CAREER: Brain Imaging Genetics via multimodal modular structure querying

职业：通过多模式模块化结构查询进行脑成像遗传学

• 批准号: 2045848
• 负责人: Liang Zhan
• 金额: $ 50万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045848&HistoricalAwards=false
• 关键词: CAREER; Brain; Imaging; Genetics; via

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.

脑成像遗传学整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，脑成像遗传学是发现大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的有力工具。该项目将开发一系列新颖的计算工具，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，并为代表性不足的群体的学生提供参与生物医学和机器学习研究的机会。当前脑成像遗传学面临的主要挑战如下。首先，大多数现有的脑成像遗传学研究假设基因与成像特征之间存在线性关系。考虑到脑磁共振成像（MRI）数据和遗传数据的高维性，这种线性关系过于简单化。其次，传统的脑MRI研究主要集中在标量统计上，将复杂的脑成像数据简化为一维，丢弃了重要的信息脑网络结构，因此在表征脑动力学方面不够理想。在这个项目中，我们选择大脑模块结构作为特征表示。这类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。大脑模块化结构作为连接基因型和表型性状的中间神经表型，以更小的维度和更集中的目标提供了一个有希望的桥梁。此外，如何从多模态数据中推导出模块化结构还没有得到很好的解决。该项目将提供有效的和有生物学意义的工具来绘制多基因成分的表型与大脑模块特征的帮助。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，本项目还为学员提供从K-12到博士后各个层次的多学科培训机会。成果将以同行评审文章、外展计划和代码/数据存储库的形式公开传播，以最大限度地发挥影响。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Revealing Continuous Brain Dynamical Organization with Multimodal Graph Transformer

使用多模态图转换器揭示连续的大脑动态组织

• 发表时间: 2022
• 期刊: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Lecture Notes in Computer Science.
• 作者: Zhao, Chongyue;Zhan, Liang;Thompson, Paul M.;Huang, Heng.
• 通讯作者: Huang, Heng.

CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning

• DOI: 10.1016/j.neunet.2021.07.028
• 发表时间: 2020-12
• 期刊: Neural networks : the official journal of the International Neural Network Society
• 作者: Haoteng Tang;Guixiang Ma;Lifang He;Heng Huang;L. Zhan

Hippocampal functional connectivity across age in an App knock-in mouse model of Alzheimer's disease.

• DOI: 10.3389/fnagi.2022.1085989
• 发表时间: 2022
• 期刊: Frontiers in aging neuroscience
• 影响因子: 4.8

Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes.

• DOI: 10.1007/978-3-030-87234-2_48
• 发表时间: 2021-09
• 期刊: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
• 作者: Zhang, Yanfu;Zhan, Liang;Wu, Shandong;Thompson, Paul;Huang, Heng
• 通讯作者: Huang, Heng

Explainable Contrastive Multiview Graph Representation of Brain, Mind, and Behavior

大脑、思想和行为的可解释对比多视图图表示

• 发表时间: 2022
• 期刊: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Lecture Notes in Computer Science.
• 作者: Zhao, Chongyue;Zhan, Liang;Thompson, Paul M;Huang, Heng.
• 通讯作者: Huang, Heng.