Dynamics of the cerebral autoregulatory response to paced hyperventilation assessed using subcomponent and time-varying analyses

Abstract

Cerebral blood flow (CBF) can be altered by a change in partial pressure of arterial CO2 (pCO2), being reduced during hyperventilation (HPV). Critical closing pressure (CrCP) and resistance area product (RAP) are parameters which can be studied to understand this change, but their dynamic response has not been investigated during paced HPV (PHPV). Seventy five participants had recordings at rest and during PHPV. Blood pressure (BP) (Finometer), bilateral CBF velocity (CBFV) (transcranial Doppler), end-tidal CO2 (capnography) and heart rate (HR) were recorded continuously. Subcomponent analysis (SCA) and time-varying CrCP, RAP and dynamic cerebral autoregulation (Autoregulation Index, ARI) were estimated comparing PHPV to poikilocapnia. PHPV caused a change in CBFV (p<0.01), EtCO2, (p<0.01), HR (p<0.001) and RAP (p<0.01). SCA demonstrated RAP was the main parameter explaining the changes in CBFV due to PHPV. The time-varying step responses for CBFV and RAP during PHPV demonstrated considerable non-stationarity compared to poikilocapnia (p<0.00001). Although time-varying ARI was temporarily depressed, after 60 s of PHPV it was significantly higher (6.81 ± 1.88) (p<0.0001) than in poikilocapnia (5.08 ± 1.86). The mean plateau of the RAP step response was -98.3 ± 58.8 % 60 s after the onset of PHPV but -71.7 ± 45.0 % for poikilocapnia (p=0.0026), with no corresponding changes in CrCP (p=0.6). Further work is needed to assess the role of sex and aging in our findings, and the potential for using RAP and CrCP to improve the sensitivity and specificity of CO2 reactivity studies in cerebrovascular conditions.

Time-Series Modeling and Forecasting of Cerebral Pressure–Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature

“…In this systematic review, 38 of the included studies investigated healthy population data [ 1 , 13 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ], 46 studied patient populations [ 2 , 3 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , …”

“…Among the studies involving healthy populations, 20 studies [ 1 , 19 , 20 , 21 , 22 , 23 , 26 , 28 , 32 , 33 , 35 , 36 , 43 , 47 , 48 , 50 , 51 , 52 , 53 , 54 ] used time-series analysis techniques composed of frequency-domain analysis methods, TFA, and wavelet analysis; 6 studies [ 27 , 30 , 31 , 37 , 38 , 46 ] used AR time-series models, namely ARMA and ARX; and 12 studies [ 13 , 24 , 25 , 29 , 34 , 39 , 40 , 41 , 42 , 44 , 45 , 49 ] employed multiple modeling techniques, including time and/or frequency domain techniques and machine learning models, for comparative analysis at various frequency ranges, i.e., very low frequency (VLF), low frequency (LF), and high frequency (HF). These studies, listed in Table 1 , are categorized based on the modeled or forecasted cerebral physiologic signals and the corresponding modeling techniques, including a comment on their ability to model or predict.…”

“…Arterial blood pressure (ABP) and end-tidal carbon dioxide (EtCO 2 ) were also recorded in the majority of the studies, except for a select few that recorded only ABP [ 1 , 25 , 28 , 31 , 35 , 38 , 44 , 45 ], only EtCO 2 [ 29 ], or neither [ 21 , 22 , 23 , 36 , 52 , 53 ]. Some studies estimated the resistance-area product (P RA ) and critical closing pressure (CrCP) from CBFv and ABP [ 27 , 33 , 47 , 56 ].…”

“…The studies employing various AR models reported the successful modeling of the CBFv signal, several of which assessed CA [ 27 , 30 , 31 , 38 ]. Similar to time–frequency domain techniques, ICP and PbtO 2 signals were not modeled, and forecasting of the cerebral physiologic signals was not carried out.…”

“…These AR time-series models included ARMA models which is a statistical model capable of analyzing and forecasting the behavior of time-series data by combining AR and moving average (MA) components, and ARX models which represent dynamic systems with dependencies on both past values and external input signals. Among these, three studies utilized ARMA for the analysis of dynamic CA under paced hyperventilation (PHPV) ([ 27 ], p -value = 0.003) and hypercapnia [ 30 ], and to examine CBFv response during motor stimulation ([ 46 ], p -value < 0.03). Another three studies employed ARX to model CA during rest ([ 31 ], p -value < 0.3 between 1.5 min and 6 min datasets, p -value ranging from 0.54 to 0.88 between 1.5 min datasets), under noisy conditions ([ 38 ]; 5-s recovery percentage (R5%) = 92–97 ± 8 depending on noise and variation in ABP), and to assess CBFv under normocapnia and hypercapnia ([ 37 ], p -value < 0.001).…”