CAREER: Towards a Living Neuron Twin for Improving Human Cognitive Health

事业：建立活神经元双胞胎以改善人类认知健康

• 批准号: 2239782
• 负责人: Minghan Chen
• 金额: $ 50.13万
• 依托单位: Wake Forest University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239782&HistoricalAwards=false
• 关键词: CAREER; Towards; Living; Neuron; Twin

Alzheimer's disease is a fatal and devastating cognitive disorder that affects millions of people worldwide, posing significant challenges to global health. Despite striking efforts, there is currently no effective treatment. The overwhelming societal burden threatens our future. This project aims to develop Neuron Twin, a digital system that simulates the human brain as a dynamic system using multimodal data analysis and multidomain knowledge integration to provide an accurate and efficient prediction of Alzheimer’s disease, and ultimately elucidate a mechanistic understanding of cognitive decline. Such an innovative system will offer new insights into treatment strategies and precision medicine that can benefit the Alzheimer's disease community and broader applications of neurodegenerative diseases. Furthermore, it leverages modeling and machine learning techniques to solve complex health data science problems, discovering relationships within large datasets and overcoming barriers across different domains. The interdisciplinary effort promotes education, diversity, and collaboration by transforming research findings into instructional materials, providing training opportunities for students from diverse backgrounds, and engaging undergraduate and underrepresented students in summer bootcamp and research activities.This project focuses on developing a computational framework for the Neuron Twin system. The backbone of Neuron Twin is the coalition of deep learning and multiscale modeling, which complement each other to overcome inherent limitations and leverage method scalability. Unlike existing approaches that rely on statistical inference, this system jointly analyzes multimodal data, including genetic data, neuroimages, and clinical data, and integrates multidomain knowledge from bioinformatics, systems biology, and network neuroscience to facilitate reliable early diagnosis and prognosis of Alzheimer's disease. The framework consists of three research thrusts. The first thrust is to build a multiscale model that can capture the spatiotemporal dynamics of disease progression by synthesizing information from gene regulation, protein interaction, and phenotypic heterogeneity. The second thrust is to develop continual model-guided learning to provide neurologically consistent predictions for small data regimes and continuously improve the system with sporadic data updates. The third thrust is to design hybrid learning-aided inference to address model incompleteness in parameterization and hypothesis validation. The project will be evaluated through large-scale neuroimaging genetic studies of neurodegenerative diseases.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.

阿尔茨海默病是一种致命的、毁灭性的认知障碍，影响着全世界数百万人，对全球健康构成了重大挑战，尽管做出了巨大的努力，但目前还没有有效的治疗方法，该项目旨在开发神经元双胞胎。一种将人脑模拟为动态系统的数字系统，利用多模态数据分析和多领域知识整合来提供对阿尔茨海默病的准确有效的预测，并最终阐明对认知衰退的机制理解。这样的创新系统将为认知衰退提供新的见解。此外，它还利用建模和机器学习技术来解决复杂的健康数据科学问题，发现大型数据集中的关系并克服不同领域的障碍。通过将研究成果转化为教学材料，为来自不同背景的学生提供培训机会，并让本科生和代表性不足的学生参与夏季训练营和研究活动，促进教育、多样性和协作。该项目重点开发神经元双胞胎的计算框架Neuron Twin 系统的支柱是深度学习和多尺度建模的结合，它们相辅相成，克服了固有的局限性并利用了方法的可扩展性，与依赖统计推断的现有方法不同，该系统联合嵌套了多模态数据，包括遗传数据、该框架包含三个研究重点，即建立神经图像和临床数据，并整合生物信息学、系统生物学和网络神经科学的多领域知识，以促进阿尔茨海默病的可靠早期诊断和预后。一个多尺度模型，可以通过综合来自基因调控、蛋白质相互作用和表型异质性的信息来捕获疾病进展的时空动态。第二个重点是开发连续的模型引导学习，为小数据体系提供神经学上一致的预测，并不断改进。第三个重点是设计混合学习辅助推理，以解决参数化和假设验证中的模型不完整性问题。该项目将通过大规模神经影像遗传学研究进行评估。神经退行性疾病。这反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。