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来源。现在迫切需要一种计算方法来从这些数据中得出大脑结构-功能的关系。虽然目前依赖于fMRI数据的网络表示的方法为大脑连接提供了有用的见解，但它们在发现和验证大脑功能底层的复杂功能机制方面存在重大局限性：功能网络中节点的概念很难定义，主体内部和主体内部的可变性导致主体内部和主体之间的网络表示差异很大；不同大脑区域活动之间的静态关联只能提供大脑活动的部分图像，而大脑活动本质上是动态的。这个探索性研究项目旨在引入动态网络分析技术来发现大脑功能区域，并研究它们随时间和学科的演变。它利用研究团队在分析连续和高度可变的时空气候数据方面的经验来解决分析从fMRI数据构建的大脑网络的几个突出挑战。本探索性研究的一个关键目标是探索分析fMRI数据的网络表示的可行性，以回答以下问题：在fMRI扫描期间，节点是否随时间动态变化？什么样的社区检测技术最适合动态网络？动态大脑网络中出现了什么模式？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，最终可能导致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的脑功能随时间的变化，包括不同人群（如青少年、患有特定脑部疾病的人等）大脑活动的特征模式。该项目为神经科学家和计算机科学家之间的跨学科合作提供了更多的机会，并为明尼苏达大学的研究生和博士后提供了基于研究的高级培训。将项目产生的算法的开源实现免费传播给更大的研究社区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.