Algorithms to Map Disease & Genetic Effects on the Brain

绘制疾病地图的算法

• 批准号: 6879950
• 负责人: PAUL M THOMPSON
• 金额: $ 15.25万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2007-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6879950
• 关键词: Alzheimer' s disease; automated data processing; bioimaging /biomedical imaging; brain imaging /visualization /scanning; brain mapping; cerebral cortex; computational neuroscience; computer simulation; computer system design /evaluation; developmental neurobiology; disease /disorder proneness /risk; family genetics; gene frequency; genetic registry /resource /referral center; genetic screening; genetic susceptibility; human data; mathematics; morphology; quantitative trait loci; schizophrenia; statistics /biometry; three dimensional imaging /topography; twin /multiplet

DESCRIPTION (provided by applicant): The overall goal of this project is to develop a powerful computational framework to map disease and genetic effects on the brain. Blending neuroimaging and genetics techniques, we will go beyond the mapping and visualization of disease-specific patterns in the brain to develop a framework to map how genes affect brain structure in health and disease. In pilot projects, we will develop the mathematics and software necessary to link large-scale brain imaging and genetic studies of the brain. These algorithms will detect, map, and help understand patterns of abnormality in subjects at genetic risk for disease. They will also empower the identification and investigation of quantitative trait loci (QTLs) that confer vulnerability for disease. First, we will encode how brain structure varies in large populations. Novel algorithms will chart how the brain changes dynamically with age, gender, in childhood, and in health and disease. Specialized methods will track average, group-specific anatomical patterns in the cerebral cortex. These patterns will be stored in a computational/statistical brain atlas, and linked with cognitive, clinical and therapeutic parameters. To detect and map how genes affect brain development and disease, we will test new tools in genetically informed designs. These include large neuroimaging data components from (1) the Finnish twin registry, (2) normally developing young twins (ages 6-20) scanned longitudinally, (3) identical and fraternal twins discordant for schizophrenia, and (4) subjects with known risk genotypes (including a schizophrenia risk allele on chromosome 1q). Following up on our recent findings, our algorithms will be tested for uncovering deficits and gene effects in schizophrenia, but they will be designed to be applicable to any brain disease (Alzheimer's, autism, bipolar disorder, drug addiction alcoholism, and other neurodevelopmental/psychiatric disorders). We will mathematically combine algorithms from computational anatomy, partial differential equations, pattern theory, random field theory, and harmonic maps, to detect gene effects on the brain with maximal power. We will also map the heritability of brain structure. Novel tools for automated segmentation and labeling of brain structures will also accelerate large scale studies with these techniques. Validated on unique datasets, these tools will greatly empower biomedical studies that bridge imaging and genetics. They will help investigate the genetic transmission, triggers, and dynamics of disease in whole human populations.

描述（由申请人提供）： 该项目的总体目标是开发一个强大的计算框架来绘制疾病和对大脑的遗传影响。 混合神经影像学和遗传学技术，我们将超越大脑中疾病特异性模式的映射和可视化，以开发一个框架，以映射基因如何影响健康和疾病中的大脑结构。 在飞行员项目中，我们将开发几个数学和软件，以将大规模的大脑成像和大脑的遗传研究联系起来。 这些算法将检测，绘制和帮助了解疾病遗传风险的受试者异常的模式。 他们还将赋予对疾病脆弱性的定量性状基因座（QTL）的识别和调查。 首先，我们将编码大脑结构在大量人群中的变化。 新颖的算法将绘制大脑如何随着年龄，性别，童年以及健康和疾病而动态变化。 专业方法将跟踪大脑皮层中平均特定的解剖模式。 这些模式将存储在计算/统计大脑图集中，并与认知，临床和治疗参数有关。 为了检测和绘制基因如何影响大脑发育和疾病，我们将测试基因知情设计中的新工具。 这些包括（1）芬兰双胞胎注册表中的大型神经影像学数据成分，（2）通常纵向扫描的年轻双胞胎（6-20岁），（3）与精神分裂症的相同和兄弟双胞胎不一致，（4）具有已知风险基因型的受试者（包括schizophrenia schizophrenia风险风险风险）。 遵循我们最近的发现，我们的算法将测试是否在精神分裂症中发现缺陷和基因作用，但它们将被设计为适用于任何脑部疾病（阿尔茨海默氏症，自闭症，双相情感障碍，药物成瘾酒精中毒和其他神经发育/精神疾病）。 我们将数学上结合计算解剖学，部分微分方程，模式理论，随机场理论和谐波图的算法，以最大力量检测基因对大脑的影响。我们还将绘制大脑结构的遗传力。用于自动分割和标记大脑结构的新型工具还将通过这些技术加速大规模研究。 在独特的数据集上验证，这些工具将极大地增强桥接成像和遗传学的生物医学研究。 他们将帮助研究整个人群中疾病的遗传传播，触发和动态。