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

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，对全球健康构成重大挑战。尽管做出了惊人的努力，但目前还没有有效的治疗方法。 巨大的社会负担威胁着我们的未来。该项目旨在开发Neuron Twin，这是一个数字系统，它使用多模态数据分析和多领域知识整合来模拟人脑作为一个动态系统，以提供对阿尔茨海默病的准确和有效的预测，并最终阐明认知衰退的机制。这样一个创新的系统将为治疗策略和精准医学提供新的见解，可以使阿尔茨海默病社区和神经退行性疾病的更广泛应用受益。此外，它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。跨学科的努力促进教育，多样性，并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，并在暑期训练营和研究活动中吸引本科生和代表性不足的学生合作。该项目的重点是开发神经元双胞胎系统的计算框架。Neuron Twin的骨干是深度学习和多尺度建模的联盟，它们相互补充，以克服固有的局限性并利用方法的可扩展性。与依赖统计推断的现有方法不同，该系统联合分析多模态数据，包括遗传数据，神经图像和临床数据，并整合来自生物信息学，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。该框架包括三个研究重点。第一个重点是建立一个多尺度模型，通过综合基因调控、蛋白质相互作用和表型异质性的信息，可以捕获疾病进展的时空动态。第二个重点是开发持续的模型引导学习，为小数据机制提供神经学上一致的预测，并通过零星的数据更新不断改进系统。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。该项目将通过对神经退行性疾病的大规模神经影像遗传学研究进行评估。该奖项反映了NSF的法定使命，并被认为值得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。

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.