A Granger causality measure for point process models of ensemble neural spiking activity.
A Granger causality measure for point process models of ensemble neural spiking activity.
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DOI：
10.1371/journal.pcbi.1001110
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发表时间：
2011-03
影响因子：
4.3
通讯作者：
Brown EN
中科院分区：
文献类型：
--
作者：
Kim S;Putrino D;Ghosh S;Brown EN
识别大脑中多个神经元之间发生的定向相互作用的能力对于理解神经元群如何合作以产生特定的大脑功能至关重要。然而，评估这些相互作用的最佳方法尚未建立。格兰杰因果关系已被证明是分析多组连续值数据之间定向相互作用的有效方法，但由于其离散性，不能应用于神经脉冲序列记录。本文提出了一个点过程框架，使格兰杰因果关系能够应用于点过程数据，如神经脉冲序列。提出的框架使用点过程似然函数将神经元的峰值概率与可能的协变量联系起来，例如其自身的峰值历史和同时记录的神经元的并发活动。格兰杰因果关系是基于一个神经元的点过程可能性的相对减少来评估的，不包括它的一个协变量，与使用它的所有协变量获得的可能性相比。该方法在模拟数据上进行了测试，然后应用于猫的初级运动皮层（MI）记录的神经活动。模拟数据中存在的相互作用预测具有很高的准确性，当应用于真实神经数据时，所提出的方法确定了许多记录神经元之间的因果关系。本文提出了一种新的方法，成功地将格兰杰因果关系应用于点过程数据，并有可能在应用于神经尖峰序列时提供独特的生理见解。多电极记录技术的最新进展使得同时记录多个神经元的活动成为可能。这提供了一个机会来研究神经元群是如何在不同的大脑区域执行不同的功能时形成功能集合的。然而，大多数试图识别神经元之间联系的方法对它们所检测到的相互作用的方向性本质提供的见解很少。近年来，格兰杰因果关系被证明是一种有效的方法来推断连续值数据集之间的因果关系，但不能直接应用于点过程数据，如神经脉冲序列。在这里，我们提出了一种新颖而成功的尝试，将格兰杰因果关系的应用扩展到点过程数据。所提出的方法在模拟数据上表现良好，然后应用于从初级运动皮层同时记录的神经元组记录的真实实验数据。真实数据分析的结果表明，所提出的方法有潜力提供关于皮层网络特性的独特神经生理学见解，这是其他当代功能相互作用检测方法所不可能实现的。
The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains. Recent advances in multiple-electrode recording have made it possible to record the activities of multiple neurons simultaneously. This provides an opportunity to study how groups of neurons form functional ensembles as different brain areas perform their various functions. However, most of the methods that attempt to identify associations between neurons provide little insight into the directional nature of the interactions that they detect. Recently, Granger causality has proven to be an efficient method to infer causal relationships between sets of continuous-valued data, but cannot be directly applied to point process data such as neural spike trains. Here, we propose a novel and successful attempt to expand the application of Granger causality to point process data. The proposed method performed well with simulated data, and was then applied to real experimental data recorded from sets of simultaneously recorded neurons from the primary motor cortex. The results of the real data analysis suggest that the proposed method has the potential to provide unique neurophysiological insights about network properties in the cortex that have not been possible with other contemporary methods of functional interaction detection.
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DOI：
10.1073/pnas.0308538101
发表时间：
2004-06-29
影响因子：
11.1
作者：
Brovelli, A;Ding, MZ;Bressler, SL
通讯作者：
Bressler, SL
影响因子：
4.3
作者：
Guo S;Wu J;Ding M;Feng J
通讯作者：
Feng J
影响因子：
2.9
作者：
Brown, EN;Barbieri, R;Frank, LM
通讯作者：
Frank, LM
影响因子：
2.5
作者：
LEGENDY, CR;SALCMAN, M
通讯作者：
SALCMAN, M
影响因子：
2.9
作者：
Brody, CD
通讯作者：
Brody, CD