Functional and effective connectivity of stopping
Functional and effective connectivity of stopping
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DOI：
10.1016/j.neuroimage.2014.02.034
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发表时间：
2014-07-01
期刊：
影响因子：
5.7
通讯作者：
Herrmann, Christoph S.
中科院分区：
文献类型：
--
作者：
Huster, Rene J.;Plis, Sergey M.;Herrmann, Christoph S.
行为抑制通常通过比较停止和继续信号的脑电图反应来研究。大多数研究只是简单地评估N200和P300事件相关电位的振幅差异，这似乎最好地对应于θ和δ频段的活动增加。然而，既没有成功的行为抑制的可靠指标被确定，也没有停止相关的神经认知过程的因果依赖关系得到解决。通过研究停止行为背后的功能和有效连接，这项研究为行为抑制的研究开辟了新的方向。组独立成分分析被用来推断功能相干网络从脑电图数据，这是从健康的人类参与者在处理停止信号任务。然后，通过贝叶斯网络估计，确定独立组件之间的因果依赖关系的时间动态。平均聚类系数和特征路径长度测量指示130和180毫秒之间和420和500毫秒之间的时间窗口，以表达条件之间的显着不同的连接配置文件。三个组成部分显示了显着的相关性之间的120和260 ms的停止信号的反应时间和失败的停止次数。其中两个组件作为因果流的来源，一个捕获P300/三角洲的特点，而另一个的特点是由α功率耗尽puppet代表的评价或处理的刺激功能。虽然结果表明，P300和相关的δ活动似乎是统计依赖于早期的过程与行为抑制，时间窗口的关键抑制符合因果模式的早期变化，并在很大程度上先于去和停止试验之间的峰值幅度差异。总之，利用停止相关的连接的分析，以前未被发现的模式出现，值得进一步调查。(C)2014爱思唯尔公司All rights reserved.
Behavioral inhibition often is studied by comparing the electroencephalographic responses to stop and to go signals. Most studies simply assess amplitude differences of the N200 and P300 event-related potentials, which seem to best correspond to increased activity in the theta and delta frequency bands, respectively. However, neither have reliable indicators for successful behavioral inhibition been identified nor have the causal dependencies of stop-related neurocognitive processes been addressed yet. By studying functional and effective connectivity underlying stopping behavior, this study opens new directions for the investigation of behavioral inhibition. Group independent component analysis was used to infer functionally coherent networks from electroencephalographic data, which were recorded from healthy human participants during processing of a stop signal task. Then, the temporal dynamics of causal dependencies between independent components were identified by means of Bayesian network estimations. The mean clustering coefficient and the characteristic path length measure indicated time windows between 130 and 180 ms and between 420 and 500 ms to express significantly different connectivity profiles between conditions. Three components showed significant correlations between 120 and 260 ms with stop signal reaction times and the number of failed stops. Two of these components acted as sources of causal flow, one capturing P300/delta characteristics while the other was characterized by alpha power depletion putatively representing the evaluation or processing of stimulus features. Although results suggest that the P300 and associated delta activity seem to be statistically dependent on earlier processes associated with behavioral inhibition, the time window critical for inhibition coincides with early changes in causal patterns and largely precedes peak amplitude differences between go and stop trials. Altogether, utilizing the analysis of stopping-related connectivity, previously undetected patterns emerged that warrant further investigation. (C) 2014 Elsevier Inc. All rights reserved.