Multivariate Pattern Analysis Methods for Neuroimaging Genetics Studies

神经影像遗传学研究的多变量模式分析方法

• 批准号: 8535152
• 负责人: Mert Rory Sabuncu
• 金额: $ 17.54万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8535152
• 关键词: Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Anatomy; Biological Markers; Brain; Candidate Disease Gene; Clinical; Clinical Trials; Cognitive; Complex; Computer software; DNA Sequence; Data; Data Set; Dementia; Development; Disease; Early Diagnosis; Event; Exhibits; Face; Foundations; Future; Genes; Genetic; Genetic Research; Genetic Risk; Genotype; Goals; Hereditary Disease; Hippocampus (Brain); Image; Individual; Joints; Late Onset Alzheimer Disease; Lead; Logistic Regressions; Machine Learning; Measurement; Mental disorders; Methods; Mining; Modeling; Motivation; Neuroanatomy; Neurodegenerative Disorders; Outcome; Pathology; Patients; Pattern; Performance; Phase; Phenotype; Probability; Process; Recruitment Activity; Research Personnel; Risk; Sample Size; Schizophrenia; Testing; Thick; Training; base; career; clinical Diagnosis; clinical phenotype; computerized tools; data modeling; disorder risk; entorhinal cortex; genetic risk factor; genetic variant; genome wide association study; high risk; image processing; improved; in vivo; interest; mild cognitive impairment; molecular pathology; neuroimaging; novel; outcome forecast; pre-clinical; programs; risk variant; tool

DESCRIPTION (provided by applicant): Common mental disorders such as Alzheimer's disease and schizophrenia are largely heritable with complex genetic underpinnings. Large-scale genome-wide association studies that contrast DNA sequence data from patients and controls have recently identified novel genetic risk variants for these disorders. Nevertheless, the processes through which genotype increases risk are yet to be fully characterized. Neuroimaging offers a richer picture of the underlying disease processes than a clinical diagnosis. Thus the joint analysis of neuroimaging and genetics data promises to advance our understanding of these processes. Today, neuroimaging genetics studies however face important challenges that obstruct progress: small sample sizes, modest effect sizes, and the extreme dimensionality of the data limit statistical power and thus our ability to explore the complex and subtle associations between genes, neuroanatomy and clinical decline. Currently, the prevalent approach in neuroimaging genetics is to concentrate the analysis on a small number of anatomic regions of interest and/or candidate genes and often ignore a large portion of the data. The core goal of the proposed project is to develop computational tools that will take full advantage of the richness in the datasets and facilitate the exploration of the multifaceted associations between genotype, neuroimaging measurements and clinical phenotype. The proposed project will use advanced multivariate pattern analysis methods such as support vector machines to compute image-based and genetic scores that reflect pathology. We will validate the tools based on their association with classical biomarkers of disease. Finally, we will develop a model that uses both imaging and genotype data to predict future clinical outcome. We expect these tools will enable progress along three directions relevant to complex mental disorders, e.g. late-onset Alzheimer's disease (AD): (1) confirming and characterizing risk genes, (2) identifying disease-specific anatomical alterations in healthy individuals, and (3) early diagnosis and prognosis. The project will (1) use three already-collected large-scale datasets to apply the developed tools to AD, (2) build on cutting-edge image processing algorithms that we have been developing, and (3) allow the candidate to receive further training in neuroanatomy, mental disorders and genetics, forming the foundation for his future career as an independent researcher.

描述(申请人提供)：常见的精神障碍，如阿尔茨海默病和精神分裂症，很大程度上是可遗传的，有复杂的遗传基础。对比患者和对照的DNA序列数据的大规模全基因组关联研究最近发现了这些疾病的新的遗传风险变异。然而，基因型增加风险的过程还没有得到充分的表征。与临床诊断相比，神经成像提供了更丰富的潜在疾病过程的图像。因此，神经成像和遗传学数据的联合分析有望促进我们对这些过程的理解。然而，今天，神经成像遗传学研究面临着阻碍进展的重要挑战：样本量小，效应大小适中，数据的极端维度限制了统计能力，从而限制了我们探索基因、神经解剖学和临床衰退之间复杂而微妙的联系的能力。目前，神经成像遗传学中流行的方法是将分析集中在少数感兴趣的解剖区域和/或候选基因上，而往往忽略大部分数据。拟议项目的核心目标是开发计算工具，充分利用数据集的丰富性，并促进探索基因、神经成像测量和临床表型之间的多方面联系。拟议的项目将使用先进的多变量模式分析方法，如支持向量机，计算基于图像的和反映病理的遗传分数。我们将根据这些工具与经典疾病生物标记物的关联来验证这些工具。最后，我们将开发一个模型，该模型同时使用成像和基因数据来预测未来的临床结果。我们预计，这些工具将使与复杂精神障碍相关的三个方向取得进展，例如晚发性阿尔茨海默病(AD)：(1)确认和表征风险基因，(2)识别健康个体特定疾病的解剖变化，以及(3)早期诊断和预后。该项目将(1)使用已经收集的三个大规模数据集将开发的工具应用于AD，(2)建立在我们一直在开发的尖端图像处理算法的基础上，以及(3)允许候选人接受神经解剖学、精神障碍和遗传学的进一步培训，为他未来作为独立研究人员的职业生涯奠定基础。