III: Small: Modeling Multi-Level Connectivity of Brain Dynamics

通过大脑动脉和静脉网络的血液流动的时间动态提供了一个了解人类大脑健康的窗口。由于大脑容易受到血液供应中断的影响，因此大脑动力学是许多神经系统疾病（如中风、脑癌和阿尔茨海默病）的重要指标。现有的描述大脑动力学特征的努力主要集中在“孤立”模型上，其中来自单体素、单模态和单主体的数据被表征。然而，大脑是一个巨大的网络，在结构和功能层面上自然连接，多模态成像提供了这种自然连接的补充信息。因此，目前孤立的模型被认为无法提供必要的平台，以实现在理解、诊断和治疗神经和认知疾病方面的许多潜在进步，从而在当前的计算建模能力和脑动力学分析的需求之间留下了一个关键的差距。该项目旨在通过利用多尺度结构（体素、脉管系统、组织）连接和多模态（解剖、血管造影、灌注）连接来弥补这一差距，以开发一个集成的连接计算范式，用于表征和理解大脑动力学。该方法包括三个重点：(1)多尺度结构连接建模，以量化单个体素以外的大脑动力学；(2)多模态动态字典学习，挖掘隐藏互补信息；(3)采用多中心评价方法，对所提出的模型在三家全国知名医疗系统的有效性进行评价。该项目的成功完成将有可能改变快速发展的脑动力学建模领域，促进基础神经科学的发现，并使神经血管疾病的全面鉴定成为可能。为了扩大其影响，该项目还将实施教育举措，使学生、中学教师和医疗专业人员接触到“全民计算机科学”，培养对STEM和跨学科职业的兴趣，并促进计算机科学和计算思维在大脑健康和神经医学方面的融合研究。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。

The temporal dynamics of blood flows through the network of cerebral arteries and veins provides a window into the health of the human brain. Since the brain is vulnerable to disrupted blood supply, brain dynamics serves as a crucial indicator for many kinds of neurological diseases such as stroke, brain cancer, and Alzheimer's disease. Existing efforts at characterizing brain dynamics have predominantly centered on 'isolated' models in which data from single-voxel, single-modality, and single-subject are characterized. However, the brain is a vast network, naturally connected on structural and functional levels, and multimodal imaging provides complementary information on this natural connectivity. Thus, the current isolated models are deemed not capable of offering the platform necessary to enable many of the potential advancements in understanding, diagnosing, and treating neurological and cognitive diseases, leaving a critical gap between the current computational modeling capabilities and the needs in brain dynamics analysis. This project aims to bridge this gap by exploiting multi-scale structural (voxel, vasculature, tissue) connectivity and multi-modal (anatomical, angiography, perfusion) connectivity to develop an integrated connective computational paradigm for characterizing and understanding brain dynamics.The approach consists of three thrusts: (1) multi-scale structural connectivity modeling to quantify brain dynamics beyond a single voxel; (2) multimodal dynamic dictionary learning for mining hidden complementary information; and (3) multicenter evaluation to assess the efficacy of the proposed models at three nationally renowned healthcare systems. Successful project completion would potentially transform the rapidly evolving field of brain dynamics modeling, facilitate basic neuroscience discovery and enable comprehensive identification of neurovascular diseases. Aiming to broaden its impact this project will also implement educational initiatives to expose students, middle school teachers, and medical professionals to 'CS for All,' to foster interests in STEM and cross-disciplinary careers, and to promote research on the convergence of computer science and computational thinking for brain health and neuromedicine.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.