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。有一个迫切需要的计算方法，从这些数据引出大脑结构功能的关系。虽然目前依赖于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的有用见解，但它们在发现和验证大脑功能基础的复杂功能机制方面具有显著的局限性：功能网络中节点的概念很难定义，受试者之间和受试者内部的差异导致网络表示在受试者内部和受试者之间变化很大;并且不同脑区域的活动之间的相关性的静态表示仅提供固有地动态的脑活动的部分图像。该探索性研究项目旨在引入动态网络分析技术，以发现功能性大脑区域，并研究其随时间和跨学科的演变。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，解决了分析从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讨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：节点动态变化，随着时间的推移，在功能磁共振成像扫描？哪些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，该项目可以建立研究方向的可行性，最终可能导致计算工具，使正常和异常的大脑功能的更好的表征;更好地了解随着时间的推移，大脑功能在个体内部和个体之间的变化，包括表征不同人群大脑活动的模式，例如，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学生提供了以研究为基础的高级培训。将该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.