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Respiratory Network Complexity in Neonatal Rat in vivo and in vitro

  • Hui Jing Yu
  • Xinnian Chen
  • Ryan M. Foglyano
  • Christopher G. Wilson
  • Irene C. Solomon
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 605)

Numerous experimental preparations from neonatal rodents have been developed to study mechanisms responsible for respiratory rhythm generation. Amongst them, the in vivo anesthetized neonatal rat preparation and the in vitro medullary slice preparation from neonatal rat are commonly used. These two preparations not only contain a different extent of the neuroanatomical axis associated with central respiratory control, but they are also studied under markedly different conditions, all of which may affect the complex dynamics underlying the central inspiratory neural network. Here, we evaluated the approximate entropy (ApEn) underlying inspiratory motor bursts as an index of inspiratory neural network complexity from each preparation to address this possibility. Our findings suggest that the central inspiratory neural network of the in vivo anesthetized neonatal rat exhibits lower complexity (i.e., more order) than that observed in the in vitro transverse medullary slice preparation, both of which are substantially lower than that observed in more intact in vitro (e.g., arterially-perfused rat) and mature in vivo (e.g., anesthetized rat, piglet, cat) preparations. We suggest that additional studies be conducted to identify the precise mechanisms responsible for the differences in central inspiratory neural network complexity between these two neonatal rat preparations.

Keywords

Approximate Entropy Burst Data Inspiratory Motor Respiratory Network Respiratory Rhythm Generation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer 2008

Authors and Affiliations

  • Hui Jing Yu
  • Xinnian Chen
  • Ryan M. Foglyano
  • Christopher G. Wilson
  • Irene C. Solomon

There are no affiliations available

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