Chasing the evidence: the influence of data segmentation on estimates of dynamic cerebral autoregulation

Depression of dynamic cerebral autoregulation during neural activation: The role of responders and non-responders

Neurovascular coupling (NVC) interaction with dynamic cerebral autoregulation (dCA) remains unclear. We investigated the effect of task complexity and duration on the interaction with dCA. Sixteen healthy participants (31.6 ± 11.6 years) performed verbal fluency (naming-words (NW)) and serial subtraction (SS) paradigms, of varying complexity, at durations of 05, 30 and 60 s. The autoregulation index (ARI), was estimated from the bilateral middle cerebral artery blood velocity (MCAv) step response, calculated by transfer function analysis (TFA), for each paradigm during unstimulated (2 min) and neuroactivated (1 min) segments. Intraclass correlation (ICC) and coefficient of variation (CV) determined reproducibility for two visits and objective criteria were applied to classify responders (R) and non-responders (NoR) to task-induced MCAv increase. ICC values demonstrated fair reproducibility in all tasks. ARI decreased in right (RH) and left (LH) hemispheres, irrespective of paradigm complexity and duration (p < 0.0001). Bilateral ARI estimates were significantly decreased during NW for the R group only (p < 0.0001) but were reduced in both R (p < 0.0001) and NoR (p = 0.03) groups for SS tasks compared with baseline. The reproducible attenuation of dCA efficiency due to paradigm-induced NVC response, its interaction, and different behaviour in R and NoR, warrant further research in different physiological and clinical conditions.

Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update

Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.

COHmax: an algorithm to maximise coherence in estimates of dynamic cerebral autoregulation

OBJECTIVE

The reliability of dynamic cerebral autoregulation (dCA) parameters, obtained with transfer function analysis (TFA) of spontaneous fluctuations in arterial blood pressure (BP), require statistically significant values of the coherence function. A new algorithm (COH_max) is proposed to increase values of coherence by means of the automated, selective removal of sub-segments of data.

APPROACH

Healthy subjects were studied at baseline (normocapnia) and during 5% breathing of CO₂ (hypercapnia). BP (Finapres), cerebral blood flow velocity (CBFV, transcranial Doppler), end-tidal CO₂ (EtCO₂, capnography) and heart rate (ECG) were recorded continuously during 5 min in each condition. TFA was performed with sub-segments of data of duration (SEG_D) 100 s, 50 s or 25 s and the autoregulation index (ARI) was obtained from the CBFV response to a step change in BP. The area-under-the curve (AUC) was obtained from the receiver-operating characteristic (ROC) curve for the detection of changes in dCA resulting from hypercapnia.

MAIN RESULTS

In 120 healthy subjects (69 male, age range 20-77 years), CO₂ breathing was effective in changing mean EtCO₂ and CBFV (p < 0.001). For SEG_D = 100 s, ARI changed from 5.8 ± 1.4 (normocapnia) to 4.0 ± 1.7 (hypercapnia, p < 0.0001), with similar differences for SEG_D = 50 s or 25 s. Depending on the value of SEG_D, in normocapnia, 15.8% to 18.3% of ARI estimates were rejected due to poor coherence, with corresponding rates of 8.3% to 13.3% in hypercapnia. With increasing coherence, 36.4% to 63.2% of these could be recovered in normocapnia (p < 0.001) and 50.0% to 83.0% in hypercapnia (p < 0.005). For SEG_D = 100 s, ROC AUC was not influenced by the algorithm, but it was superior to corresponding values for SEG_D = 50 s or 25 s.

SIGNIFICANCE

COH_max has the potential to improve the yield of TFA estimates of dCA parameters, without introducing a bias or deterioration of their ability to detect impairment of autoregulation. Further studies are needed to assess the behaviour of the algorithm in patients with different cerebrovascular conditions.