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.

构建人脑功能的综合图片需要了解分子的影响 遗传因素通过许多介入结构和相互作用的层次向上传播 影响行为，精神病和认知特征。大脑倡议（BI）等项目认识到 建立这样的图片需要专家在遗传学，基因组学，神经科学， 和临床研究，并创造了资源以帮助这些学科的数据整合。然而， 结合实验方法和涵盖庞大长度/时间尺度的理论模型的挑战 仍然很重要。应对这一挑战的更有承诺的途径之一是使用可解释的 深入学习数据中固有的高维结构的方法。通过嵌入约束 已知的生物结构，研究人员可以将模型的内部表示与可识别因素联系起来 来自神经科学。该建议将与其他公众一起利用BI档案中的广泛资源 资源，以整合遗传学，功能基因组学和神经影像学的数据。通过次要 对这些数据的分析，我们将构建高级特质的深层多基因模型，例如认知， 我们将将此类性状的基础机制追踪到特定区域，细胞 类型，功能连接模式和结构成像特征。另外，通过嵌入生物学 中间水平的结构（组织和细胞类型基因调节网络；结构/功能约束 根据MRI数据），我们将建立改善多基因风险的添加性遗传力量的模型。在 流程，我们将将BI数据与其他公开可用的大脑OMIC和成像数据集进行协调。我们将 将所有资源和模型存放到相关的BI档案中。该提案被构成如下。首先，我们会的 将遗传学与来自多种大脑区域和细胞类型的基于基因组学的网络相结合，并发展 区域和细胞类型特异性的OMICS变化的预测模型。这些将包含在可解释的 认知和精神病特征的深层模型（AIM 1）。其次，我们将学习结构和结构的预测模型 遗传预测因子的功能成像特征，同样将嵌入可解释的深处 高级特征的模型（AIM 2）。第三，通过将两者结合起来建立一个集成的多基因模型 功能基因组学和基于神经影像学的特征，允许两个子组件的影响 评估。此外，我们将扩展以前的工作以开发基于压缩的可解释性 方法，允许网络在不同级别的分辨率水平上进行粗粒和解释。 解释将包括探索精神疾病和互动中的亚表型结构 在特征之间（目标3）。我们期望拟议的方法具有广泛的影响，包括见解 进入大脑功能的机械基础，用于集成多级分析的新框架以及 开发未来研究的方法和资源。

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

The Cellular Underpinnings of the Human Cortical Connectome.

人类皮质连接组的细胞基础。

• DOI: 10.1101/2023.07.05.547828
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Zhang,Xi-Han;Anderson,KevinM;Dong,Hao-Ming;Chopra,Sidhant;Dhamala,Elvisha;Emani,PrashantS;Margulies,Daniel;Holmes,AvramJ
• 通讯作者: Holmes,AvramJ