Electrical Geodesics, Inc., Eugene OR, USA ; Department of Psychology, University of Oregon, Eugene OR, USA.
Aptima, Inc., Woburn MA, USA.
Front Hum Neurosci. 2014 Jan 29;8:4. doi: 10.3389/fnhum.2014.00004. eCollection 2014.
Working memory (WM) is one of the most studied cognitive constructs. Although many neuroimaging studies have identified brain networks involved in WM, the time course of these networks remains unclear. In this paper we use dense-array electroencephalography (dEEG) to capture neural signals during performance of a standard WM task, the n-back task, and a blend of principal components analysis and independent components analysis (PCA/ICA) to statistically identify networks of WM and their time courses. Results reveal a visual cortex centric network, that also includes the posterior cingulate cortex, that is active prior to stimulus onset and that appears to reflect anticipatory, attention-related processes. After stimulus onset, the ventromedial prefrontal cortex, lateral prefrontal prefrontal cortex, and temporal poles become associated with the prestimulus network. This second network appears to reflect executive control processes. Following activation of the second network, the cortices of the temporo-parietal junction with the temporal lobe structures seen in the first and second networks re-engage. This third network appears to reflect activity of the ventral attention network involved in control of attentional reorientation. The results point to important temporal features of network dynamics that integrate multiple subsystems of the ventral attention network with the default mode network in the performance of working memory tasks.
工作记忆 (WM) 是研究最多的认知结构之一。尽管许多神经影像学研究已经确定了与 WM 相关的大脑网络,但这些网络的时间进程仍不清楚。在本文中,我们使用高密度脑电图 (dEEG) 在执行标准 WM 任务(n-back 任务)期间捕获神经信号,并使用主成分分析和独立成分分析的混合方法 (PCA/ICA) 从统计学上确定 WM 网络及其时间进程。结果揭示了一个以视觉皮层为中心的网络,该网络还包括后扣带皮层,在刺激出现之前活跃,似乎反映了预期、与注意力相关的过程。刺激出现后,腹侧前额叶皮层、外侧前额叶皮层和颞极与前刺激网络相关联。第二个网络似乎反映了执行控制过程。第二个网络被激活后,与第一个和第二个网络中颞叶结构相关的颞顶联合皮层重新参与。第三个网络似乎反映了参与注意力重新定向控制的腹侧注意网络的活动。研究结果指出了网络动态的重要时间特征,这些特征将腹侧注意网络的多个子系统与执行 WM 任务时的默认模式网络整合在一起。