CAREER: Brain Imaging Genetics via multimodal modular structure querying

脑成像遗传学结合了脑成像技术和遗传数据的优点，有可能提高我们对人类大脑的理解。最近的研究表明，脑成像遗传学是一个强有力的工具，发现脑疾病的多基因贡献和定量表征受风险基因变异影响的神经系统。该项目将开发一系列新的计算工具，以解决当前脑成像遗传学研究中的关键挑战和瓶颈，并将直接影响生物医学信息学，脑研究和数据科学。该项目的成功将用于开发新的课程，将研究纳入课堂，并为来自代表性不足群体的学生提供参与生物医学和机器学习研究的机会。当前脑成像遗传学的主要挑战如下。首先，大多数现有的脑成像遗传学研究假设基因和成像特征之间的线性关系。考虑到脑磁共振成像（MRI）数据和遗传数据的高维性，这种线性过于简单。其次，传统的脑MRI研究在表征脑动力学方面是次优的，因为它们通常关注标量统计，这将复杂的脑成像数据简化为一维，并丢弃重要的信息脑网络结构。在这个项目中，我们选择大脑模块结构作为特征表示。这些表征可以更好地描述大脑网络组织的中间尺度，而不是任何全局或局部尺度。大脑模块化结构提供了一个有前途的桥梁，作为一个更小的尺寸和更集中的目标，连接基因型和表型性状的中间神经内表型。此外，如何从多模态数据导出模块结构还没有得到很好的解决。该项目将提供有效的和生物学上有意义的工具，以映射多基因组件的表型与大脑模块化功能的帮助。这些新工具的成功开发将对大脑研究、网络科学和机器学习产生直接而强烈的影响。此外，该项目还为从K-12到博士后的各级学员提供多学科培训机会。成果将在同行评审的文章、推广计划和代码/数据库的形式中公开传播，以最大限度地发挥影响力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Brain imaging genetics, which integrates the merits of brain imaging technologies and genetic data, has the potential to improve our understanding of the human brain. Recent studies have shown that brain imaging genetics is a powerful tool to discover the polygenic contributions for brain disorders and quantitatively characterize the neural systems affected by risk gene variants. This project will develop a series of novel computational tools to address the critical challenges and bottlenecks in current brain imaging genetics research, and will directly impact biomedical informatics, brain research, and data science. The success of this project will be used to develop new curriculums that incorporate research into the classroom and provide students from under-represented groups with opportunities to participate in biomedical and machine learning research.The main challenges in current brain imaging genetics are as follows. First, most existing brain imaging genetics studies assume the linear relationship between genes and imaging features. Considering the high dimensionality of brain magnetic resonance imaging (MRI) data and genetic data, this linearity is too simplistic. Second, traditional brain MRI research is suboptimal in characterizing brain dynamics because they usually focus on scalar statistics, which reduce the complex brain imaging data to a one-dimension and discard important informative brain network structures. In this project, we choose the brain modular structure as the feature representations. These kinds of representations can better describe the intermediate scale of brain network organization, rather than any global or local scales. The brain modular structure provides a promising bridge as the intermediate neuroendophenotype with a smaller dimension and a more focused objective to link genotypic and phenotypic traits. Moreover, how to derive the modular structure from multimodal data has not been well addressed. This project will provide efficient and biologically meaningful tools to map polygenetic components to phenotypes with the aid of brain modular features. The successful development of these new tools will have an immediate and strong impact on brain research, network science, and machine learning. Moreover, this project offers multidisciplinary training opportunities for trainees at all levels from K-12 to postdoctoral levels. Outcomes will be openly disseminated in peer-reviewed articles, outreach programs, and in the form of code/data repositories to maximize impact.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.