Estimating the directed information to infer causal relationships in ensemble neural spike train recordings.
Estimating the directed information to infer causal relationships in ensemble neural spike train recordings.
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DOI：
10.1007/s10827-010-0247-2
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发表时间：
2011-02
影响因子：
1.2
通讯作者：
Hatsopoulos NG
中科院分区：
文献类型：
--
作者：
Quinn CJ;Coleman TP;Kiyavash N;Hatsopoulos NG
记录技术的进步使神经科学研究人员能够获得大量数据，特别是同时记录大脑不同部位的大量神经元。各种定量技术已被用来分析神经元的尖峰活动，以阐明记录的神经元的功能连接。在过去，研究人员使用了相关的措施。最近，为了更好地捕捉数据中存在的动态复杂关系，神经科学家采用了因果测量方法--其中大多数是格兰杰因果关系的变体--但收效有限。本文提出了有向信息，一个信息和控制理论的概念，作为格兰杰的因果关系的原始概念的模态独立的体现。主要特性包括：（a）它是非零的，当且仅当一个过程因果地影响另一个过程;（B）它的具体值可以解释为因果关系的强度。接下来，我们将描述在已知其他过程的情况下，两个过程之间的因果条件有向信息如何提供因果关系的网络版本：它是非零的当且仅当，在存在其他过程的现在和过去的情况下，一个过程因果地影响另一个过程。这个概念被证明是能够区分真正的直接因果影响，共同的投入，级联效应在两个以上的过程。接下来，我们描述了一个程序来估计的有向信息的神经尖峰列车使用点过程广义线性模型，最大似然估计和信息理论模型的顺序选择。我们证明，在一个模拟的神经元网络，它（a）正确识别所有成对的因果关系和（B）正确识别网络因果关系。然后，这个过程被用来分析合奏尖峰列车记录在一个清醒的猴子的初级运动皮层，同时执行目标达到的任务，揭示因果关系的方向性是一致的，从波传播的同时记录的局部场电位的预测。
Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures—most of which are variants of Granger causality—with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger's original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering causal relationships whose directionality are consistent with predictions made from the wave propagation of simultaneously recorded local field potentials.
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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：
2.9
作者：
Brown, EN;Barbieri, R;Frank, LM
通讯作者：
Frank, LM
影响因子：
2.5
作者：
Cai, HX;Kulkarni, SR;Verdú, S
通讯作者：
Verdú, S
影响因子：
5.7
作者：
David, Olivier;Kiebel, Stefan J.;Friston, Karl J.
通讯作者：
Friston, Karl J.
影响因子：
3.9
作者：
Al-khassaweneh, Mahmood;Aviyente, Selin
通讯作者：
Aviyente, Selin