EAGER: Building and analyzing dynamic brain functional networks

功能磁共振成像 (fMRI) 为了解大脑结构和功能提供了丰富的数据源。迫切需要一种计算方法来从这些数据中得出大脑结构与功能的关系。虽然目前依赖功能磁共振成像数据的网络表示的方法提供了对大脑连接性的有用见解，但它们在发现和验证大脑功能背后的复杂功能机制方面存在显着的局限性：功能网络中节点的概念本身很难定义，受试者间和受试者内的变异性导致网络表示在受试者内部和受试者之间存在很大差异；不同大脑区域活动之间相关性的静态表示仅提供了本质上动态的大脑活动的部分图像。这个探索性研究项目旨在引入动态网络分析技术来发现功能性大脑区域并研究它们随时间和跨学科的演变。它利用研究团队在分析连续且高度可变的时空气候数据方面的经验，解决了分析根据功能磁共振成像数据构建的大脑网络时面临的几个突出挑战。这项探索性研究的一个关键目标是探索分析功能磁共振成像数据的网络表示的可行性，以回答以下问题：在功能磁共振成像扫描期间，节点是否随时间动态变化？哪些社区检测技术最适合动态网络？动态大脑网络中出现什么模式？观察到的模式的统计有效性是什么？更广泛的影响：如果成功，该项目可以确定研究方向的可行性，最终可以开发出能够更好地表征正常和异常大脑功能的计算工具；更好地了解个体内部和个体之间大脑功能随时间的变化，包括不同人群（例如青少年、患有特定大脑疾病的人等）大脑活动特征的模式。该项目为神经科学家和计算机科学家之间的跨学科合作以及明尼苏达大学研究生和博士后的基于研究的高级培训提供了更多机会。该项目产生的算法的开源实现向更大的研究社区的免费传播有助于其更广泛的影响。

Funtional Magnetic Resonance Imaging (fMRI) offers a rich source of data for understanding brain structure and function. There is an urgent need for computational approaches to eliciting brain structure-function relationships from such data. While current approaches that rely on network representations of fMRI data offer useful insights into brain connectivity, they have significant limitations in terms of discovering and validating complex functional mechanisms that underly brain function: the very notion of a node in the functional network is difficult to define, inter- and intra- subject variability leads to network representations that vary a great deal within and among subjects; and static representations of correlations between activities of different brain regions offer only a partial picture of brain activity which is inherently dynamic. This exploratory research project aims to introduce dynamic network analysis techniques to discover functional brain regions and study their evolution over time and across subjects. It leverages the research team's experience in the analysis of continuous and highly variable spatio-temporal climate data to address several of the outstanding challenges in analyzing brain networks built from fMRI data. A key goal of this exploratory study is to explore the feasibility of analyzing network representations of fMRI data to answer questions such as the following: Do nodes changes dynamically with time during an fMRI scan? What community detection techniques work best for dynamic networks? What patterns arise in dynamic brain networks? What is the statistical validity of the observed patterns?Broader Impact: If successful, the project could establish the feasibility of research directions that could eventually lead to computational tools that enable better characterization of normal and abnormal brain function; better understanding of the variation of brain function within and across individuals over time, including patterns that characterize the brain activities of different populations e.g., adolescents, those suffering from specific brain disorders, etc. The project offers enhanced opportunities for interdisciplinary collaborations between neuroscientists and computer scientists, and research-based advanced training of graduate and postdoctoral students at the University of Minnesota. Free dissemination of open source implementations of the algorithms resulting from the project to the larger research community contribute to its broader impact.