Multiple coherence of cerebral blood flow velocity in humans

Abstract

The coherence function has been used in transfer function analysis of dynamic cerebral autoregulation to assess the statistical significance of spectral estimates of gain and phase frequency response. Interpretation of the coherence function and choice of confidence limits has not taken into account the intrinsic nonlinearity represented by changes in cerebrovascular resistance due to vasomotor activity. For small spontaneous changes in arterial blood pressure (ABP), the relationship between ABP and cerebral blood flow velocity (CBFV) can be linearized, showing that corresponding changes in cerebrovascular resistance should be included as a second input variable. In this case, the standard univariate coherence function needs to be replaced by the multiple coherence, which takes into account the contribution of both inputs to explain CBFV variability. With the use of two different indicators of cerebrovascular resistance index [CVRI ϭ ABP/CBFV and the resistance-area product (RAP)], multiple coherences were calculated for 42 healthy control subjects, aged 20 to 40 yr (28 Ϯ 4.6 yr, mean Ϯ SD), at rest in the supine position. CBFV was measured in both middle cerebral arteries, and ABP was recorded noninvasively by finger photoplethysmography. Results for the ABP ϩ RAP inputs show that the multiple coherence of CBFV for frequencies Ͻ0.05 Hz is significantly higher than the corresponding values obtained for univariate coherence (P Ͻ 10 Ϫ5 ). Corresponding results for the ABP ϩ CVRI inputs confirm the principle of multiple coherence but are less useful due to the interdependence between CVRI, ABP, and CBFV. The main conclusion is that values of univariate coherence between ABP and CBFV should not be used to reject spectral estimates of gain and phase, derived from small fluctuations in ABP, because the true explained power of CBFV in healthy subjects is much higher than what has been usually predicted by the univariate coherence functions. cerebral autoregulation; coherence function; transfer function analysis; multivariate modeling; transcranial Doppler ultrasound TRANSFER FUNCTION ANALYSIS (TFA) is a useful technique to study physiological systems whose properties are characterized by linear or quasi-linear dynamic relationships between two or more physiological time series (6, 36). One area that has benefited from this approach is the study of dynamic cerebral blood flow (CBF) autoregulation in humans (27,28,40). Aaslid et al. (2) have extended the classical concept of "static" autoregulation (20) by showing that a sudden change in arterial blood pressure (ABP) leads to a relatively fast recovery of CBF to its original level. This transient response of the autoregulatory mechanisms is what is now termed "dynamic" cerebral autoregulation (CA) (2). TFA of dynamic CA was initially proposed by Giller (13), who modeled CA as an input-output relationship between beat-to-beat values of ABP and CBF, as estimated by transcranial Doppler ultrasound (TCD) recordings of CBF velocity (CBFV). The many contributions that followed...

Nonlinear Assessment of Cerebral Autoregulation from Spontaneous Blood Pressure and Cerebral Blood Flow Fluctuations

“…This is not surprising because, unlike traditional approaches, the MMPF makes no assumptions of stationary signals and linear BP-BFV relationships and, thus, can more reliably quantify nonlinear relationship between nonstationary signals such as blood pressure and blood flow velocity. This result along with other findings indicate that inherent nonlinearities of cerebral autoregulation can be better described by nonlinear methods such as MMPF and multivariate coherence-an approach that takes into account contributions of other inputs, e.g., pressure and cerebrovascular resistances (Panerai et al 2006). Note that, for a demonstration that MMPF can be used for the assessment of autoregulation during baseline conditions, we applied the method to one selected cycle of spontaneous BP and BFV oscillations during baseline each time.…”

“…CPP is calculated from intra-arterial (ABP) and intracracranial pressure (ICP) recordings (i.e., CPP = ABP−ICP). The MMPF, as well as other autoregulation analyses (Diehl et al 1995;Tiecks et al 1995;Panerai et al 2006), quantified the BP-BFV relationship based on peripheral arterial blood pressure (ABP) due to the complicated and invasive experimental settings for ICP measurements. Though in normal condition the change in CPP is dominated by the change in ABP and many studies indicated that the ABP-BFV relationship can be used to identify the alteration and impairment in autoregulation, it is important to understand whether there is a difference between the autoregulation measures based on ABP and CPP.…”

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

“…Therefore, a single transfer function may be not sensitive enough to identify the influences of cerebral autoregulation at different time scales and to assess nonlinearities in pressure-flow relationship using a small sample size of subjects. Furthermore, another intrinsic problem of the transfer function is that low values of univariate coherences between two signals (e.g., coherence <0.5 for BP-BFV transfer function at frequency <0.07Hz) violate the assumption of a linear relationship between two signals and thus may preclude use of gain and phase as measures of coupling strength between BFV and BP [57].…”

“…These findings provide additional important evidence to support that cerebral autoregulation is a continuous dynamic process, influencing BP-BFV relationship at multiple time-scales over a larger frequency range. They also suggest that inherent nonlinearities of cerebral autoregulation can be better described by nonlinear methods such as MMPF [17,48,51], multivariate coherence [57], and general Volterra-Wiener approaches [56,58,59].…”

Cerebral Hemodynamics After Short- and Long-Term Reduction in Blood Pressure in Mild and Moderate Hypertension

“…4 -6 In contrast to static autoregulation, which describes CBF responses to steady-state changes in BP, dynamic autoregulation examines CBF responses to transient changes in BP in time scales of seconds to minutes, which may be critical in preserving CBF during rapid perturbations in BP presented in daily life. 4,6 Recently, several studies have shown that dynamic autoregulation is likely to be preserved in patients with mild to moderate hypertension 5,7,8 but becomes less effective in severe hypertension. 9 However, one important question yet to be answered is whether dynamic autoregulation is altered at the initial stages of lowering of BP consequent to effective antihypertensive treatment.…”