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Non-Linear Characterisation of Cerebral Pressure-Flow Dynamics in Humans

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  • Saqib Saleem
  • Paul D Teal
  • W Bastiaan Kleijn
  • Terrence O’Donnell
  • Trevor Witter
  • Yu-Chieh Tzeng

Abstract

Cerebral metabolism is critically dependent on the regulation of cerebral blood flow (CBF), so it would be expected that vascular mechanisms that play a critical role in CBF regulation would be tightly conserved across individuals. However, the relationships between blood pressure (BP) and cerebral blood velocity fluctuations exhibit inter-individual variations consistent with heterogeneity in the integrity of CBF regulating systems. Here we sought to determine the nature and consistency of dynamic cerebral autoregulation (dCA) during the application of oscillatory lower body negative pressure (OLBNP). In 18 volunteers we recorded BP and middle cerebral artery blood flow velocity (MCAv) and examined the relationships between BP and MCAv fluctuations during 0.03, 0.05 and 0.07Hz OLBNP. dCA was characterised using project pursuit regression (PPR) and locally weighted scatterplot smoother (LOWESS) plots. Additionally, we proposed a piecewise regression method to statistically determine the presence of a dCA curve, which was defined as the presence of a restricted autoregulatory plateau shouldered by pressure-passive regions. Results show that LOWESS has similar explanatory power to that of PPR. However, we observed heterogeneous patterns of dynamic BP-MCAv relations with few individuals demonstrating clear evidence of a dCA central plateau. Thus, although BP explains a significant proportion of variance, dCA does not manifest as any single characteristic BP-MCAv function.

Suggested Citation

  • Saqib Saleem & Paul D Teal & W Bastiaan Kleijn & Terrence O’Donnell & Trevor Witter & Yu-Chieh Tzeng, 2015. "Non-Linear Characterisation of Cerebral Pressure-Flow Dynamics in Humans," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-19, September.
    • Handle: RePEc:plo:pone00:0139470
    DOI: 10.1371/journal.pone.0139470
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    References listed on IDEAS

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    1. Bashtannyk, David M. & Hyndman, Rob J., 2001. "Bandwidth selection for kernel conditional density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 36(3), pages 279-298, May.
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