Division Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland; Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Cortex. 2013 Oct;49(9):2424-38. doi: 10.1016/j.cortex.2013.01.008. Epub 2013 Jan 31.
The human brain is a highly interconnected network. Recent studies have shown that the functional and anatomical features of this network are organized in an efficient small-world manner that confers high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of functional brain networks is related to performance in working memory (WM) tasks and if these networks can be modified by WM training. Therefore, we conducted a double-blind training study enrolling 66 young adults. Half of the subjects practiced three WM tasks and were compared to an active control group practicing three tasks with low WM demand. High-density resting-state electroencephalography (EEG) was recorded before and after training to analyze graph-theoretical functional network characteristics at an intracortical level. WM performance was uniquely correlated with power in the theta frequency, and theta power was increased by WM training. Moreover, the better a person's WM performance, the more their network exhibited small-world topology. WM training shifted network characteristics in the direction of high performers, showing increased small-worldness within a distributed fronto-parietal network. Taken together, this is the first longitudinal study that provides evidence for the plasticity of the functional brain network underlying WM.
人类大脑是一个高度互联的网络。最近的研究表明，这个网络的功能和解剖特征以有效的小世界方式组织，在相对较低的连接成本下赋予了高效的信息处理效率。然而，目前还不清楚功能大脑网络的结构与工作记忆 (WM) 任务的表现有何关系，以及这些网络是否可以通过 WM 训练来改变。因此，我们进行了一项双盲训练研究，共招募了 66 名年轻成年人。其中一半的受试者练习了三个 WM 任务，并与练习三个 WM 需求较低的任务的主动对照组进行比较。在训练前后记录了高密度静息状态脑电图 (EEG)，以分析皮质内水平的图论功能网络特征。WM 表现与θ频带的功率呈独特相关，而θ功率通过 WM 训练增加。此外，一个人的 WM 表现越好，他们的网络表现出的小世界拓扑结构就越多。WM 训练使网络特征朝着表现更好的人的方向转变，在分布式额顶网络中显示出更高的小世界性。总之，这是第一项提供 WM 功能大脑网络可塑性证据的纵向研究。
