Department of Psychology, University of Konstanz, 78464 Konstanz, Germany,
Department of Psychology, University of Konstanz, 78464 Konstanz, Germany.
J Neurosci. 2018 May 2;38(18):4348-4356. doi: 10.1523/JNEUROSCI.2639-17.2018. Epub 2018 Apr 10.
Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample = 96, replication sample = 237, total = 333, both sexes) who performed a color-word Stroop task. Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation. Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to inherent methodological constraints, much of this research has been unable to characterize the temporal dynamics of such networks (e.g., direction of information flow between nodes). Guided by fMRI research identifying the structure of brain networks supporting inhibitory control, results of EEG source analysis in a test sample ( = 96) and replication sample ( = 237) using effective connectivity and predictive analytics strategies advance a model of inhibitory control by characterizing the precise temporal dynamics by which this network operates and exemplify an approach by which mechanistic models can be developed for other key psychological processes.
血流动力学研究最近阐明了实现抑制控制的脑网络中的关键节点和链接。尽管 fMRI 方法是优化用于识别脑网络结构的，但 fMRI 的相对较慢的时间过程限制了表征网络运行的能力。后者对于发展对脑网络如何动态转变以支持抑制控制的机制理解至关重要。为了解决这个关键差距，我们应用了频谱分辨 Granger 因果关系（GC）和随机森林机器学习工具，对执行颜色-词 Stroop 任务的两个成年人样本（测试样本=96，复制样本=237，总样本=333，男女均有）的人类 EEG 数据进行了分析。时频分析证实，抑制控制的招募伴随着较慢的行为反应，与theta 和 alpha/beta 功率的变化有关。GC 分析揭示了额前和额前与顶间脑区之间的方向不对称交换：上额前回（SFG）对背侧前扣带回（dACC）和下额前回（IFG）的自上而下的影响，dACC 对中额前回（MFG）的控制，以及额顶间的交换（IFG、楔前叶、MFG）。预测分析证实，行为和大脑衍生变量的组合是抑制控制需求的最佳预测变量集，SFG theta 的分类重要性高于 dACC theta，而后部 beta 则跟踪行为反应的开始。本研究结果为心理现象的生物学实现提供了机制上的见解：抑制控制是通过动态路由过程实现的，在该过程中，通过关键 PFC 节点内的 theta 介导的有效连通性和通过 beta 介导的运动准备来上调目标反应。血流动力学神经影像学研究最近阐明了支持抑制控制的脑网络中的区域结构。然而，由于固有的方法学限制，这项研究中的大部分研究都无法表征这些网络的时间动态（例如，节点之间的信息流方向）。受 fMRI 研究的指导，该研究确定了支持抑制控制的脑网络结构，使用有效连通性和预测分析策略，对测试样本（=96）和复制样本（=237）的 EEG 源分析结果，通过表征该网络运行的确切时间动态，推进了抑制控制模型，并举例说明了如何为其他关键心理过程开发机制模型。
