Intrinsic Modeling and Tracking of Neuroanatomy in Alzheimer's Disease

阿尔茨海默病神经解剖学的内在建模和跟踪

• 批准号: 8758885
• 负责人: Yonggang Shi
• 金额: $ 16.66万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8758885
• 关键词: Algorithms; Alzheimer' s Disease; Anatomy; Atlases; Biometry; Brain; Brain Mapping; Brain imaging; Classification; Clinical; Cognitive; Computer Assisted; Data; Data Set; Dementia; Descriptor; Detection; Development; Diagnosis; Diffusion Magnetic Resonance Imaging; Discrimination; Disease; Disease Progression; Early Diagnosis; Emotional; Family; Fiber; Foundations; Goals; Graph; Image; Image Analysis; Imaging Techniques; Inherited; Joints; Label; Lead; Magnetic Resonance Imaging; Manuals; Maps; Masks; Measurement; Measures; Methods; Metric; Modeling; Multimodal Imaging; Neuroanatomy; Neurologic; Output; Pathology; Patients; Pattern; Perfusion; Play; Population; Prevention; Psychometrics; Public Health; Research; Research Personnel; Role; Sensitivity and Specificity; Series; Site; Societies; Software Tools; Solid; Solutions; Spin Labels; Staging; Structure; Surface; System; Testing; Thick; Tissues; Training; Training Activity; Validation; Variant; abstracting; base; cerebral atrophy; clinical Diagnosis; clinical practice; disability; gray matter; improved; mild cognitive impairment; neuroimaging; novel; reconstruction; tool; white matter

DESCRIPTION (provided by applicant): Project Summary/Abstract Neuroimaging plays an increasingly important role in the early diagnosis of Alzheimer's disease (AD). The availability of data from large scale, multi-site studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Dominantly Inherited Alzheimer's Network (DIAN), provide unprecedented opportunities of improving our understanding of this complicated disease. On the other hand, these large scale, high dimensional imaging data of ever growing size call for the urgent needs of developing and validating robust and automated mapping tools. To become in independent investigator of brain imaging research in AD, the candidate proposes in this K01 application to receive training in multimodal image analysis, clinical diagnosis of AD, MR imaging techniques, and biostatistics. These training activities will greatly augment the candidate's background in neuroimage analysis and establish a solid foundation for his long term goal of being a leading researcher in computer-aided early diagnosis of AD. In the research plan, the candidate will develop and validate a suite of novel tools for the mapping of neuroanatomy during the development and progression of AD using intrinsic geometry of the anatomical structure. In contrast to conventional approaches that align brains in a canonical Euclidean space such as the Talairach atlas, the candidate models the anatomy intrinsically with the eigenfunctions of the Laplace-Beltrami (LB) operator and their Reeb graphs. This spectral approach is invariant to natural pose variations, robust to geometric deformations due to pathology and disease progression, and leads to novel methods for surface reconstruction, modeling, and mapping. The specific aims are: 1. Validate and continue to develop an intrinsic framework for the mapping of sub-cortical structures based on the LB eigenfunctions. 2. Develop and validate an automated system for cortical surface extraction, major sulci identification, and mapping. 3. Develop and validate novel algorithms for multimodal fusion with cortical mapping. The new algorithms will be validated with cognitive measures using data from ADNI and DIAN, and compared with existing methods in terms of the discrimination power in the early diagnosis of AD. The software tools developed in this project will be distributed publicly.

描述（由申请人提供）：项目摘要/摘要神经影像学在早期诊断阿尔茨海默氏病（AD）中起越来越重要的作用。来自大规模的多站点研究的数据的可用性，例如阿尔茨海默氏病神经影像学计划（ADNI）和主要遗传的阿尔茨海默氏症网络（DIAN），提供了前所未有的机会，可以提高我们对这种复杂疾病的理解。另一方面，这些尺寸不断增长的大规模，高维成像数据需要开发和验证可靠和自动化的映射工具的紧急需求。为了成为AD中脑成像研究的独立研究者，该候选人建议在此K01应用中接受多模式图像分析，AD临床诊断，MR成像技术和生物统计学的培训。这些培训活动将大大扩大候选人在神经图像分析方面的背景，并为他的长期目标是成为AD的计算机辅助早期诊断的长期目标。在研究计划中，候选人将使用解剖结构的固有几何形状在AD的开发和进展过程中开发和验证一套新的新工具，用于在AD开发和进展过程中映射神经解剖学。与常规的方法相比，在诸如Talairach Atlas之类的规范欧几里得空间中对齐大脑的常规方法，候选人本质上与Laplace-Beltrami（LB）操作员及其REEB图的特征性函数本质上对解剖结构进行了建模。这种光谱方法是自然姿势变化的不变，由于病理和疾病进展而导致的几何变形，并导致了表面重建，建模和映射的新方法。具体目的是：1。验证并继续开发一个基于LB本征函数的亚皮质结构映射的内在框架。 2。开发和验证自动化系统，以进行皮质表面提取，主要的沟识别和映射。 3。开发和验证新型算法，用于用皮质映射进行多模式融合。新算法将使用ADNI和DIAN的数据对认知措施进行验证，并将其与现有方法在AD早期诊断中的歧视能力方面进行验证。该项目中开发的软件工具将公开分发。