Computational inference of neural information flow networks.
Computational inference of neural information flow networks.
复制标题
神经信息流网络的计算推断。
DOI：
10.1371/journal.pcbi.0020161
复制
发表时间：
2006-11-24
影响因子：
4.3
通讯作者：
Jarvis, Erich D.
中科院分区：
文献类型：
--
作者：
Smith, V. Anne;Yu, Jing;Smulders, Tom V.;Hartemink, Alexander J.;Jarvis, Erich D.
确定信息如何沿着解剖的大脑路径流动是理解动物如何感知环境、学习和行为的基本要求。人们试图用线性计算方法来揭示这种神经信息流，但众所周知，神经相互作用是非线性的。在这里，我们证明了我们最初开发的用于从微阵列收集的基因表达数据推断非线性转录调控网络的动态贝叶斯网络(DBN)推理算法也成功地从微电极阵列收集的电生理学数据推断出非线性神经信息流网络。我们从鸣禽听觉通路中恢复的网络被正确地限制在已知解剖路径的子集上，与系统的时序一致，并揭示了在听觉处理中相互反馈的重要性，以及当鸟类听到自然声音而不是合成声音时，更多的信息流向更高阶的听觉区域。应用于相同数据的线性方法不正确地产生具有流向非神经组织的信息流的网络，并且通过已知不存在的路径。据我们所知，这项研究是成功推断神经信息流网络的第一个生物验证的算法演示。大脑研究领域的挑战之一是破译以电生理信号的形式描述通信神经元之间信息流的网络。这些网络被认为负责感知和学习环境，以及产生行为。监测这些网络受到清醒动物大脑中可放置的电极数量的限制，而对这些网络的推断和推理则受到适当计算工具可用性的限制。在这里，史密斯和Yu及其同事开始解决这些问题，他们将微电极阵列植入自由活动的鸣禽的听觉通路，并使用他们为破译网络而设计的新计算工具分析数据。作者发现，他们开发的从基因表达数据中破译基因调控网络的动态贝叶斯网络算法，有效地从微电极阵列数据中推断出大脑中假定的信息流网络。他们推断的网络符合听觉系统已知的解剖学和其他生物学特性，并为听觉系统如何处理自然和合成声音提供了新的见解。作者认为，他们的结果代表了对大脑中信息流网络推理的第一次验证研究。
Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks. One of the challenges in the area of brain research is to decipher networks describing the flow of information among communicating neurons in the form of electrophysiological signals. These networks are thought to be responsible for perceiving and learning about the environment, as well as producing behavior. Monitoring these networks is limited by the number of electrodes that can be placed in the brain of an awake animal, while inferring and reasoning about these networks is limited by the availability of appropriate computational tools. Here, Smith and Yu and colleagues begin to address these issues by implanting microelectrode arrays in the auditory pathway of freely moving songbirds and by analyzing the data using new computational tools they have designed for deciphering networks. The authors find that a dynamic Bayesian network algorithm they developed to decipher gene regulatory networks from gene expression data effectively infers putative information flow networks in the brain from microelectrode array data. The networks they infer conform to known anatomy and other biological properties of the auditory system and offer new insight into how the auditory system processes natural and synthetic sound. The authors believe that their results represent the first validated study of the inference of information flow networks in the brain.
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