Development of imaging genetics structural equation modeling for examining gene-brain-behavioural/cognitive relationships

用于检查基因-大脑-行为/认知关系的成像遗传学结构方程模型的开发

• 批准号: RGPIN-2019-04461
• 负责人: Hwang, Heungsun
• 金额: $ 1.46万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764044
• 关键词: Development; imaging; genetics; structural; equation

Researchers in statistical science have increasingly been able to access both genetic and neuroimaging data collected from the same individuals, thus enabling to statistically investigate genetic influence on the variation of brain function which is in turn associated with behavioral/cognitive variation. This emerging field, called statistical imaging genetics, aims to develop and apply statistical methods for integrating genetic and neuroimaging data with behavioral or cognitive outcomes to identify the neuromechanisms linked to individual differences in psychiatric disorders or cognitive tasks. Despite its great potential for studying disease- or task-specific gene-brain-behavior/cognition relationships, the field is faced with an ever-increasing need for statistical tools to examine such relationships in a more unified and confirmatory manner, while taking into account biological complexities (e.g., gene-gene interactions, genetic networks, etc.) and methodological challenges (e.g., high dimensionality, multicollinearity, etc.) embedded in genetic and neuroimaging data. Thus, the long-term objective of the proposed program is to develop a general statistical approach to investigating associations among genetic, brain, and behavioral/cognitive variables in a biologically meaningful and confirmatory manner. This program has two short-term objectives. First, it will develop a novel statistical methodology, named Imaging Genetics Structural Equation Modeling (IGSEM), as a flexible and unified tool that enables researchers to specify various biologically plausible gene-brain-behavior/cognition relationships and examine the relationships in a confirmatory manner. Second, it will further extend IGSEM to address several issues of theoretical and empirical importance in the field. The program will begin by technically developing IGSEM and its extensions, which involves model development, parameter estimation, and computer programming. It will then conduct a series of simulated and real data analyses to rigorously evaluate the empirical performance of the proposed methods. The program will make significant technical contributions to the field in that it will develop a general statistical methodology to investigate various gene-brain-behavior/cognition relationships in a more biologically interpretable and confirmatory manner. It will also make empirical contributions, providing researchers and practitioners with efficient tools for understanding the neurobiological basis of individual behavioral or cognitive differences. In addition, the program will contribute to attracting outstanding students and providing training opportunities for them to gain the anticipated research skills and experience that will enable them to progress in the field, or the natural sciences or engineering in general. Lastly, it will lead to ample interdisciplinary collaborations with national and international researchers as well as potential industrial partnerships.

统计科学的研究人员越来越能够获得从同一个人收集的遗传和神经影像学数据，从而能够统计地调查遗传对脑功能变异的影响，而脑功能变异又与行为/认知变异有关。这一新兴领域被称为统计成像遗传学，旨在开发和应用统计方法，将遗传和神经成像数据与行为或认知结果相结合，以确定与精神疾病或认知任务的个体差异相关的神经机制。尽管该领域在研究疾病或特定任务的基因-大脑-行为/认知关系方面具有巨大潜力，但该领域面临着对统计工具的日益增长的需求，以便以更统一和更确认的方式检查这种关系，同时考虑到遗传和神经影像学数据中嵌入的生物复杂性（例如，基因-基因相互作用，遗传网络等）和方法挑战（例如，高维，多重共线性等）。因此，该计划的长期目标是发展一种通用的统计方法，以生物学上有意义和确定的方式调查遗传、大脑和行为/认知变量之间的关联。这个项目有两个短期目标。首先，它将开发一种新的统计方法，称为成像遗传学结构方程建模（IGSEM），作为一种灵活和统一的工具，使研究人员能够指定各种生物学上合理的基因-大脑-行为/认知关系，并以确认的方式检查这些关系。其次，它将进一步扩展IGSEM，以解决该领域理论和实证重要性的几个问题。该计划将从技术上开发IGSEM及其扩展开始，包括模型开发、参数估计和计算机编程。然后，它将进行一系列模拟和真实数据分析，以严格评估所提出方法的经验性能。该计划将为该领域做出重大的技术贡献，因为它将开发一种通用的统计方法，以一种更具生物学可解释性和可验证性的方式调查各种基因-大脑-行为/认知关系。它还将做出实证贡献，为研究人员和实践者提供有效的工具，以理解个体行为或认知差异的神经生物学基础。此外，该计划将有助于吸引优秀的学生，并为他们提供培训机会，以获得预期的研究技能和经验，使他们能够在该领域或自然科学或工程领域取得进步。最后，它将导致与国内和国际研究人员的广泛跨学科合作以及潜在的工业伙伴关系。