Algorithms to Map Disease & Genetic Effects on the Brain

绘制疾病地图的算法

• 批准号: 6739693
• 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 2006-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6739693
• 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遗传对大脑的影响。 结合神经影像学和遗传学技术，我们将超越大脑中疾病特定模式的绘图和可视化，开发一个框架来绘制基因如何影响健康和疾病中的大脑结构。 在试点项目中，我们将开发将大规模脑成像和大脑遗传研究联系起来所需的数学和软件。 这些算法将检测、绘制图谱并帮助理解具有疾病遗传风险的受试者的异常模式。 它们还将有助于识别和调查导致疾病易感性的数量性状基因座（QTL）。 首先，我们将对大量人群中大脑结构的变化进行编码。 新颖的算法将绘制大脑如何随着年龄、性别、童年、健康和疾病而动态变化的图表。 专门的方法将追踪大脑皮层的平均、特定群体的解剖模式。 这些模式将存储在计算/统计大脑图谱中，并与认知、临床和治疗参数相关联。 为了检测和绘制基因如何影响大脑发育和疾病，我们将测试遗传设计中的新工具。 其中包括来自 (1) 芬兰双胞胎登记处的大型神经影像数据组件，(2) 纵向扫描的正常发育的年轻双胞胎（6-20 岁），(3) 精神分裂症不一致的同卵双胞胎和异卵双胞胎，以及 (4) 已知风险的受试者基因型（包括 1q 染色体上的精神分裂症风险等位基因）。 根据我们最近的发现，我们的算法将进行测试，以揭示精神分裂症的缺陷和基因效应，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、毒瘾酗酒和其他神经发育/精神疾病）障碍）。 我们将在数学上结合计算解剖学、偏微分方程、模式理论、随机场理论和调和图的算法，以最大功率检测基因对大脑的影响。我们还将绘制大脑结构的遗传力图。用于自动分割和标记大脑结构的新工具也将加速利用这些技术的大规模研究。 这些工具经过独特的数据集验证，将极大地促进连接成像和遗传学的生物医学研究。 他们将帮助研究整个人类群体中疾病的遗传传播、触发因素和动态。