Integrative analysis of genomics and imaging data from the BRAIN Initiative and other public data sources

对来自 BRAIN Initiative 和其他公共数据源的基因组学和成像数据进行综合分析

• 批准号: 10190025
• 负责人: Mark Bender Gerstein
• 金额: $ 130.99万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10190025
• 关键词: Address; Adult; Affective; Archives; Atlases; Autopsy; BRAIN initiative; Behavioral; Biological; Brain; Brain Diseases; Brain region; Cells; Clinical Research; Cognitive; Collection; Data; Data Analyses; Data Set; Data Sources; Deposition; Discipline; Exhibits; Functional Imaging; Future; Genetic; Genetic Epistasis; Genetic Models; Genetic Variation; Genomics; Genotype-Tissue Expression Project; Goals; Grain; Heritability; Human; Image; Joints; Knowledge; Lead; Learning; Length; Link; Magnetic Resonance Imaging; Measures; Mental disorders; Metadata; Methods; Modeling; Molecular; Molecular Genetics; Network-based; Neurosciences; Pattern; Phenotype; Polygenic Traits; Process; Regulator Genes; Research; Research Personnel; Resolution; Resources; Risk; Structural Models; Structure; Techniques; Theoretical model; Thick; Time; Tissues; Twin Multiple Birth; Variant; Work; base; behavior influence; biobank; brain cell; cell type; connectome; data harmonization; data integration; deep learning; design; functional genomics; genetic predictors; genetic variant; genome wide association study; genomic data; high dimensionality; human disease; improved; in vivo; insight; interest; method development; model building; multilevel analysis; neural circuit; neuroimaging; phenomics; phenotypic data; predictive modeling; psychologic; repository; secondary analysis; trait

Constructing an integrated picture of human brain function requires understanding how the effects of molecular and genetic factors propagate upwards, through many intervening layers of structure and interaction, to influence behavioral, psychiatric and cognitive traits. Projects such as the BRAIN Initiative (BI) recognize that building such a picture requires the convergent efforts of experts across genetics, genomics, neuroscience, and clinical studies, and have created resources to aid the integration of data from these disciplines. However, the challenge of combining experimental methods and theoretical models spanning vast length/time scales remains significant. One of the more promising avenues of addressing this challenge is the use of interpretable deep-learning approaches to learn high-dimensional structure inherent in data. By embedding constraints from known biological structure, investigators can relate the models’ internal representations to identifiable factors from neuroscience. This proposal will draw on the extensive resources in BI archives, along with other public resources, to integrate data from genetics, functional genomics, and neuroimaging. Through secondary analysis on this data we will build deep, multilevel polygenic models of high-level traits, such as cognitive, affective and psychiatric traits. We will trace the mechanisms underlying such traits to specific regions, cell types, functional connectivity patterns and structural imaging features. Additionally, by embedding biological structure at intermediate levels (tissue and cell-type gene regulatory networks; structural/functional constraints from MRI data), we will build models that improve on additive heritability measures of polygenic risk. In the process, we will harmonize BI data with other publicly available brain omics and imaging datasets. We will deposit all resources and models into relevant BI archives. The proposal is framed as follows. First, we will combine genetics with genomics-based networks from multiple brain regions and cell types, and develop predictive models of region- and cell-type-specific omics variation. These will be included in an interpretable deep model of cognitive and psychiatric traits (Aim 1). Second, we will learn predictive models of structural and functional imaging features from genetic predictors, which will likewise be embedded in interpretable deep models of high-level traits (Aim 2). Third, an integrated, polygenic model will be built by combining both functional-genomics- and neuroimaging-based features, allowing the impact of both subcomponents to be assessed. Furthermore, we will extend our previous work to develop compression-based interpretability methods, which allow a network to be coarse-grained and interpreted at varying levels of resolution. Such interpretation will include the exploration of subphenotypic structure in psychiatric disorders and interactions between traits (Aim 3). We expect the proposed approach to have wide-ranging implications, including insights into mechanistic underpinnings of brain function, new frameworks for integrative multilevel analysis, and the development of methods and resources for future research.

构建人类大脑功能的整体图景需要理解分子的作用

项目成果

Anxiety Shapes Amygdala-Prefrontal Dynamics During Movie Watching.

• DOI: 10.1016/j.bpsgos.2022.03.009
• 发表时间: 2023-07
• 期刊: Biological psychiatry global open science

Wave-like properties of functional dynamics across the cortical sheet.

整个皮质层功能动力学的波状特性。

• DOI: 10.1016/j.neuron.2023.03.033
• 发表时间: 2023
• 期刊: Neuron
• 影响因子: 16.2
• 作者: Chopra,Sidhant;Zhang,Xi-Han;Holmes,AvramJ
• 通讯作者: Holmes,AvramJ

Language network lateralization is reflected throughout the macroscale functional organization of cortex.

• DOI: 10.1038/s41467-023-39131-y
• 发表时间: 2023-06-09
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Labache, Loic;Ge, Tian;Yeo, B. T. Thomas;Holmes, Avram J.
• 通讯作者: Holmes, Avram J.

A shared spatial topography links the functional connectome correlates of cocaine use disorder and dopamine D2/3 receptor densities.

共享的空间拓扑将可卡因使用障碍和多巴胺 D2/3 受体密度的功能连接组相关联。

• DOI: 10.1101/2023.11.17.567591
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Ricard,JocelynA;Labache,Loïc;Segal,Ashlea;Dhamala,Elvisha;Cocuzza,CarrisaV;Jones,Grant;Yip,Sarah;Chopra,Sidhant;Holmes,AvramJ
• 通讯作者: Holmes,AvramJ

Meta-matching as a simple framework to translate phenotypic predictive models from big to small data.

元匹配是一个简单的框架，将表型预测模型从大数据转化为小数据。

• DOI: 10.1038/s41593-022-01059-9
• 发表时间: 2022-06
• 期刊: NATURE NEUROSCIENCE
• 影响因子: 25
• 作者: He, Tong;An, Lijun;Chen, Pansheng;Chen, Jianzhong;Feng, Jiashi;Bzdok, Danilo;Holmes, Avram J.;Eickhoff, Simon B.;Yeo, B. T. Thomas
• 通讯作者: Yeo, B. T. Thomas