Laboratory of Neurobiology in Medicine, School of Biomedical Engineering, Tianjin Medical University, Tianjin, China.
PLoS One. 2014 Mar 21;9(3):e91481. doi: 10.1371/journal.pone.0091481. eCollection 2014.
Working memory (WM) is critically important in cognitive tasks. The functional connectivity has been a powerful tool for understanding the mechanism underlying the information processing during WM tasks. The aim of this study is to investigate how to effectively characterize the dynamic variations of the functional connectivity in low dimensional space among the principal components (PCs) which were extracted from the instantaneous firing rate series. Spikes were obtained from medial prefrontal cortex (mPFC) of rats with implanted microelectrode array and then transformed into continuous series via instantaneous firing rate method. Granger causality method is proposed to study the functional connectivity. Then three scalar metrics were applied to identify the changes of the reduced dimensionality functional network during working memory tasks: functional connectivity (GC), global efficiency (E) and casual density (CD). As a comparison, GC, E and CD were also calculated to describe the functional connectivity in the original space. The results showed that these network characteristics dynamically changed during the correct WM tasks. The measure values increased to maximum, and then decreased both in the original and in the reduced dimensionality. Besides, the feature values of the reduced dimensionality were significantly higher during the WM tasks than they were in the original space. These findings suggested that functional connectivity among the spikes varied dynamically during the WM tasks and could be described effectively in the low dimensional space.
工作记忆（WM）在认知任务中至关重要。功能连接已成为理解 WM 任务期间信息处理背后机制的有力工具。本研究旨在探讨如何有效地描述从瞬时放电率序列中提取的主成分（PC）之间的低维空间中功能连接的动态变化。通过微电极阵列植入的大鼠的中前额叶皮层（mPFC）获得尖峰，并通过瞬时放电率方法将其转换为连续序列。采用格兰杰因果关系方法来研究功能连接。然后，应用三个标量度量来识别工作记忆任务期间降低维数功能网络的变化：功能连接（GC）、全局效率（E）和因果密度（CD）。作为比较，还计算了 GC、E 和 CD 来描述原始空间中的功能连接。结果表明，这些网络特征在正确的 WM 任务期间动态变化。度量值增加到最大值，然后在原始和降低维数中都减小。此外，在 WM 任务期间，降低维数的特征值明显高于原始空间。这些发现表明，WM 任务期间尖峰之间的功能连接是动态变化的，可以在低维空间中有效地描述。
