Assessment of dynamic changes in cerebral autoregulation
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Frank Noack
Abstract
Cerebral autoregulation (CA) is a control mechanism that adjusts cerebral vasomotor tone in response to changes in arterial blood pressure (ABP) to ensure a nearly constant cerebral blood flow. Patient treatment could be optimized if CA monitoring were possible. Whereas the concept of static CA assessment is simply based on comparison of mean values obtained from two stationary states (e.g., before and after a pressure change), the evaluation of dynamic CA is more complex. Among other methods, moving cross-correlation analysis of slow waves in ABP and cerebral blood flow velocity (CBFV) seems to be appropriate to monitor CA quasi-continuously. The calculation of an “instantaneous transfer function” between ABP and CBFV oscillations in the low-frequency band using the Wigner-Ville distribution may represent an acceptable compromise in time-frequency resolution for continuous CA monitoring.
References
[1] Diehl RR. Cerebral autoregulation studies in clinical practice. Eur J Ultrasound2002; 16: 31–36.10.1016/S0929-8266(02)00048-4Search in Google Scholar
[2] Tiecks FP, Lam AM, Aaslid R, Newell DW. Comparison of static and dynamic cerebral autoregulation measurements. Stroke1995; 26: 1014–1019.10.1161/01.STR.26.6.1014Search in Google Scholar PubMed
[3] Panerai RB. Assessment of cerebral pressure autoregulation in humans – a review of measurement methods. Physiol Meas1998; 19: 305–338.10.1088/0967-3334/19/3/001Search in Google Scholar PubMed
[4] Giller CA, Bowman G, Dyer H, Mootz L, Krippner W. Cerebral arterial diameters during changes in blood pressure and carbon dioxide during craniotomy. Neurosurgery1993; 32: 737–742.10.1097/00006123-199305000-00006Search in Google Scholar
[5] Panerai RB, Rennie JM, Kelsall AWR, Evans DH. Frequency-domain analysis of cerebral autoregulation from spontaneous fluctuations in arterial blood pressure. Med Biol Eng Comput1998; 36: 315–322.10.1007/BF02522477Search in Google Scholar PubMed
[6] Schröder T, Hagmüller A, Morgenstern U, Steinmeier R. Präzisierung und Applikation des Cuff-Deflation-Tests zur Beurteilung der zerebralen Autoregulation. In: Jahrestagung der Deutschen Gesellschaft für Biomedizinische Technik, Salzburg, 2003. Biomed Tech2003; 48(Suppl 1): 336–337.10.1515/bmte.2003.48.s1.336Search in Google Scholar
[7] Reinhard M, Hetzel A, Hinkov V, Lucking CH. Cerebral haemodynamics during the Mueller manoeuvre in humans. Clin Physiol2000; 20: 292–303.10.1046/j.1365-2281.2000.00262.xSearch in Google Scholar PubMed
[8] Diehl RR, Linden D, Lucke D, Berlit P. Phase relationship between cerebral blood flow velocity and blood pressure. A clinical test of autoregulation. Stroke1995; 26: 1801–1804.10.1161/01.STR.26.10.1801Search in Google Scholar
[9] Diehl RR, Linden D, Lucke D, Berlit P. Spontaneous blood pressure oscillations and cerebral autoregulation. Clin Auton Res1998; 8: 7–12.10.1007/BF02267598Search in Google Scholar PubMed
[10] Czosnyka M, Smielewski P, Kirkpatrick P, et al. Continuous assessment of the cerebral vasomotor reactivity in head injury. Neurosurgery1997; 41: 11–17.10.1097/00006123-199707000-00005Search in Google Scholar PubMed
[11] Kuo TBJ, Chern C-M, Sheng W-Y, Wong W-J, Hu H-H. Frequency domain analysis of cerebral blood flow velocity and its correlation with arterial blood pressure. J Cereb Blood Flow Metab1998; 18: 311–318.10.1097/00004647-199803000-00010Search in Google Scholar PubMed
[12] Panerai RB, White RP, Markus HS, Evans DH. Grading of cerebral dynamic autoregulation from spontaneous fluctuations in arterial blood pressure. Stroke1998; 29: 2341–2346.10.1161/01.STR.29.11.2341Search in Google Scholar
[13] Zhang R, Zuckerman JH, Levine BD. Spontaneous fluctuations in cerebral blood flow: insights from extended-duration recordings in humans. Am J Physiol Heart Circ Physiol2000; 278: H1848–H1855.10.1152/ajpheart.2000.278.6.H1848Search in Google Scholar PubMed
[14] Steinmeier R, Bauhuf C, Hubner U, et al. Slow rhythmic oscillations of blood pressure, intracranial pressure, microcirculation, and cerebral oxygenation. Dynamic interrelation and time course in humans. Stroke1996; 27: 2236–2243.10.1161/01.STR.27.12.2236Search in Google Scholar PubMed
[15] Czosnyka M, Smielewski P, Kirkpatrick P, et al. Monitoring of cerebral autoregulation in head-injured patients. Stroke1996; 27: 1829–1834.10.1161/01.STR.27.10.1829Search in Google Scholar
[16] Kuo TBJ, Chern C-M, Yang CCH, et al. Mechanisms underlying phase lag between systemic arterial blood pressure and cerebral blood velocity. Cerebrovasc Dis2003; 16: 402–409.10.1159/000072564Search in Google Scholar PubMed
[17] Priestley MB. Spectral analysis and time series. San Diego: Academic Press 1981.Search in Google Scholar
[18] Flandrin P. Time-frequency/time-scale analysis. San Diego: Academic Press 1999.Search in Google Scholar
[19] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology Circulation. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 1996; 93: 1043–1065.Search in Google Scholar
[20] Stauss HM. Heart rate variability. Am J Physiol Regul Integr Comp Physiol2003; 285: 927–931.Search in Google Scholar
[21] Hagmüller A, Schröder T, Morgenstern U, Christ M, Steinmeier R. Kontinuierliches Monitoring der Cerebralen Autoregulation mit Hilfe der Kreuzkorrelation. In: Jahrestagung der Deutschen Gesellschaft für Biomedizinische Technik, Salzburg, 2003. Biomed Tech2003; 48(Suppl 1): 484–485.10.1515/bmte.2003.48.s1.484Search in Google Scholar
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