Collaborative Research: NCS-FO: Dynamic Brain Graph Mining

合作研究：NCS-FO：动态脑图挖掘

• 批准号: 2319451
• 负责人: Lifang He
• 金额: $ 30万
• 依托单位: Lehigh 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 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319451&HistoricalAwards=false
• 关键词: Collaborative; Research; NCS; FO; Dynamic

Mapping the connections in human brains as networked systems, i.e., brain graphs, has become a pervasive paradigm in neuroscience. In cognitive development, aging, and disease, it is crucial to understand how the structures and functions of the brain change over time to provide insights into individual differences and the mechanisms underlying different behaviors and disorders. Traditional models, however, mostly treat the brain graphs as “static,” ignoring the underlying changes over time. This project aims to develop new methods for modeling the dynamics of brain graphs that are robust in generating accurate, interpretable, and fair predictions. This interdisciplinary project will provide a unique mix of training for the participating researchers, and the research findings will be incorporated into education. The investigators will disseminate their findings through an established benchmark platform, new publications, tutorials, and collaborations with domain experts.This project seeks to overcome the barriers of existing static brain graph models and develop practical foundations and computational tools for processing and analyzing complex brain graphs derived from dynamic neuroimaging data. The project will develop a unified framework of Brain Graph Ordinary Differential Equations (BrainGDE) interweaving advanced deep graph learning techniques and ordinary differential equations, addressing the challenges of data complexity, model interpretability, fairness and trustworthiness, as well as clinical transformation. Planned research tasks will focus on: (1) unimodal dynamic brain graph mining, (2) multimodal dynamic brain graph mining, and (3) clinical investigations, in collaboration with domain experts. If successful, this research will reshape deep learning approaches for temporal data mining in bioinformatics and healthcare technologies. The dynamic graph mining framework established in this project will also guide research on the problems of sensing, knowledge discovery, reasoning, and inference on high-dimensional dynamic data with structures and will serve as a universal benchmark for future work in this direction.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.

将人类大脑中的连接映射为网络系统，即脑图，已成为神经科学中普遍存在的范式。在认知发展，衰老和疾病中，重要的是要了解大脑的结构和功能如何随着时间的流逝而变化，以提供对个体差异以及不同行为和疾病的机制的见解。但是，传统模型主要将大脑图视为“静态”，而忽略了随着时间的流逝的潜在变化。该项目旨在开发新的方法来建模大脑图的动力学，这些动态在产生准确，可解释和公平的预测方面具有牢固的态度。这个跨学科项目将为参与研究人员提供独特的培训组合，研究结果将纳入教育中。研究人员将通过既定的基准平台，新出版物，教程以及与域专家的合作来传播他们的发现。本项目旨在克服现有的静态脑图模型的障碍，并开发处理和分析从动态神经数据中得出的处理和分析的复杂脑图的实用基础和计算工具。该项目将开发大脑图的统一框架普通微分方程（BRAINGDE）相互作用的先进深度图学习技术和普通的微分方程，从而解决了数据复杂性，模型解释性，公平性和可靠性以及临床转换的挑战。计划的研究任务将重点关注：（1）与域专家合作，单形动态脑图挖掘，（2）多模式动态脑图挖掘和（3）临床研究。如果成功，这项研究将重塑生物信息学和医疗保健技术中临时数据挖掘的深度学习方法。该项目中建立的动态图挖掘框架还将指导有关敏感性，知识发现，推理和对具有结构的高维动态数据的推断的研究的研究，并将作为在此方向上未来工作的普遍基准。该奖项反映了NSF的法定任务，并通过使用基础的智力来评估来评估NSF的法定任务，并以基础的智力效果和广泛的评估来进行评估。