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

构建人脑功能的综合图像需要了解分子的影响如何影响大脑功能。 遗传因素通过许多结构和相互作用的中间层向上传播， 影响行为、精神和认知特征。BRAIN Initiative（BI）等项目认识到， 建立这样一幅图景需要遗传学，基因组学，神经科学， 和临床研究，并创建了资源，以帮助整合这些学科的数据。然而，在这方面， 将实验方法和跨越巨大长度/时间尺度的理论模型相结合的挑战 仍然很重要。解决这一挑战的一个更有希望的途径是使用可解释的 深度学习方法来学习数据中固有的高维结构。通过将约束从 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的因素联系起来 从神经科学。该提案将利用BI档案中的广泛资源，沿着其他公共 资源，整合来自遗传学，功能基因组学和神经影像学的数据。通过二次 对这些数据的分析，我们将建立深层次的多基因模型的高层次性状，如认知， 情感和精神特质我们将追踪这些特征背后的机制到特定的区域，细胞， 类型、功能连接模式和结构成像特征。此外，通过嵌入生物 中间水平的结构（组织和细胞型基因调控网络;结构/功能限制 从MRI数据），我们将建立模型，提高多基因风险的加性遗传力措施。在 在这个过程中，我们将协调BI数据与其他公开可用的脑组学和成像数据集。我们将 存款所有资源和模型到相关的BI档案。该建议的框架如下。一是 联合收割机将遗传学与来自多个大脑区域和细胞类型的基于基因组学的网络相结合， 区域和细胞类型特异性组学变异的预测模型。这些将被包括在一个可解释的 认知和精神特质的深层模型（目标1）。其次，我们将学习结构和 遗传预测因子的功能成像特征，这同样将嵌入到可解释的深度 高水平性状模型（目标2）。第三，将两者结合起来，建立一个综合的多基因模型 功能基因组学和神经成像为基础的功能，使这两个子组件的影响， 评估。此外，我们将扩展我们以前的工作，以开发基于压缩的可解释性 方法，它允许网络被粗粒度化，并以不同的分辨率级别进行解释。等 解释将包括精神疾病和相互作用的亚表型结构的探索 目标3（Aim 3）我们希望所提出的方法具有广泛的影响，包括见解 大脑功能的机械基础，综合多层次分析的新框架，以及 为未来的研究开发方法和资源。

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.