Objective: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings, have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. Approach: Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index , ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way Intraclass Correlation Coefficient analysis (ICC). Main results: For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p=0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p <0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods , ICC was 0.21 (0.21; [0.056-0.35]). Significance: When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.
Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of >0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data ( p < 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ≤ 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters.
A novel method is described for mapping dynamic cerebral blood flow autoregulation to assess autoregulatory efficiency throughout the brain, using magnetic resonance imaging (MRI). Global abnormalities in autoregulation occur in clinical conditions, including stroke and head injury, and are of prognostic significance. However, there is limited information about regional variations. A gradient-echo echo-planar pulse sequence was used to scan the brains of healthy subjects at a rate of 1 scan/second during a transient decrease in arterial blood pressure provoked by a sudden release of pressure in bilateral inflated thigh cuffs. The signal decrease and subsequent recovery were analyzed to provide an index of autoregulatory efficiency (MRARI). MRI time-series were successfully acquired and analyzed in eleven subjects. Autoregulatory efficiency was not uniform throughout the brain: white matter exhibited faster recovery than gray (MRARI = 0.702 vs. 0.672, p = 0.009) and the cerebral cortex exhibited faster recovery than the cerebellum (MRARI = 0.669 vs. 0.645, p = 0.016). However, there was no evidence for differences between different cortical regions. Differences in autoregulatory efficiency between white matter, gray matter and the cerebellum may be a result of differences in vessel density and vasodilation. The techniques described may have practical importance in detecting regional changes in autoregulation consequent to disease.
Novel MRI-based dynamic cerebral autoregulation (dCA) assessment enables the estimation of both global and spatially discriminated autoregulation index values. Before exploring this technique for the evaluation of focal dCA in acute ischaemic stroke (AIS) patients, it is necessary to compare global dCA estimates made using both TCD and MRI. Both techniques were used to study 11 AIS patients within 48 h of symptom onset, and nine healthy controls. dCA was assessed by the rate of return of CBFV (R) following a sudden drop induced by the thigh cuff manoeuvre. No significant between-hemisphere differences were seen in controls using either the TCD or MRI technique. Inter-hemisphere averaged R values were not different between TCD (1.89 ± 0.67%/s) and MRI (2.07 ± 0.60%/s) either. In patients, there were no differences between the affected and unaffected hemispheres whether assessed by TCD (R 0.67 ± 0.72 vs. 0.98 ± 1.09%/s) or MRI (0.55 ± 1.51 vs. 1.63 ± 0.63%/s). R for both TCD and MRI was impaired in AIS patients compared to controls in both unaffected and affected hemispheres (ANOVA, p = 0.00005). These findings pave the way for wider use of MRI for dCA assessment in health and disease.
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