Dynamics of Large Scale Cortical Networks

视觉功能依赖于许多相互关联的子过程的显著整合，如预期、刺激识别和刺激辨别。面对不断变化的处理需求，大脑能够通过协调不同神经结构的活动来协调这些基本过程。这种协调如何运作的问题是理解视觉功能的神经基础的核心。本研究旨在研究非人类灵长类动物在执行视觉模式识别任务时，大脑皮层中分布式神经元群的协调活动。它的动机是理论考虑，表明相互作用的神经元集合的大规模功能协调是视觉功能的重要组成部分（Bressler 1995,1996; Bressler & Kelso 2001）。提出了一种实验工作和方法开发相结合的方法来研究视觉功能背后的大规模皮层网络的运作。关于大规模皮层协调动力学的具体假设将通过分析大脑皮层内留置电极记录的局部场电位来检验。通过使用在我们之前的R03项目下开发的自适应多元自回归方法，可以访问油田潜在相互依赖性的快速变化动态。该方法由许多相互关联的技术组成，这些技术可以在很短的时间框架内表征大规模分布式皮层网络的多重复杂相互作用（Ding et al. 2000）。包括推导多位点相互作用模式的方法，分析特定神经元群上功能关系的依赖性，以及测量皮层区域之间的因果影响。我们的方法学发展将继续构建一个全面的框架，以优化现有技术的利用，并通过开发更强大的网络函数来处理非线性和非平稳性问题。本研究有望：(1)对视觉大尺度皮层网络的协调动力学有新的认识；(2)为研究其他认知功能背后的大规模神经系统提供新的数字信号处理工具。

Visual function depends on the remarkable integration of a number of interrelated subprocesses such as anticipation, stimulus recognition, and stimulus discrimination. The brain is able to orchestrate these elementary processes by coordinating the activities of diverse neural structures in the face of continuously varying processing demands. The question of how this coordination operates is central to understanding the neural basis of visual function. This proposal aims to investigate the coordinated activity of distributed neuronal ensembles in the cerebral cortex of non-human primates performing a visual pattern discrimination task. It is motivated by theoretical considerations suggesting that the large-scale functional coordination of interacting neuronal ensembles is an essential component of visual function (Bressler 1995, 1996; Bressler & Kelso 2001). A combined approach of experimental work and methods development is proposed to investigate the operations of large-scale cortical networks underlying visual function. Specific hypotheses concerning the dynamics of large-scale cortical coordination will be tested by analysis of local field potentials recorded from indwelling electrodes in the cerebral cortex. Access to the rapidly changing dynamics of field potential interdependency will be possible through the use of the adaptive multivariate autoregressive methodology developed under our previous R03 project. This methodology consists of a number of interrelated techniques that can characterize the multiple, complex interactions of large-scale distributed cortical networks in a very short time frame (Ding et al. 2000). Included are methods to derive multi-site interaction patterns, analyze the dependencies of functional relations on particular group of neurons, and measure causal influences between cortical areas. Our methodological development will continue by constructing a comprehensive framework for the optimal utilization of existing techniques, and by developing more powerful measures of network function that deal with problems of nonlinearity and nonstationarity. This work is expected to: (1) produce new insights into the coordination dynamics of large-scale cortical networks in vision; and (2) make available new digital signal processing tools for the investigation of large-scale neural systems underlying other cognitive functions.