Suppose you want to take a car for a test drive. You prefer a smooth ride, so you
are probably particularly interested in the car's suspension system. Where do you
take your car? Will you take it for a ride on a well‐maintained highway, or will you
select worn‐down roads with cobblestones, potholes and speed bumps? The answer is
clear: you can't test the car's suspension system if you don't challenge it. Now imagine
cerebral autoregulation (CA) as our brain's suspension system, dampening out fluctuations
in blood flow as blood pressure varies. In everyday life, ‘bumps’ in blood pressure
can be caused by standing up after lying or sitting (orthostatic blood pressure changes),
by exercise, emotional stress, medication or infection. The magnitude of these transient
changes in blood pressure, for example during orthostatic changes, can easily reach
20% of baseline blood pressure levels (van Beek et al. 2008).
Dynamic cerebral autoregulation (dCA) is the concept (or construct) that refers to
how the cerebral vasculature counteracts these transient changes in blood pressure.
It is an essential concept in human physiology because the unique upright human posture
also makes our species uniquely vulnerable to orthostatic hypotension. Being upright
requires constant physiological adaptation to counteract the effects of gravity on
our circulation, which favours the pooling of blood in the lower half of the body
– precisely where our brain is not. Multiple factors can influence the body's ability
to maintain blood pressure when upright, such as fluid homeostasis, autonomic function,
baroreflex function, drugs that affect the cardiovascular system, infection, lower
body muscle contraction, exercise or stress. Together these factors create daily challenges
for blood pressure stability, or the ‘speed bumps’ in blood pressure that in turn
challenge cerebral autoregulation. The maximum decrease in systolic blood pressure
upon standing increases from roughly 30 mmHg between 50 and 60 years of age to 40 mmHg
in those over 80, but 95% confidence intervals range from 10 to over 70 mmHg in the
normal population (Finucane et al. 2014). Sudden changes in blood pressure of this
magnitude strongly affect cerebral blood flow and cause perturbations in flow that
are almost equal in magnitude (Claassen et al. 2009). These large challenges might
be compared to the spontaneous random fluctuations in blood pressure in adults at
rest, which may typically show a standard deviation of about 6 mmHg (Simpson D., unpublished
results from 18 min of recording in 20 healthy young adults).
Therefore, if we want to study cerebral autoregulation in an ecologically meaningful
manner, i.e. representative of its main purpose in daily life, do we choose to study
cerebral autoregulation while it is operating idly in standby mode, while supine or
sitting at rest, or do we want to engage cerebral autoregulation by challenging it
in a manner that is more representative of when its brain‐protective function is actually
Below, we will present a brief overview of the various methods that have been investigated
to quantify dynamic cerebral autoregulation and discuss the complexity of assessing
its function, with particular reference to why increased blood pressure challenges
might be preferred.
Currently there is no gold standard to assess dCA, neither for the experimental protocol
nor for how to process the recorded signals of arterial blood pressure (ABP) and cerebral
blood flow (CBF) (Claassen et al. 2016). Indeed, the notion that there would be a
gold standard to assess CA may be unrealistic given the complexity of the mechanisms
and their possible dysfunctions that underlie the dynamic pressure–flow relationship
of the cerebral circulation. Presently, two categories of methods can be identified:
measurements with only spontaneous fluctuations in ABP that are taken at rest (supine,
reclining or sitting), and methods that have been proposed to cause larger changes
in ABP. These include the inflation and release of thigh cuffs, lower‐body negative
pressure, a cold pressor test, hand‐grip exercise, Valsalva manoeuvre, and sit‐to‐stand
and squat‐to‐stand manoeuvres (e.g. Panerai, 1998; van Beek et al. 2008; Payne, 2016).
Related are tests of neurovascular coupling (mental tests; passive arm movement) and
cerebrovascular reactivity (hypo‐ and hypercapnia; acetazolamide) and tests of static
cerebral autoregulation (sCA) involving the use of drugs to raise or lower mean arterial
blood pressure over extended periods rather than as relatively brief transients.
The advantage that these methods using induced oscillations in ABP may have are twofold.
First, as indicated in the introduction, their larger perturbations in ABP and CBF
are representative of physiologically and clinically relevant everyday challenges
to CA, where CA responses probably have a necessary protective function. Second, these
large perturbations allow us to study the CBF response to ABP with increased certainty
that there is a causal relationship between ABP and CBF, which is a prerequisite to
assessing CA. With smaller challenges, the response may be masked by the spontaneous
variations and other sources of noise in the data.
But can we be certain that the CA we assess using large, induced perturbations is
comparable to the CA we assess with smaller, spontaneous perturbations?
There is some evidence that autoregulatory responses to relatively large changes in
ABP are similar to those resulting from small changes. In a direct comparison between
transfer function analysis (TFA) of spontaneous versus induced oscillations (squat–stand
manoeuvres), the TFA parameters’ gain and phase were similar, with an expected higher
coherence for the squat–stand manoeuvres (Claassen et al. 2009). Panerai et al. (2001)
found great similarity in the ratio of increase in CBF velocity over the increase
in ABP for a number of different protocols, including spontaneous responses and induced
larger ABP changes, when calculated for the mean of the sample. However, the effect
on within‐ and between‐subject dispersion of the different protocols was not tested.
Robustness of measures, including a well‐defined and narrow range of values in healthy
subjects (which can be clearly distinguished from those found in impairment), and
repeatability are additional concerns, as is ‘convergent validity’ (different measures
deemed to quantify the same physiological construct provide correlated results) (Tzeng
et al. 2012).
Finally, we must consider the possibility that the CA responses to induced, large
changes in ABP and the CA responses to small spontaneous ABP changes could present
two different ‘modalities’ of CA. The responses to increased ABP changes may reflect
the more basic and constant underlying mechanisms of CA, while CA responses to spontaneous
ABP variations may reflect more time‐varying and context‐dependent modulations in
Could enhanced oscillations solve CA's poor reproducibility and poor correlation between
CA metrics? Tzeng et al. (2012) found a poor correlation between different dCA measures
that were obtained during spontaneous variations in ABP. These results provide a challenge
to the common construct of autoregulation, as well as the choice of experimental and
signal processing methods. In addition to this low correlation between measures, low
repeatability of dCA measures was also observed in a recent multicentre study (CARNet
2, Sanders M & Elting J.W., in preparation), and was also found in previous works
(Brodie et al. 2009; Gommer et al. 2010). Considerable changes over time within the
same recording have also been noted in estimates of autoregulation at rest (Panerai
et al. 2003).
Thus the question arises whether assessment of dCA during larger changes in ABP could
solve these problems of correlation and reproducibility.
A decrease in the variability of CA measures has been found to be associated with
increased ABP fluctuations, both with spontaneous variability (Liu et al. 2005) and
in ABP challenges (Birch et al. 2002; Claassen et al. 2009). This might also be expected
from theoretical signal processing considerations, given that the signal‐to‐noise
ratio in measurements tends to improve as the excitation becomes larger. It is therefore
not surprising that, for example, the detection of impaired autoregulation (during
the inhalation of 5% CO2 in air) was enhanced when ABP variability was mildly increased
using pseudorandom inflations of a thigh cuff (Katsogridakis et al. 2012).
Increased changes in ABP, however, do not guarantee good repeatability, as shown by
Mahony et al. (2000) for thigh cuffs. Strong individual difference not only in the
mean of autoregulation, but also in the repeatability across recordings made on the
same or on different days has been observed (Mahony et al. 2000; Brodie et al. 2009).
In summary, increased variations in ABP are not a ‘magic’ solution to robustly assess
CA, though there is a consistent decrease in CA variability when CA is challenged
by increased ABP variations (Birch et al. 2002; Liu et al. 2005; Claassen et al. 2009;
Katsogridakis et al. 2012). These findings suggest that enhanced ABP oscillations
may lead to better reproducibility in studies that look at repeated measures of CA
(e.g. before and after an intervention).
One concern with these protocols, however, is the extent to which these measures might
affect CA status itself. There are a number of possible factors. Manoeuvres might
change breathing patterns and hence CO2 levels, which might be exacerbated by increased
CO2 production during muscular activity. Hypercapnia is known to be a powerful inhibitor
of CA, and indeed is commonly used to provoke temporary impairment of CA in many studies
of healthy subjects. Some protocols induce powerful autonomic stimulation (e.g. Valsalva,
cold pressor) and there is some debate as to the impact of this on CBF as well as
on CA. CA responses change depending on the operating point (mean ABP or resistance–area
product) at the start of the manoeuvre (Panerai et al. 2001; Cipolla, 2009). Changes
in cerebral metabolic demand and thus mean blood flow that may also be associated
with the imposed challenges may further confound measurements. Another practical problem
is that some protocols increase movement artefacts as there is voluntary (e.g. squat–stand)
or some imposed (e.g. lower‐body negative pressure) movement, requiring additional
care in data collection but even so, often some data is lost. A major practical concern
is whether these protocols would be acceptable in a vulnerable population, such as
elderly patients or those in intensive care. Repeated sit‐to‐stand protocols were
feasible, however, in geriatric patients, including older patients with Alzheimer
dementia (Van Beek et al. 2010).
Diversity in CA measures between and within subjects challenges our understanding
of autoregulation, as well as questioning the methods used to quantify this construct.
In the study of CA from spontaneous changes in ABP, it is a common assumption that
the system is linear. The implication of this is that the blood flow response is strictly
proportional to the size of the blood pressure challenge, i.e. the response to say
a 30% change in blood pressure is six times larger than that to a 5% change in pressure.
It thus makes no allowance for a possible threshold effect with more vigorous autoregulation
following physiologically more important large swings in ABP, than to weak fluctuations
where an autoregulatory response may not be required to protect the brain. Linearity
also implies that the response to a positive‐going step in ABP is the exact inverse
of the response to a negative‐going step. There is some evidence that this may not
be justified – with slightly larger CA responses to increases in ABP than to decreases
(Panerai et al. 2001; Aaslid et al. 2007; Cipolla, 2009). Non‐linear methods that
do not make these assumptions have also been used (Chacon et al. 2011; Kostoglou et al.
2014; Marmarelis et al. 2016), and allow for responses that vary according to the
size and sign of the ABP challenge, but in general these more sophisticated models
with more degrees of freedom have not greatly improved the robustness of CA measures.
Before deciding what is the best protocol for assessing CA, what we mean by ‘best’
must be clarified. A number of possible criteria have been considered, for example
good repeatability, ability to predict clinical outcomes, ability to identify changes
in CA caused by disease and dysfunction (e.g. stroke, brain trauma) or through hyper‐
or hypocapnia, or a well‐defined range of normality. Until we clearly state the priorities,
recommendations as to which method to use to assess CA will remain open for debate.
Whether the limited robustness of current measures of CA from spontaneous variations
reflects a fundamental problem of CA or our still imperfect choice of CA parameter
to estimate from the data is still unclear. Given the current challenges in the field,
and in the absence of strong evidence in support of using only spontaneous variations,
measuring the response to a larger ABP challenge might be expected to provide greater
insight into clinically significant autoregulation and prognostic power than the lesser
excitations. While the smooth road makes for a more comfortable ride for all passengers,
the bumpy track may give a better understanding of the damping (regulatory) system
and lead to more exciting places and greater adventures in science.
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Both authors contributed to the conception or design of the work; acquisition or analysis
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D.S. is funded by the Engineering and Physical Sciences Research Council (EPSRC) (EP/K036157/1).