Conditional connectivity within hippocampus using multivariate partial coherence analysis

  • Abstract
  • Keywords
  • References
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  • Abstract

    The emergence of Multiple Electrode Array recording in capturing high volumes of physiological and neuronal signals benefit researchers to study the essential processes of functional connectivity in the brain. Appropriate methods for multivariate analysis becomes crucial to study the pattern of interactions between neurons on various condition of the brain. This study was conducted to analyze conditional connectivity across different sub-regions of hippocampus, namely left CA1, right CA1, left CA3 and right CA3 in isoflurane-anaesthetized Lister-hooded rats. Pairwise conditional connectivity among neurons were computed using multivariate partial coherence analysis. Time-frequency plots show the comparison between conditional and unconditional pairwise interactions. These analyses could be used to compare the strength of direct and indirect connectivity which localized in specific frequency bands. Further investigation on particular pairwise interaction reveal the predictor neurons that contribute to the unconditional connectivity. Multivariate partial coherence analysis could reduce the network complexity by representation of true connectivity without the influence from predictor neurons as shown in unconditional relationship.



  • Keywords

    conditional network; functional connectivity; multivariate; multi-electrode; partial coherence.

  • References

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Article ID: 26039
DOI: 10.14419/ijet.v7i4.33.26039

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