MRI Biomarker Discovery for Preclinical Alzheimers Disease with Geometry Methods

利用几何方法发现临床前阿尔茨海默病的 MRI 生物标志物

• 批准号: 8696981
• 负责人: Yalin Wang
• 金额: $ 21.66万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-15 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8696981
• 关键词: Accounting; Affect; Age; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Anatomic Models; Apolipoprotein E; Area; Arizona; Atrophic; Base of the Brain; Biological Markers; Brain; Brain imaging; Brain region; Brain scan; Cognitive; Computer software; Data; Data Set; Dementia; Detection; Diagnosis; Diagnostic; Diffusion; Early Intervention; Functional Imaging; Genetic; Genotype; Geometry; Goals; Health Benefit; Image; Image Analysis; Individual; Late Onset Alzheimer Disease; Lateral; Life; Liquid substance; Magnetic Resonance Imaging; Maps; Measurement; Measures; Memory; Methods; Modeling; Morphology; Multivariate Analysis; Nerve Degeneration; Patients; Pattern; Performance; Population; Prevention strategy; Preventive Intervention; Public Health; Radial; Relative (related person); Research; Research Technics; Risk; Risk Factors; Shapes; Staging; Structure; Subgroup; Surface; System; Techniques; Testing; Therapeutic; Thick; Ventricular; Work; age related; base; brain morphology; cerebral atrophy; cohort; disability; disorder prevention; geometric methodologies; gray matter; high risk; hippocampal subregions; improved; interest; lateral ventricle; mild cognitive impairment; morphometry; neuroimaging; novel; open source; pre-clinical; public health relevance; statistics; vector

DESCRIPTION (provided by applicant): We will develop and apply novel geometric algorithms to brain MRIs obtained from the well-characterized Arizona APOE cohort of presymptomatic individuals and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary results have shown statistically significant differences in several brain regions between APOE e4 carriers and non-carriers who were either healthy controls or patients with mild cognitive impairment (MCI) in the ADNI. In the current project we will continue developing novel MRI analysis methods using diffusion geometry and hyperbolic conformal geometry. Specifically, we will develop brain grey matter morphology signatures to measure grey matter morphometry changes and will also extend our surface fluid registration method to hyperbolic Poincar disk model to match lateral ventricular surfaces. The proposed system will focus on two brain cortical and subcortical structures: grey matter and lateral ventricle and compute their complete geometric features to measure all AD related atrophy. This may provide an effective way to pinpoint subregional areas of differential vulnerability associated with known neurodegenerative risk and protective factors for late-onset AD. To investigate the reliability and practicality of our method, we seek to 1) validate developed algorithms with established models in an independent cohort of Arizona APOE carriers, 2) explore APOE genetic influence in MCI patients and healthy subjects in the ADNI dataset and younger presymptomatic individuals of Arizona APOE cohort, and 3) explore additional regions of anatomical interest so as to develop MRI biomarkers for the identification of preclinical stage Alzheimer's disease that in turn will facilitate the therapeutic goals of prevention and earlier intervention to delay dementia onset and retard dementia progression.

描述（由申请人提供）：我们将开发新的几何算法，并将其应用于从症状前个体的亚利桑那州APOE队列和阿尔茨海默病神经成像倡议（ADNI）数据集获得的脑MRI。我们的初步研究结果显示，APOE e4携带者和非携带者的健康对照组或轻度认知障碍（MCI）患者在ADNI中的几个脑区之间存在统计学显著差异。在目前的项目中，我们将继续开发新的MRI分析方法，使用扩散几何和双曲共形几何。具体来说，我们将开发脑灰质形态特征来测量灰质形态学变化，并将我们的表面流体配准方法扩展到双曲庞加莱盘模型，以匹配侧脑室表面。所提出的系统将集中在两个大脑皮层和皮层下结构：灰质和侧脑室，并计算其完整的几何特征，以测量所有AD相关的萎缩。这可能提供一种有效的方法来查明与已知的神经退行性疾病风险和晚发性AD的保护因素相关的差异脆弱性的次区域。为了调查可靠性和 我们的方法的实用性，我们寻求1）在亚利桑那州APOE携带者的独立队列中用已建立的模型验证开发的算法，2）在ADNI数据集中的MCI患者和健康受试者以及亚利桑那州APOE队列的年轻症状前个体中探索APOE遗传影响，和3）探索解剖学感兴趣的其他区域，以便开发用于识别临床前阶段阿尔茨海默病的MRI生物标志物，反过来将促进预防和早期干预的治疗目标，以延迟痴呆发作和延缓痴呆进展。

项目成果

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

• DOI: 10.1007/978-3-319-19992-4_32
• 发表时间: 2015
• 期刊: Information processing in medical imaging : proceedings of the ... conference
• 作者: Su Z;Zeng W;Wang Y;Lu ZL;Gu X
• 通讯作者: Gu X