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<p>This study explores graph theory methods for analyzing the functional connectivity of multiple spike trains. We study simultaneously recorded multiple spike trains recorded from the visual cortex of a cat under different visual stimuli. To find the functional connectivity for a given visual stimulus we use the Cox method (Masud and Borisyuk, J Neurosci Methods 196:201–219, 2011). The application of graph theory methods for analysing the connectivity circuit, revealed that the functional connectivity of multiple spike trains is characterized by low density, long communication distances, and weak interconnectivity. Nevertheless, some spike trains also exhibit high degrees of centrality, including betweenness centrality, expansiveness coefficient, and attractiveness coefficient. Additionally, the analysis also identified significant motifs within the functional connections. Thus, our approach allows to describe the correspondence between the stimulus and functional connectivity diagram and compare functional connections under different stimuli.</p>

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Graph theory methods for analyzing functional connectivity in multiple spike trains: application to data recorded from the visual cortex of a cat

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • Mohammad Shahed Masud,
  • Danko Nikolić,
  • Liz Stuart,
  • Roman Borisyuk

摘要

This study explores graph theory methods for analyzing the functional connectivity of multiple spike trains. We study simultaneously recorded multiple spike trains recorded from the visual cortex of a cat under different visual stimuli. To find the functional connectivity for a given visual stimulus we use the Cox method (Masud and Borisyuk, J Neurosci Methods 196:201–219, 2011). The application of graph theory methods for analysing the connectivity circuit, revealed that the functional connectivity of multiple spike trains is characterized by low density, long communication distances, and weak interconnectivity. Nevertheless, some spike trains also exhibit high degrees of centrality, including betweenness centrality, expansiveness coefficient, and attractiveness coefficient. Additionally, the analysis also identified significant motifs within the functional connections. Thus, our approach allows to describe the correspondence between the stimulus and functional connectivity diagram and compare functional connections under different stimuli.