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
基本信息
- 批准号:8165447
- 负责人:
- 金额:$ 17.54万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AlgorithmsAlzheimer&aposs DiseaseAlzheimer&aposs disease riskAnatomyBiological MarkersBrainCandidate Disease GeneClinicalClinical TrialsCognitiveComplexComputer softwareDNA SequenceDataData SetDementiaDevelopmentDiseaseEarly DiagnosisEventExhibitsFaceFoundationsFutureGenesGeneticGenetic ResearchGenetic RiskGenotypeGoalsHereditary DiseaseHippocampus (Brain)ImageIndividualJointsLate Onset Alzheimer DiseaseLeadLogistic RegressionsMachine LearningMeasurementMental disordersMethodsMiningModelingMotivationNeuroanatomyNeurodegenerative DisordersOutcomePathologyPatientsPatternPerformancePhasePhenotypeProbabilityProcessRecruitment ActivityResearch PersonnelRiskSample SizeSchizophreniaTestingThickTrainingVariantbasecareerclinical Diagnosisclinical phenotypecomputerized toolsdata modelingdisorder riskentorhinal cortexgenetic risk factorgenetic variantgenome wide association studyhigh riskimage processingimprovedin vivointerestmild neurocognitive impairmentmolecular pathologyneuroimagingnoveloutcome forecastpre-clinicalprogramstool
项目摘要
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.
PUBLIC HEALTH RELEVANCE: Project Narrative/Relevance We will develop computational tools for analyzing complex associations between images, genotype and clinical phenotype. The tools will be user-friendly and freely available, and will potentially facilitate accurate early diagnosis and prognosis of mental disorders such as Alzheimer's.
描述(由申请人提供):常见的精神障碍,如阿尔茨海默病和精神分裂症,在很大程度上是遗传的复杂的遗传基础。对比患者和对照组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遗传风险变体。然而,基因型增加风险的过程尚未得到充分的表征。 神经影像学比临床诊断提供了更丰富的潜在疾病过程的图片。因此,神经影像学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,神经影像遗传学研究面临着阻碍进展的重要挑战:小样本量,适度的效应量,以及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关联的能力。目前,在神经影像遗传学的流行方法是集中在少数解剖区域的兴趣和/或候选基因的分析,往往忽略了大部分的数据。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,并促进基因型,神经影像学测量和临床表型之间的多方面关联的探索。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,以计算基于图像和遗传的分数,反映病理。我们将根据这些工具与经典疾病生物标志物的关联来验证它们。最后,我们将开发一个模型,使用成像和基因型数据来预测未来的临床结果。我们期望这些工具将使进展沿着三个方向相关的复杂的精神障碍,如迟发性阿尔茨海默病(AD):(1)确认和表征风险基因,(2)确定健康个体的疾病特异性解剖学改变,(3)早期诊断和预后。该项目将(1)使用三个已经收集的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,(3)允许候选人接受神经解剖学,精神障碍和遗传学方面的进一步培训,为他未来作为独立研究人员的职业生涯奠定基础。
公共卫生相关性:项目叙述/相关性我们将开发用于分析图像,基因型和临床表型之间复杂关联的计算工具。这些工具将是用户友好和免费提供的,并将有可能促进准确的早期诊断和阿尔茨海默氏症等精神疾病的预后。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Mert Rory Sabuncu其他文献
Mert Rory Sabuncu的其他文献
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{{ truncateString('Mert Rory Sabuncu', 18)}}的其他基金
Advanced machine learning algorithms that integrate genomewide, longitudinal MRI and demographic data to predict future cognitive decline toward dementia
先进的机器学习算法,集成全基因组、纵向 MRI 和人口统计数据,以预测未来痴呆症的认知能力下降
- 批准号:
9307096 - 财政年份:2017
- 资助金额:
$ 17.54万 - 项目类别:
Advanced machine learning algorithms that integrate genomewide, longitudinal MRI and demographic data to predict future cognitive decline toward dementia
先进的机器学习算法,集成全基因组、纵向 MRI 和人口统计数据,以预测未来痴呆症的认知能力下降
- 批准号:
10188360 - 财政年份:2017
- 资助金额:
$ 17.54万 - 项目类别:
Multi-modal Prediction of Future Clinical Dementia
未来临床痴呆的多模式预测
- 批准号:
9033273 - 财政年份:2016
- 资助金额:
$ 17.54万 - 项目类别:
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 - 财政年份:2011
- 资助金额:
$ 17.54万 - 项目类别:
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:
8726983 - 财政年份:2011
- 资助金额:
$ 17.54万 - 项目类别:
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:
8308347 - 财政年份:2011
- 资助金额:
$ 17.54万 - 项目类别:
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:
8916113 - 财政年份:2011
- 资助金额:
$ 17.54万 - 项目类别: