Statistical criteria for estimation of the cerebral autoregulation index (ARI) at rest.

The autoregulation index (ARI) can reflect the effectiveness of cerebral blood flow (CBF) control in response to dynamic changes in arterial blood pressure (BP), but objective criteria for its validation have not been proposed. Monte Carlo simulations were performed by generating 5 min long random input/output signals that mimic the properties of mean beat-to-beat BP and CBF velocity (CBFV) as usually obtained by non-invasive measurements in the finger (Finometer) and middle cerebral artery (transcranial Doppler ultrasound), respectively. Transfer function analysis (TFA) was used to estimate values of ARI by optimal fitting of template curves to the output (or CBFV) response to a step change in input (or BP). Two-step criteria were adopted to accept estimates of ARI as valid. The 95% confidence limit of the mean coherence function (0.15-0.25 Hz) ([Formula: see text]) was estimated from 15 000 runs, resulting in [Formula: see text] = 0.190 when using five segments of data, each with 102.4 s (512 samples) duration (Welch's method). This threshold for acceptance was dependent on the TFA settings and increased when using segments with shorter duration (51.2 s). For signals with mean coherence above the critical value, the 5% confidence limit of the normalised mean square error (NMSEcrit) for fitting the step response to Tieck's model, was found to be approximately 0.30 and independent of the TFA settings. Application of these criteria to physiological and clinical sets of data showed their ability to identify conditions where ARI estimates should be rejected, for example due to CBFV step responses lacking physiological plausibility. A larger number of recordings were rejected from acute ischaemic stroke patients than for healthy volunteers. More work is needed to validate this procedure with different physiological conditions and/or patient groups. The influence of non-stationarity in BP and CBFV signals should also be investigated.

The influence of calculation method on estimates of cerebral critical closing pressure.

The critical closing pressure (CrCP) of cerebral circulation is normally estimated by extrapolation of instantaneous velocity-pressure curves. Different methods of estimation were analysed to assess their robustness and reproducibility in both static and dynamic applications. In ten healthy subjects (mean ± SD age 37.5 ± 9.2 years) continuous recordings of arterial blood pressure (BP, Finapres) and bilateral cerebral blood flow velocity (transcranial Doppler ultrasound, middle cerebral arteries) were obtained at rest. Each session consisted of three separate 5 min recordings. A total of four recording sessions for each subject took place over a 2 week period. A total of 117 recordings contained 34 014 cardiac cycles. For each cardiac cycle, CrCP and resistance-area product (RAP) were estimated using linear regression (LR), principal component analysis (PCA), first harmonic fitting (H1), 2-point systolic/diastolic values (2Ps) and 2-point mean/diastolic values (2Pm). LR and PCA were also applied using only the diastolic phase (LRd, PCAd). The mean values of CrCP and RAP for the entire 5 min recording ('static' condition) were not significantly different for LRd, PCAd, H1 and 2Pm, as opposed to the other methods. The same four methods provided the best results regarding the absence of negative values of CrCP and the coefficient of variation (CV) of the intra-subject standard error of the mean (SEM). On the other hand, 'dynamic' applications, such as the transfer function between mean BP and RAP (coherence and RAP step response) led to a different ranking of methods, but without significant differences in CV SEM coherence. For the CV of the RAP step response though, LRd and PCAd performed badly. These results suggest that H1 or 2Pm perform better than LR analysis and should be used for the estimation of CrCP and RAP for both static and dynamic applications.