Abstract
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.
Highlights
Working memory (WM) refers to a brain system that provides temporary storage and manipulation of the information necessary for complex cognitive tasks [1]
In order to investigate dynamic variations of the functional connectivity during the WM tasks in rat medial prefrontal cortex (mPFC), the average values of granger causality matrix obtained from the continuous spikes (GC) were calculated (80 trials of 6 rats)
We analyzed the global efficiency (E) and the causal density (CD) in order to feature the changing tendencies of the parallel information transfer and the global coordination of the network based on granger causality matrix
Summary
Working memory (WM) refers to a brain system that provides temporary storage and manipulation of the information necessary for complex cognitive tasks [1]. The prefrontal cortex (PFC) is thought to play a critical role in memory organization and executive functions during working memory tasks [2,3]. The functional connectivity has been previously investigated to explore the mechanism of working memory [7], and provides novel insights into psychiatric and neurological disorders [8,9]. Abnormalities of the brain functional connectivity network have been observed in Alzheimer’s disease [10,11,12], which is thought to relate to the declination of WM. GCCA, originating from the field of economics and being widely used in neuroscience [14], is an effective method to investigate the interactions between variables [15]
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