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Functional Connectivity among Spikes in Low Dimensional Space during Working Memory Task in Rat

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  • Mei Ouyang
  • Shuangyan Li
  • Xin Tian

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.

Suggested Citation

  • Mei Ouyang & Shuangyan Li & Xin Tian, 2014. "Functional Connectivity among Spikes in Low Dimensional Space during Working Memory Task in Rat," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-9, March.
    • Handle: RePEc:plo:pone00:0091481
    DOI: 10.1371/journal.pone.0091481
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    References listed on IDEAS

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    1. Nicoletta Nicolaou & Saverios Hourris & Pandelitsa Alexandrou & Julius Georgiou, 2012. "EEG-Based Automatic Classification of ‘Awake’ versus ‘Anesthetized’ State in General Anesthesia Using Granger Causality," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-13, March.
    2. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    1. Deng, Bin & Deng, Yun & Yu, Haitao & Guo, Xinmeng & Wang, Jiang, 2016. "Dependence of inter-neuronal effective connectivity on synchrony dynamics in neuronal network motifs," Chaos, Solitons & Fractals, Elsevier, vol. 82(C), pages 48-59.
    2. Yu, Haitao & Guo, Xinmeng & Qin, Qing & Deng, Yun & Wang, Jiang & Liu, Jing & Cao, Yibin, 2017. "Synchrony dynamics underlying effective connectivity reconstruction of neuronal circuits," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 674-687.

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