Statistical methods for longitudinal integrated mechanistic modeling of multiview data
多视图数据纵向综合机制建模的统计方法
基本信息
- 批准号:10445698
- 负责人:
- 金额:$ 54.45万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-01 至 2027-06-30
- 项目状态:未结题
- 来源:
- 关键词:AddressAdolescentAgingAlcoholsAlgorithmsAlzheimer&aposs DiseaseAlzheimer&aposs disease pathologyAreaBehaviorBehavioralBig DataBiological MarkersBrainCharacteristicsClinical InvestigatorClinical SciencesComplexComplex VariablesComputational algorithmComputer softwareDataData AnalysesData ElementDevelopmentDiseaseDisease ProgressionElderlyEquationFosteringFoundationsFunctional ImagingGeneticGoalsGrowthHeterogeneityHumanImageIndividualIntermediate VariablesInterventionInvestigationJointsKnowledgeLiteratureMathematicsMental disordersMethodologyMethodsModalityModelingNerve DegenerationNeurocognitiveNeurodegenerative DisordersNeurodevelopmental DisorderNeuropsychologyNeurosciencesOutcomePathologyPathway interactionsPatternPersonal SatisfactionPhenotypePopulationPopulation HeterogeneityReproducibilityResearchRisk FactorsRoleSeriesSex DifferencesSiteSourceStatistical MethodsStatistical ModelsStructureStudy modelsSubstance abuse problemTechniquesTimeTime FactorsVariantbasebehavior measurementcomplex dataconnectomedata integrationdata modelingdata structuredisease heterogeneityheterogenous datahigh dimensionalityimprovedindividualized preventioninnovationinsightlarge datasetslongitudinal analysismultidimensional datanetwork modelsneurodevelopmentneuroimagingnovelnovel markeropen sourcepersonalized interventionprecision medicinepreventive interventionsexstructured datatraitvector
项目摘要
Abstract
In longitudinal neuroimaging studies, modeling within-subject variation across time offers insights about time-
dependent effects and causal relationships in brain changes related to neurodevelopment, neurodegeneration, or
disease progression. Uncovering and quantifying the multi-way relationship across modalities, including environ-
mental, *omics, imaging, and neurocognitive data, will help better understand the mechanisms behind complex
diseases, such as the impact of substance abuse on neurodevelopment and Alzheimer's Disease. Considering
genetic, demographic, and phenotypic traits, it is crucial to characterize disease heterogeneity, such as sex-
related differences, for precision medicine. Though methods to perform longitudinal and path analysis of univari-
ate data can be applied to individual data elements, limited methods are available directly for data with structured
constraints and integrated analysis of large datasets. The long-term goal of this proposal is to develop novel
statistical methodologies to analyze longitudinal high-dimensional data with mathematical constraints and novel
generalized path analysis methodologies to integrate complex data collected from multiple sources, with appli-
cation to the study of neurodevelopment/neurodegeneration and related mental disorders. The overall objective
is to elucidate longitudinal effects on brain structure and function, to characterize population heterogeneity, to
understand the role of different modalities and mechanisms, and to provide guidance on personalized early
prevention/intervention strategies. The challenges of longitudinal integrated mechanistic modeling of multiview
data include (i) longitudinal modeling of variables with complex structure (e.g. positive definite matrices), (ii)
high dimensionality and heterogeneity, (iii) delineation of multiple pathways, and (iv) development of large-scale
and computationally efficient algorithms. To address these, three specific aims are proposed: (1) develop novel
regression frameworks for multiple longitudinal, high-dimensional covariance matrix outcomes with predictors
across modalities; (2) develop big-data path analysis with longitudinal, high-dimensional, complex variables;
(3) develop statistical methodologies to characterize individual growth trajectories of complex variables. Aim 1
introduces longitudinal models with covariance matrices as the outcome to investigate changes in data struc-
ture and/or characteristics at a network level. Aim 2 innovates path regularization and integrated optimization
criteria for high-dimensional structured data to identify markers and search for causal pathways under longitudi-
nal settings. Aim 3 develops methodologies to guide personalized prevention/intervention strategies. To foster
dissemination, repeatability, reproducibility, and replicability of scientific findings, open-source software will be
developed. The proposed research is innovative because it proposes methodologies to perform longitudinal and
path analysis for high-dimensional data with complex and specific structures collected from multiple domains.
The proposed research is significant because it will enrich the understanding of the human brain and guide
practitioners to promote well-being in adolescent and elderly populations.
摘要
在纵向神经影像学研究中,对受试者内随时间变化的建模提供了关于时间的见解,
与神经发育、神经变性或
疾病进展。揭示和量化包括环境在内的各种模式之间的多方面关系,
心理学、组学、成像和神经认知数据,将有助于更好地理解复杂的
疾病,如药物滥用对神经发育和阿尔茨海默病的影响。考虑
遗传、人口统计学和表型特征,表征疾病异质性(例如性别)至关重要,
相关的差异,用于精准医疗。通过纵向和路径分析的方法,
数据可以应用于单个数据元素,有限的方法可直接用于结构化数据
约束和大型数据集的综合分析。这项提案的长期目标是开发新的
统计方法来分析纵向高维数据的数学约束和新的
广义路径分析方法,以整合从多个来源收集的复杂数据,与appli,
神经发育/神经变性和相关精神障碍的研究。总体目标
是阐明对大脑结构和功能的纵向影响,描述群体异质性,
了解不同模式和机制的作用,并提供个性化的早期指导
预防/干预战略。纵向一体化多视图机构建模面临的挑战
数据包括(i)具有复杂结构的变量的纵向建模(例如,正定矩阵),(ii)
高维性和异质性,(iii)多个途径的描绘,以及(iv)大规模
和计算效率高的算法。为了解决这些问题,提出了三个具体目标:(1)开发新的
多个纵向、高维协方差矩阵结果与预测因子的回归框架
(2)开发具有纵向、高维、复杂变量的大数据路径分析;
(3)开发统计方法,以描述复杂变量的个人增长轨迹。要求1
介绍了纵向模型与协方差矩阵的结果,调查数据结构的变化,
网络级的真实性和/或特性。目标2创新路径正则化和集成优化
标准的高维结构化数据,以确定标记和搜索因果关系的途径下,
最终设置。目标3制定指导个性化预防/干预战略的方法。促进
科学发现的传播、可重复性、可再现性和可复制性,开源软件将
开发拟议的研究是创新的,因为它提出了方法来执行纵向和
从多个领域收集的具有复杂和特定结构的高维数据的路径分析。
这项拟议中的研究意义重大,因为它将丰富对人类大脑的理解,并指导
从业者促进青少年和老年人的福祉。
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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{{ truncateString('Yi Zhao', 18)}}的其他基金
Statistical methods for longitudinal integrated mechanistic modeling of multiview data
多视图数据纵向综合机制建模的统计方法
- 批准号:
10685565 - 财政年份:2022
- 资助金额:
$ 54.45万 - 项目类别:
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