A high precision method to estimate effective connectivity networks at the group and individual levels

认知脑成像的前沿正在从对孤立的个体脑区域的检查转向对分布式脑网络中多个区域之间相互作用的检查。经验证据一致表明，识别大脑网络的结构和动态是理解认知信息处理作为发展、衰老和临床条件等因素的功能的关键。在国家科学基金会的资助下，Peter C. Molenaar博士和他的同事。宾夕法尼亚州立大学大学公园的弗兰克·希拉里、李平和迈克尔·罗文正在开发一种被称为“有效连接映射”的新方法，以确定不同的大脑区域如何相互影响彼此的活动。目前可用的方法有几个已知的缺点，而拟议的项目是通过开发新的方法来明确地模拟大脑区域活动之间的同步和滞后关系来解决这些缺点。该方法对脑功能成像研究具有重要意义。它旨在有效地适应连接图中的个体差异，并进行自动数据驱动的最优网络解决方案搜索。研究人员特别强调将该方法与降维技术相结合，以识别感兴趣的大脑区域，并应用新的估计技术，允许大脑区域之间有效连接的任意时变强度。新的方法是基于双线性向量自回归模型，有或没有外部输入，并将该模型扩展到随机状态空间模型。脑成像的应用越来越广泛，越来越多地集中在理解大脑区域的活动如何整合到相互关联的网络中。每个人的大脑结构和功能都有很大的不同，由于多种因素，他们大脑区域之间的联系可能会随着时间而变化。因此，需要稳健的统计方法来可靠地识别感兴趣的大脑区域的网络。该项目旨在通过应用创新的统计方法来实现这一目标，该方法经过模拟和经验数据的广泛验证，并在软件工具中实现，可以以理论驱动和数据驱动的方式应用。研究人员正在通过大规模模拟研究和对现有数据集的应用来验证该方法。新的分析工具将在常用的计算平台上实现。为了更大的科学界的利益，这些方法也将通过开发用户友好的界面、课件和咨询向公众提供。

The frontiers of cognitive brain imaging are shifting from examinations of isolated individual brain regions to examinations of interactions between multiple regions in distributed brain networks. Empirical evidence consistently shows that identification of the structure and dynamics of brain networks is the key to understanding cognitive information processing as functions of development, aging, and clinical conditions, among other factors. With funding from the National Science Foundation, Dr. Peter C. Molenaar and his colleagues, Drs. Frank Hillary, Ping Li, and Michael Rovine of the Pennsylvania State University University Park are developing new methods, known as "effective connectivity mapping," to identify how different brain regions causally influence each other's activity. Currently available methods have several known shortcomings, and the proposed project is addressing these shortcomings by developing new methods to explicitly model both contemporaneous and time-lagged relationships among the activities of brain regions. The new method will be highly important for functional brain imaging research. It is designed to efficiently accommodate individual differences in connectivity maps and to carry out automatic data-driven search for the optimal network solutions. The researchers are giving special emphasis to integrating the methodology with dimension-reduction techniques in order to identify brain regions of interest and to application of new estimation techniques allowing for arbitrarily time-varying strengths of effective connections among brain regions. The new methodology is based on bilinear vector-autoregressive models, with or without external input, as well as extension of this model into stochastic state-space models. Application of brain imaging is widespread and increasingly focused on understanding the ways in which activities of brain regions are integrated into interconnected networks. Individual humans differ substantially in brain structure and function, and connections among their brain regions can vary across time due to multiple factors. Therefore, robust statistical methods are required to reliably identify the networks of brain regions of interest. This project is aimed at accomplishing this goal by means of the application of innovative statistical methodology that is extensively validated with simulated and empirical data and implemented in software tools that can be applied in both theory-driven and data-driven ways. The investigators are validating the methodology with large-scale simulation studies and with applications to existing data sets. The new analysis tools will be implemented in commonly used computational platforms. For the benefit of the greater scientific community, the methods will also be made publicly available through the development of user-friendly interfaces, courseware, and consultation.