Time Course of Brain Network Reconfiguration Supporting Inhibitory Control
Time Course of Brain Network Reconfiguration Supporting Inhibitory Control
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DOI：
10.1523/jneurosci.2639-17.2018
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发表时间：
2018-05-02
影响因子：
5.3
通讯作者：
Miller, Gregory A.
中科院分区：
文献类型：
--
作者：
Popov, Tzvetan;Westner, Britta U.;Miller, Gregory A.
血流动力学研究最近澄清了大脑网络中实施抑制控制的关键节点和链接。虽然功能磁共振成像方法是最佳的识别大脑网络的结构，相对缓慢的时间过程的功能磁共振成像限制了表征网络操作的能力。后者对于发展大脑网络如何动态地转变以支持抑制控制的机械理解至关重要。为了解决这一关键差距，我们将光谱分辨格兰杰因果关系（GC）和随机森林机器学习工具应用于两个大型成人样本（测试样本n = 96，复制样本n = 237，总N = 333，两种性别）的人类EEG数据，他们执行了颜色词Stroop任务。时频分析证实，招聘的抑制控制伴随着较慢的行为反应与θ和α/β功率的变化。GC分析揭示了额叶内以及额叶和顶叶脑区之间的方向性不对称交换：上级额回（SFG）对背侧ACC（dACC）和额下回（IFG）的自上而下的影响，dACC对额中回（MFG）的控制，以及额顶叶交换（IFG，楔前叶，MFG）。预测分析证实了行为和脑源性变量的组合作为抑制控制需求的最佳预测因子集，SFG θ具有比dACC θ和后β更高的分类重要性，跟踪行为反应的发生。目前的研究结果提供了一个心理现象的生物学实现机制的见解：抑制控制是通过动态路由过程，在此过程中，目标响应是通过θ介导的有效连接在关键PFC节点和通过β介导的电机准备上调。
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 n = 96, replication sample n = 237, total N = 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.