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Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation

Author

Listed:
  • Hu, Kun
  • Peng, C.K.
  • Huang, Norden E.
  • Wu, Zhaohua
  • Lipsitz, Lewis A.
  • Cavallerano, Jerry
  • Novak, Vera

Abstract

Cerebral autoregulation is an important mechanism that involves dilatation and constriction in arterioles to maintain relatively stable cerebral blood flow in response to changes of systemic blood pressure. Traditional assessments of autoregulation focus on the changes of cerebral blood flow velocity in response to large blood pressure fluctuations induced by interventions. This approach is not feasible for patients with impaired autoregulation or cardiovascular regulation. Here we propose a newly developed technique—the multimodal pressure-flow (MMPF) analysis, which assesses autoregulation by quantifying nonlinear phase interactions between spontaneous oscillations in blood pressure and flow velocity during resting conditions. We show that cerebral autoregulation in healthy subjects can be characterized by specific phase shifts between spontaneous blood pressure and flow velocity oscillations, and the phase shifts are significantly reduced in diabetic subjects. Smaller phase shifts between oscillations in the two variables indicate more passive dependence of blood flow velocity on blood pressure, thus suggesting impaired cerebral autoregulation. Moreover, the reduction of the phase shifts in diabetes is observed not only in previously-recognized effective region of cerebral autoregulation (<0.1 Hz), but also over the higher frequency range from ∼0.1 to 0.4 Hz. These findings indicate that type 2 diabetes mellitus alters cerebral blood flow regulation over a wide frequency range and that this alteration can be reliably assessed from spontaneous oscillations in blood pressure and blood flow velocity during resting conditions. We also show that the MMPF method has better performance than traditional approaches based on Fourier transform, and is more suitable for the quantification of nonlinear phase interactions between nonstationary biological signals such as blood pressure and blood flow.

Suggested Citation

  • Hu, Kun & Peng, C.K. & Huang, Norden E. & Wu, Zhaohua & Lipsitz, Lewis A. & Cavallerano, Jerry & Novak, Vera, 2008. "Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(10), pages 2279-2292.
  • Handle: RePEc:eee:phsmap:v:387:y:2008:i:10:p:2279-2292
    DOI: 10.1016/j.physa.2007.11.052
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    Citations

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    Cited by:

    1. Wang, Yung-Hung & Yeh, Chien-Hung & Young, Hsu-Wen Vincent & Hu, Kun & Lo, Men-Tzung, 2014. "On the computational complexity of the empirical mode decomposition algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 159-167.
    2. Yeh, Chien-Hung & Lo, Men-Tzung & Hu, Kun, 2016. "Spurious cross-frequency amplitude–amplitude coupling in nonstationary, nonlinear signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 143-150.
    3. Max Chacón & José Luis Jara & Rodrigo Miranda & Emmanuel Katsogridakis & Ronney B Panerai, 2018. "Non-linear models for the detection of impaired cerebral blood flow autoregulation," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.
    4. Young, Hsu-Wen Vincent & Hsu, Ke-Hsin & Pham, Van-Truong & Tran, Thi-Thao & Lo, Men-Tzung, 2017. "A new approach to sparse decomposition of nonstationary signals with multiple scale structures using self-consistent nonlinear waves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 1-10.
    5. Wang, Yung-Hung & Young, Hsu-Wen Vincent & Lo, Men-Tzung, 2016. "The inner structure of empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1003-1017.

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