Multilevel mediation techniques for fMRI

近年来，成像人脑内部运作的技术能力取得了戏剧性的进步。当人类参与心理过程时，每秒可以获得对整个人脑活动的功能磁共振成像(FMRI)测量--数十万个数据点。收集如此庞大和复杂的数据集的能力的发展已经超过了分析它们的方法。一个关键的缺口是，尽管大脑是按照连接多个大脑区域的功能路径来组织的，但没有足够的、广泛可用的方法来识别和测试人类功能路径的完整性。在国家科学基金会的支持下，Tor Wager博士和哥伦比亚大学的同事们将进行研究，旨在开发用于模拟人脑功能连接的分析技术，并为此目的生产高质量的软件，并将公开提供。他们打算开发的多层调解/缓和(M3)框架将扩展多方程系统的多层建模的最新进展，并扩展其功能以检查许多大脑区域之间的连接。M3框架将是第一个可以提供跨越两个以上区域的大脑连接的群体推断的方法。对疼痛的神经反应将被用作开发和测试该框架的模型系统，因为疼痛路径具有良好的特征。M3框架将用于识别丘脑皮质的上升路径--这是一项现有方法无法提供的创新--并研究它们的活动是如何由额叶皮质的“控制”电路调节的。总而言之，该方法和应用有望阐明如何在人脑中研究功能通路和内部反馈。这项研究将导致结构方程建模的进展，包括在多水平框架中将协方差建模为随机变量，稳健估计，以及结构模型和降维技术的融合。这项工作的另一个创新方面将是开发允许在结构模型中选择变量的递归算法，这将允许应用于大型数据集所需的混合验证性/模型构建方法。用户友好的软件和文档将使神经科学、经济学、生物工程和心理学等不同领域的研究人员能够使用这些新工具，并将鼓励更广泛地参与基于大脑的人类认知和情感过程分析。这些方法还将在课程和研讨会中传播，通过美国和国际上多学科研究小组之间的科学互动，以及通过哥伦比亚大学网站上发布的供公众下载的软件来传播。最后，统计学和认知神经科学的本科生和研究生交叉培训将使这两个领域的学生从接触新概念和技术中受益。该奖项是2006财年数学科学优先领域数学社会科学和行为科学(MSBS)特别竞赛的一部分。

Recent years have seen dramatic advances in the technical capacity to image the internal workings of the human brain. As humans engage in mental processes, functional magnetic resonance imaging (fMRI) measurements of the activity of the entire human brain -- hundreds of thousands of data points -- can be acquired each second. Developments in the ability to collect such enormous and complex datasets have outstripped methods for analyzing them. One critical gap is that whereas the brain is organized in terms of functional pathways connecting multiple brain regions, no adequate, widely-available methods exist for identifying and testing the integrity of functional pathways in humans. With support from the National Science Foundation, Dr. Tor Wager and colleagues at Columbia University will conduct research aimed at developing analysis techniques for modeling functional connectivity in the human brain and produce high-quality software for this purpose that will be made publicly available. The multilevel mediation/moderation (M3) framework that they intend to develop will extend recent advances in multilevel modeling of multi-equation systems and expands their functionality to examine connectivity among many brain regions. The M3 framework will be the first method that can provide population inference about brain connectivity spanning more than two regions. Neural responses to pain will be used as a model system to develop and test the framework because pain pathways are well-characterized. The M3 framework will be used identify ascending thalamocortical pathways - an innovation not afforded by existing methods - and to examine how their activity is regulated by "control" circuits in the frontal cortex. Together, the method and application are expected to shed light on how functional pathways and internal feedback may be investigated in the human brain. This research will lead to advances in structural equation modeling, including the modeling of covariances as random variables in a multi-level framework, robust estimation, and the fusion of structural models and dimension reduction techniques. Another innovative aspect of this work will be the development of recursive algorithms that permit variable selection in structural models, which will allow for hybrid confirmatory/model-building approaches necessary for application to large datasets. User-friendly software and documentation will make the new tools accessible to researchers in diverse fields, including neuroscience, economics, bioengineering, and psychology, and will encourage broader participation in brain-based analysis of human cognitive and affective processes. The methods will also be disseminated in courses and workshops, through scientific interactions between multi-disciplinary research groups in the U. S. and internationally, and through software posted for public download on Columbia's website. Finally, undergraduate and graduate cross-training in statistics and cognitive neuroscience will allow students in both areas to benefit from exposure to new concepts and techniques.This award was supported as part of the fiscal year 2006 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).