性，克服了多个非均匀间隔时间点和数据缺失的固有挑战；(2)提供高效、鲁棒的四维形状建模，无需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来保证平滑的4D形状轨迹；(4)对多物体复合物进行联合分析，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目标皮层下结构的病理生理过程的时间过程建模，为研究人员提供病理的时间过程和进展的新见解。该项目将为爱荷华州的MRI分割工作、我们新颖的4D形状建模方法、犹他州统计形状分析和时空形状建模方面的专业知识以及爱荷华州PREDICT-HD联盟在纵向数据分析方面的生物统计学卓越性提供最佳合作。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。除了提供工具之外，我们的合作还将处理和分析庞大的PREDICT-HD数据库，其中包含多达351个多时间点MRI数据集。这将有可能导致新的生物标记物的出现，这些生物标记物对癌症至关重要