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

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

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)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Yi Zhao其他文献

Yi Zhao的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ 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:
Pulmonary Biostatistics Core
肺生物统计学核心
  • 批准号:
    10662242
  • 财政年份:
    2021
  • 资助金额:
    $ 54.45万
  • 项目类别:
Pulmonary Biostatistics Core
肺生物统计学核心
  • 批准号:
    10457995
  • 财政年份:
    2021
  • 资助金额:
    $ 54.45万
  • 项目类别:
Pulmonary Biostatistics Core
肺生物统计学核心
  • 批准号:
    10269971
  • 财政年份:
    2021
  • 资助金额:
    $ 54.45万
  • 项目类别:

相似海外基金

Exploring the mental health and wellbeing of adolescent parent families affected by HIV in South Africa
探讨南非受艾滋病毒影响的青少年父母家庭的心理健康和福祉
  • 批准号:
    ES/Y00860X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Fellowship
Scaling-up co-designed adolescent mental health interventions
扩大共同设计的青少年心理健康干预措施
  • 批准号:
    MR/Y020286/1
  • 财政年份:
    2024
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Fellowship
Shared Spaces: The How, When, and Why of Adolescent Intergroup Interactions
共享空间:青少年群体间互动的方式、时间和原因
  • 批准号:
    ES/T014709/2
  • 财政年份:
    2024
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Research Grant
Social Media Mechanisms Affecting Adolescent Mental Health (SoMe3)
影响青少年心理健康的社交媒体机制 (SoMe3)
  • 批准号:
    MR/X034925/1
  • 财政年份:
    2024
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Fellowship
Parent-adolescent informant discrepancies: Predicting suicide risk and treatment outcomes
父母与青少年信息差异:预测自杀风险和治疗结果
  • 批准号:
    10751263
  • 财政年份:
    2024
  • 资助金额:
    $ 54.45万
  • 项目类别:
The Impact of Online Social Interactions on Adolescent Cognition
在线社交互动对青少年认知的影响
  • 批准号:
    DE240101039
  • 财政年份:
    2024
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Discovery Early Career Researcher Award
Adolescent sugar overconsumption programs food choices via altered dopamine signalling
青少年糖过度消费通过改变多巴胺信号来影响食物选择
  • 批准号:
    BB/Y006496/1
  • 财政年份:
    2024
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Research Grant
Resilience Factors, Pain, and Physical Activity in Adolescent Chronic Musculoskeletal Pain
青少年慢性肌肉骨骼疼痛的弹性因素、疼痛和体力活动
  • 批准号:
    10984668
  • 财政年份:
    2024
  • 资助金额:
    $ 54.45万
  • 项目类别:
Usefulness of a question prompt sheet for onco-fertility in adolescent and young adult patients under 25 years old.
问题提示表对于 25 岁以下青少年和年轻成年患者的肿瘤生育力的有用性。
  • 批准号:
    23K09542
  • 财政年份:
    2023
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Evaluating the impact of changes in the proximity and density of vape retailers around secondary schools in Ontario on adolescent vaping behaviours
评估安大略省中学周围电子烟零售商的距离和密度变化对青少年电子烟行为的影响
  • 批准号:
    500515
  • 财政年份:
    2023
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Operating Grants
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了