Directional sensitivity of dynamic cerebral autoregulation ...

Directional sensitivity of dynamic cerebral autoregulation

1

2

Directional sensitivity of dynamic cerebral autoregulation 3

in squat-stand maneuvers 4

RB Panerai1,2, SC Barnes1, Nath M1,2, N Ball1, TG Robinson1, 2, VJ Haunton1,2 5

1Department of Cardiovascular Sciences, University of Leicester, Leicester, UK. 6

2National institute for Health Research (NIHR) Leicester Biomedical Research Centre, 7

University of Leicester, Leicester, UK. 8

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10

Running head: Directional sensitivity of dynamic cerebral autoregulation 11

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14

Corresponding author: 15

Ronney B. Panerai 16

Department of Cardiovascular Sciences 17

Room 439, Robert Kilpatrick Clinical Sciences Building 18

University of Leicester 19

PO Box 65 20

Leicester LE2 7LX 21

Email: [email protected] 22

Phone: 0116 252 3130 23 24

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26

mailto:[email protected]


Abstract 27

Dynamic cerebral autoregulation (CA), the transient response of cerebral blood flow (CBF) to 28

rapid changes in arterial blood pressure (BP), is usually modelled as a linear mechanism. 29

We tested the hypothesis that dynamic CA can display non-linear behavior resulting from 30

differential efficiency dependent on the direction of BP changes. CBF velocity (CBV, 31

transcranial Doppler), heart rate (HR, 3-lead ECG), continuous BP (Finometer) and end-tidal 32

CO2 (capnograph) were measured in 10 healthy young subjects during 15 squat-stand 33

maneuvers (SSM) with a frequency of 0.05 Hz. The protocol was repeated with a median 34

[IQR] of 44 [35-64] days apart. Dynamic CA was assessed with the autoregulation index 35

(ARI), obtained from CBV step responses estimated with an autoregressive moving-average 36

model. Mean BP, HR, and CBV were different (all p<0.001) between squat and stand, 37

regardless of visits. ARI showed a strong interaction (p<0.001) of SSM with the progression 38

of transients; in general, the mean ARI was higher for the squat phase compared to 39

standing. The changes in ARI were partially explained by concomitant changes in CBV 40

(p=0.023) and pulse pressure (p<0.001), but there was no evidence that ARI differed 41

between visits (p=0.277). These results demonstrate that dynamic CA is dependent on the 42

direction of BP change, but further work is needed to confirm if this finding can be 43

generalised to other physiological conditions, and also to assess its dependency on age, sex 44

and pathology. 45

46

Keywords – cerebral blood flow, transcranial Doppler, arterial blood pressure, posture, non-47

linear behavior 48

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Introduction 52

Cerebral blood flow (CBF) is tightly controlled by metabolic, myogenic and neurogenic 53

mechanisms, usually assumed to optimise brain perfusion in response to changes in arterial 54

blood pressure (autoregulation), arterial carbon dioxide (CO2 reactivity), and oxygen demand 55

(neurovascular coupling). The speed at which these regulatory mechanisms respond to 56

different stimuli can provide insight into the underlying physiology, and also potentially 57

provide diagnostic and prognostic information in clinical studies. 58

Both CO2 reactivity and neurovascular coupling (NVC) have been shown to be directional 59

mechanisms. In other words, when changing from normocapnia to hypercapnia, CBF 60

increases with a much longer time-constant than in the reverse direction (29). With NVC, 61

different types of sensorimotor and cognitive paradigms have been shown to induce faster 62

increases in CBF at the start of stimulation than that observed when stimulation ceases and 63

flow returns to its baseline level (1, 17, 23, 30). However, in the case of autoregulation, 64

particularly its dynamic response to sudden changes in blood pressure (BP), the presence of 65

directionality remains controversial. Using repeated compression and release of thigh cuffs, 66

Aaslid et al. (2) suggested the existence of directionality based on asymmetric changes in 67

critical closing pressure following increases or decreases in BP. On the other hand, 68

Katsogridakis et al. (16), using a similar protocol, could not identify any differences in 69

measures of dynamic CA when comparing increases with decreases in BP. In a previous 70

study, differences in cerebral blood velocity (CBV) step response could not be detected for 71

maneuvers that induce either a sudden reduction in BP (e.g. single thigh cuff deflation) or 72

increases in BP (e.g. cold stress test)(26). More recently, a study based on the squat-stand 73

maneuver (SSM) concluded that directionality, or ‘hysteresis’, was a feature of BP 74

autoregulation (CA). However, this was ascertained on somewhat ‘static’ estimates of the 75

CA response based on only two values of CBV and BP during either the stand or the 76

squatting phases of the maneuver (8). 77


Determining if dynamic CA has a directional, or asymmetrical, behaviour is highly relevant 78

for both improving our understanding of the underlying physiology and potentially optimising 79

the current methods of assessment (13, 37). If dynamic CA is proven to show asymmetry in 80

relation to BP changes, this will impact on the current modelling techniques used to quantify 81

dynamic CA in health and disease, as methods will be required to take into account the 82

directionality of BP changes. On teleological grounds, a greater sensitivity of dynamic CA to 83

increases in BP may confer an evolutionary advantage given the need to protect the 84

microcirculation from surges in BP (21). On the other hand, the argument could be reversed 85

when considering the importance of avoiding syncope following acute hypotension. 86

The SSM is a suitable protocol to investigate the directional sensitivity of CA, given the 87

relatively large changes induced in BP. SSMs provide a better signal-to-noise ratio than 88

corresponding measurements with either spontaneous BP fluctuations or repeated thigh cuff 89

maneuvers (15, 20, 32). On the other hand, SSMs induce considerable changes in PaCO2 90

and other peripheral cardiovascular parameters (5) which could contribute, or even explain, 91

the apparent directionality reported by Brassard et al. (8). To address these concerns, we 92

used a modelling approach more suitable to quantify dynamic CA, to test three inter-related 93

hypotheses: i) that the validated and widely used autoregulation index (ARI), has higher 94

values during squatting as compared to the standing phase of SSMs, ii) that differences in 95

ARI between squatting and standing are reproducible in the same group of subjects, and iii) 96

that differences in ARI between squatting and standing can be explained by corresponding 97

changes in other cerebrovascular or peripheral parameters. 98

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Methods 102

A subset of data collected for a wider investigation (4) was analysed to test the specific 103

hypotheses of this study. The study was approved by the University of Leicester Ethics 104

Committee (reference 8442-vjh12-cardiovascularsciences). Healthy young subjects (<30 105

years old) provided written informed consent. 106

Physiological measurements 107

Beat-to-beat estimates of BP were obtained by arterial volume-clamping of the digital artery 108

(Finometer, FMS, Amsterdam, Netherlands). Transcranial Doppler CBV was measured in 109

the middle cerebral arteries (MCA) using 2MHz probes (Viasys Companion III) held in place 110

by a custom-built headset. A tilt-sensor was attached to the subject’s right thigh 20cm above 111

the superior border of the patella to measure the angle of the squatting motion. Nasal 112

capnography (Salter labs, ref 4000) was used to measure end-tidal CO2 (EtCO2). Heart rate 113

was measured using three-lead ECG. The right hand was held in position with a sling to 114

minimise movement throughout the recordings and to keep the BP finger cuff at heart height. 115

The servo-reset mechanism of the Finometer was disabled throughout the recordings to 116

allow for a continuous BP trace, but enabled between recordings. Intermittent brachial BP 117

was measured using a validated electrosphygmomanometer (UA 767 BP monitor) to 118

calibrate the Finometer recordings. 119

Continuous analogue recordings were digitised at 500 samples/s by a Physiological Data 120

Acquisition System (PHYSIDAS) designed by the Leicester Medical Physics Department for 121

subsequent analysis. 122

Experimental protocol 123

Experiments were performed in a well-lit, environmentally controlled, laboratory that was free 124

from distraction and maintained at a temperature of 20-24⁰C. Participants were asked to 125

avoid strenuous exercise, caffeine, smoking, large meals and alcohol in the four hours prior 126

to their visit. 127


Recordings were performed following a 5-min period of rest standing. For the purposes of 128

this study, a five minute baseline recording was performed with the subject in the standing 129

position, followed by fixed frequency SSM at a frequency of 0.05Hz, corresponding to 15 130

squats and stands, each with a duration of 10 s. A computer program provided visual cues to 131

guide the timing of the squatting motion. A period of instruction and practice preceded the 132

SSM recordings. When performing the SSMs, subjects were instructed to squat down as low 133

as they felt able. They were informed that they would need to perform 15 squats, and to take 134

this into account when choosing their depth. Throughout each recording, subjects were 135

asked to breathe through their nose and to avoid a Valsalva-like maneuver during the SSM. 136

Subjects were invited back into the laboratory at a subsequent date for repeated 137

measurements following the same protocol. 138

Data processing 139

The readings from the Finometer were calibrated to the brachial BP recordings. Data were 140

visually inspected; non-physiological spikes in CBV were removed through linear 141

interpolation. Files that contained any segments of significantly poor TCD signals were 142

excluded from further analyses. Narrow spikes (<100 ms) and artefacts were removed by 143

linear interpolation. Subsequently, all signals were filtered in the forward and reverse 144

direction using an eighth-order Butterworth low-pass filter with a cut-off frequency of 20 Hz. 145

The beginning and the end of each cardiac cycle were detected in the BP signal, and mean 146

values of BP, CBV and heart rate were obtained for each heartbeat. EtCO2 was obtained for 147

each breath, linearly interpolated and resampled to coincide with each cardiac cycle. Beat-148

to-beat parameters were interpolated with a third-order polynomial and resampled at 5 Hz to 149

generate signals with a uniform time base. 150

Under visual inspection, the beginning of each squat and stand phases of the SSM were 151

marked and the resulting transient changes in BP, CBV, heart rate, EtCO2, and pulse 152

pressure (PP) were extracted for further analyses. This corresponded to 15 squat and 15 153


standing transients for each subject. Although each phase lasted 10 s, the extracted 154

transient data were extended to 12 s to allow for the transition phase when moving from 155

squat to stand and vice-versa. The same time interval was adopted to average 156

corresponding values of BP, HR, EtCO2, CBV and pulse pressure (PP). The dynamic 157

relationship between BP and CBV for each transient was modelled using an autoregressive-158

moving average (ARMA) structure as previously described (14, 27). The CBV step response 159

to a sudden change in BP was compared with 10 template curves proposed by Tiecks et al. 160

(35) and the best fit curve corresponded to the ARI. Values of ARI = 0 indicate absence of 161

CA, whilst ARI = 9 corresponds to the most efficient CA that can be observed (35). This 162

procedure generated a value of ARI for each of the 15 squat and 15 standing transients in 163

each subject. The quality of the CBV step response fitting to Tiecks et al. model was 164

expressed by the normalised mean square error (NMSE). As previously described, NMSE 165

values ≤ 0.3 guarantee CBV step responses that are physiologically plausible (28). 166

167

Statistical analysis 168

For each transient, ARI values were rejected for NMSE>0.3 and were replaced by the mean 169

value of the preceding and subsequent transients. We modelled the data on ARI along the 170

15 transients of a subject during SSMs for both visits by a repeated measures model. We 171

developed the model in two stages. First, we identified the correlation structure between the 172

repeated measures within the same subject; we observed that the second order 173

autoregressive process along with the first order moving average represented the 174

appropriate correlation structure for the data. In the second step, we developed a multi-175

variable linear model incorporating categorical variables: maneuvers (two levels: squat and 176

standing), transients (15 levels) and visit (two levels) as predictors. The model also included 177

a two-way interaction effect of maneuvers and transients. In addition, we explored the 178

association of other covariates with ARI, namely MAP, HR, EtCO2, CBFV and PP. Using a 179

stepwise model selection based approach, we only retained the covariates that were 180


statistically significant (p<0.05). The final model included CBV and PP as covariates. We 181

used Akaike's information criterion (AIC) and Bayesian information criterion (BIC) to perform 182

the model selection in both stages. For other cerebral autoregulation (CA) covariates, 183

namely MAP, HR, EtCO2, CBV and PP, we investigated the effect of visit and maneuver 184

using similar repeated measures models accounting for the correlation between 185

measurements within the same subject. All statistical tests were two-sided with type 1 error 186

rate (p-value) of 0.05 to determine statistical significance. The fitting of a repeated measures 187

model was carried out using the R package nlme in R software environment (version 3.4). 188

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Results 192

Measurements on two separate occasions were performed in 11 subjects (2 females) with a 193

median [IQR] interval of 44 [35-64] days between visits. One male subject had poor 194

recordings of BP with the Finometer in his second visit to the laboratory and was removed 195

from the study. Good quality recordings with bilateral values of CBV were obtained in the 196

other 10 subjects, aged 22 ± 1 years. Standing at rest (‘baseline’) did not show any 197

differences between visits for the parameters in Table 1. With the exception of EtCO2, all 198

other parameters showed highly significant differences between the squat and stand phases 199

of the maneuver (Table 1). 200

The NMSE was >0.3 in 34 (5.7%) transients for the right MCA and 25 (4.1%) transients from 201

the left MCA. An example of a transient with NMSE>0.30 is shown in Fig. 4 (transient #10). 202

These outliers were interpolated as described above; they represented a small fraction of the 203

600 transients analysed for each hemisphere and were randomly distributed across visits, 204

subjects and maneuvers. Mean ± SD values of NMSE (<0.30) for the right and left MCAs for 205

both visits were 0.094 ± 0.050 and 0.095 ± 0.048, respectively. 206

No significant differences were found between values of CBV and ARI for the right and left 207

MCAs either at baseline or for the squatting and standing phases of SSM; mean values for 208

the two hemispheres were used in all subsequent analyses. 209

The CBV transients during both squatting and standing showed temporal patterns consistent 210

with an active CA response to the corresponding BP rise for the 15 squatting (Figs. 1 & 2) 211

and BP drop for the 15 standing (Figs. 3 & 4) responses from a single subject. Similar 212

behaviour was observed in all the other subjects. 213

Mean BP, PP and HR, but not EtCO2, showed marked differences between squatting and 214

standing regardless of visits (Fig. 5). Similar behavior was observed for CBV and ARI as 215

shown in Fig. 6. Most parameters showed a different behavior at the beginning of SSM, as 216

compared to steady-state values (Figs. 5 & 6), but this pattern was more accentuated for the 217


ARI (Figs. 6.C & 6.D). The repeated measures model of ARI showed a strong interaction 218

effect (p<0.0001) of SSM with the progression of transients, indicating that the differences 219

between squat and stand were also dependent on the progression of the maneuvers, as 220

observed in Fig. 6. The fraction of the total variance explained by these variables (maneuver, 221

transients and visits) was expressed by R2=0.254 (p<0.0001). The ARI differences were also 222

associated with changes in CBV, which increased R2 to 0.356 (p=0.023) and PP, which 223

increased R2 to 0.414 (p=0.0002). HR was only borderline significant, increasing R2 to 0.441 224

(p=0.073), but there was no evidence that ARI differences were associated with EtCO2 225

(p=0.50) or BP (p=0.86). The estimates of slopes (mean ± SE) of CBV, PP and HR with 226

transients were -0.0254 ± 0.0111 s.cm-1, -0.0347 ± 0.0094 mmHg-1 and 0.0156 ± 0.0084 227

bpm-1, respectively. There was no evidence that mean ARI differed between visits (p=0.277). 228

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Discussion 231

Using the autoregulation index (ARI), a well-established method to quantify the strength of 232

dynamic CA (32), we have confirmed the recent results of Brassard et al. (8), suggesting that 233

the squatting phase of SSM has a more efficient CA response than the corresponding 234

standing phase. However, by uniquely analysing the time-series of ARI and other cerebral 235

and systemic parameters (Figs. 5 & 6), we have also shown that the initial maneuvers, of a 236

total sequence of 15 squats and stands, induced cerebro- and cardiovascular changes that 237

were unstable, in comparison with the steady-state behavior observed with the subsequent 238

transients (Figs. 5 & 6). Of considerable relevance, we have observed that the changes in 239

ARI and other parameters were highly reproducible at a second visit to the laboratory, 240

approximately six weeks apart, thus suggesting that directional sensitivity is a consistent 241

phenomenon, not related to the cognitive reaction to a new procedure. When considering the 242

potential contribution of other parameters to help explain the differences in ARI between the 243


squatting and standing phases of the maneuvers, we found that both CBV and PP had 244

significant effects, contrary to BP, EtCO2 and HR. From these results, we have been able to 245

confirm the three main hypotheses of the study, as listed in the Introduction. These findings 246

stimulate a debate about the underlying mechanisms controlling CBF in humans and 247

strongly suggest the need to reassess current techniques for modelling dynamic CA. 248

249

Physiological considerations 250

Further studies are needed to confirm our findings and to establish if the directionality of 251

dynamic CA is age dependent. CBV and PP changes had a significant effect on explaining 252

the variance of the ARI differences between squatting and standing (ΔARISQ-ST). When the 253

borderline contribution of HR was included, there was a 73.6 % increase in R2 (from 0.254 to 254

0.441). However, variance estimates do not reflect the sign of the slopes (β coefficients) of 255

each co-variate. In Figs. 5 & 6, it is possible to estimate that, from the 7th to the 15th 256

maneuver, when most variables showed a more steady behaviour, changes in ARI, CBV, PP 257

and HR, from stand to squat, were approximately 2 (ARI units), 20 cm/s, -10 mmHg and -15 258

bpm, respectively. Based on the slopes of the CBV and PP given above, the CBV effects 259

would correspond to attenuation of the ARI change of approximately -0.5 (ARI units), with a 260

corresponding increase of 0.35 (ARI units) for the PP contribution, and – 0.23 (ARI units) 261

due to HR, if that is also included. Therefore, neither of these effects would explain the 262

overall changes of the order of 2 ARI units, and, moreover, they show opposite influences 263

that tend to cancel each other. Therefore, despite their significant influence on ΔARISQ-ST, 264

differences, CBV and PP, and HR, if also considered, have a minor effect in explaining the 265

more steady changes in ARI from stand to squat observed from the 7th to the 15th transient. 266

Associations derived from the repeated-measures linear model do not provide reliable 267

evidence of causality, and it is possible that other underlying variables could be mediating 268

these statistical associations. Several reasons point towards sympathetic neural activity 269

(SNA) as a strong candidate for this role (9). 270


Previous studies that could not detect asymmetry of dynamic CA (16, 26), involved BP 271

changes that were much smaller than in our case (Table 1, Fig. 5) and other studies where 272

asymmetry was reported (8, 36). Although Aaslid et al. (2), concluded in favor of asymmetry 273

in CA, despite relatively smaller changes in BP, this was only found in severe head injury 274

patients, and not in their healthy volunteers. Directionality of CA in head injury patients was 275

also reported using the Mx index (31). In these two studies, it is possible that directionality 276

was caused by the underlying pathology, with different mechanisms compared to healthy 277

subjects, including exaggerated SNA. On the other hand, the lack of association between 278

ΔARISQ-ST and ΔBPSQ-ST suggests that the amplitude of the BP change might not be the 279

main determinant of directionality. Alternatively, it is possible to speculate that the rate of 280

change of BP, usually termed rate-sensitivity, could be the dominant factor responsible for 281

the asymmetry in dynamic CA. One key study in this case would be to perform SSM with a 282

modified protocol where the change from squat-to-stand, and vice-versa, would be 283

performed gradually, instead of the rapid change in posture traditionally used in SSM (5, 8, 284

12, 32, 34, 36). Two previous studies though suggest that directionality might not be the 285

result of a rapid rate of change in BP; as it was also observed following relatively slower 286

changes in BP induced by phenylephrine and nitroprusside in healthy subjects (36), and it 287

was again noted in a review of 40 previous studies of static CA which concluded that CA 288

was more efficient for increases (26 studies) than decreases (23 studies) in mean BP (24). 289

As suggested by these investigators, directional sensitivity could result from SNA activation 290

as described by Casaglia et al. (11), showing increases in SNA before surges in BP, but not 291

before drops in BP. In fact, it is possible to see a clear distinction in SNA temporal patterns 292

in their Fig. 3 (11), showing much faster rates of rise in SNA than the rate of fall in the return 293

to baseline. Although the influence of sympathetic activation could be manifested in heart 294

rate, the lack of association between ΔARISQ-ST and ΔHRSQ-ST (p=0.07) cannot be used to 295

dismiss the involvement of SNA as the root cause of directionality for two main reasons. 296

First, as indicated in Fig. 5, it is more likely that HR changes were dominated by the 297

baroreflex and, second, sympathetic cardiac activity cannot be accepted as representative of 298


arteriolar sympathetic control due to the well-known regional differences in SNA. Gradual 299

changes in SNA during the 15 distinct SSM could also help to explain the non-stationary 300

behavior of CBV and ARI in (Fig. 6), where differences between values at squat and stand 301

were not noticeable at the beginning, but became more established with the progression of 302

the repeated maneuvers. 303

The recent finding that systolic BP values during SSM reflect a more efficient dynamic CA 304

than corresponding diastolic values could also help to explain the directional sensitivity of the 305

ARI observed in our study (34). As suggested by the authors (34), greater efficiency of 306

dynamic CA might protect the brain from the larger values of systolic pressure that could 307

lead to hyperperfusion and haemorrhage. Considering that systolic BP values are much 308

larger during squatting compared to standing, this greater efficiency of the cerebral 309

circulation to counteract changes in systolic pressure could explain our main findings, 310

including the association of PP with the ΔARISQ-ST. 311

In summary, further research is needed to explain the physiological mechanism responsible 312

for the directional sensitivity of the CA dynamic response. One interesting possibility would 313

be to investigate if directionality is already present in the myogenic response of isolated 314

cerebral arteries. 315

316

Clinical implications 317

Previous applications of the repeated SSM in physiological and clinical studies have 318

extracted parameters from the entire recording (4, 5, 6, 12, 18, 32, 33, 34).The more detailed 319

information provided by this study suggests that more robust results might be obtained by 320

removing the first 2 or 3 maneuvers from the analysis (Fig. 6). Further work is needed to 321

determine if an even smaller number of maneuvers, say from #3 to #9 (Figs. 5 & 6) would 322

suffice to provide reliable data. 323


Translation of current knowledge of the pathophysiology of dynamic CA to clinical practice 324

has been hindered by concerns about the reliability of existing parameters and protocols 325

adopted in its assessment (9, 23, 25). The main motivation for adopting SSM for assessment 326

of dynamic CA is the improved quality of estimates, as indicated by values of coherence 327

approaching 1.0 and the excellent reproducibility achieved within several days apart (4, 32). 328

As discussed below, the presence of directionality in CBF control requires some re-329

evaluation of current methodology for assessment of dynamic CA, but it also provides 330

potential opportunities to improve the sensitivity and specificity of diagnostic/prognostic 331

techniques, as suggested by studies that identified asymmetry of CA in patients with head 332

injury, despite relatively low amplitude of BP changes (2, 31). Given the amplitude of the ARI 333

differences observed in volunteers (Fig. 6), it is tempting to speculate that having two 334

measures of CA in each patient, from a single SSM, might be more pathognomonic in 335

different clinical conditions. Clearly, much more work is needed to test these different 336

hypotheses. 337

338

Assessment of dynamic autoregulation 339

Current techniques for assessment of dynamic CA can be classified as directional or non-340

directional. The former is represented by methods such as thigh cuff deflation, that induces 341

only a sudden drop in BP and the latter, by the widely used approach based on spontaneous 342

fluctuations in BP, where transient increases and reductions in BP are both present and 343

cannot be easily separated. Even with protocols that can induce clearly identifiable increases 344

and reductions in BP, such as the SSM, and the related sit-to-stand approach, modelling has 345

usually been performed with transfer function analysis (TFA) that does not distinguish 346

positive from negative changes in BP (13). If the asymmetry of dynamic CA is confirmed by 347

further studies, it will be important to move towards modelling techniques that could reflect 348

the directional sensitivity we, and others, have observed. As demonstrated in this study, 349

SSM combined with ARMA modelling would be a strong possibility, but alternatives are also 350


needed to allow investigation of dynamic CA in patients that cannot perform the SSM, or in 351

physiological studies where the protocol would not allow SSMs simultaneously with other 352

physiological challenges. 353

Compared to previous studies where ARMA modelling was adopted to estimate dynamic CA 354

parameters (3, 19), our study also showed innovation in the use of segments of data only 12 355

s long, as compared to 60 s or more used for analysis of time-varying estimates of ARI (14, 356

27). Although these relatively short segments would be expected to produce unreliable 357

estimates, surprisingly, the NMSE of CBV step responses from 1200 transients led to 358

rejection of only 59 estimates of ARI, that had to be replaced by interpolated values (28). 359

Undoubtedly, these exceptional results were possible due to the large changes in BP caused 360

by SSM and the corresponding high signal-to-noise ratios of the BP and CBV transients 361

(Figs. 1 & 3). 362

Previous application of TFA to SSM data, yielded estimates of ARI that were significantly 363

lower during SSM as compared to baseline standing (4). In the present study though, it is not 364

possible to directly compare values obtained for the squat or standing phases of SSM with 365

the baseline period, given the different modelling approaches used in each case, to suit the 366

much longer data segments available during baseline and the much reduced amplitude of 367

BP spontaneous fluctuations at rest. For this reason, it might be more prudent if future 368

studies report on multiple estimates of ARI, or other indices of dynamic CA, such as the 369

phase difference between CBV and BP (13), to provide a more comprehensive picture of the 370

effects of physiological changes, or disease, on parameters at rest as well as during the 371

separate phases of squatting and standing. Similar considerations would apply to the use of 372

the sit-to-stand maneuver. 373

Limitations of the study 374

Changes in CBV can reflect corresponding changes in CBF if the diameter of the insonated 375

artery (MCA) remains constant. Changes in MCA diameter, due to either squatting or 376


standing, could distort values of CBV changes, but, it would be very unlikely that the change 377

in MCA diameter would present a temporal pattern that would nullify the differences in ARI 378

values observed between the squatting and standing phases of the maneuver. 379

Our study was not designed to address potential effects of sex on the directionality of 380

dynamic CA, but this important aspect of phenotype deserves further investigation. In our 381

population, only two subjects were females and their results were not dissimilar from those 382

of the majority of male subjects. 383

Finally, our conclusions were derived from CBV measurements in the MCA and cannot be 384

generalised to other large intra-cerebral arteries like the PCA or ACA. 385

386

Perspectives and Significance 387

Better efficiency of dynamic CA, following relatively large changes in BP resulting from the 388

squat maneuver, in comparison with drops in BP resulting from standing up, could be 389

partially explained by corresponding differences CBV and PP, but not by absolute BP levels 390

or other parameters, such as HR, or EtCO2, but were highly reproducible in repeated 391

measurements taken several weeks apart. The underlying physiological mechanisms 392

responsible for the directional sensitivity of dynamic CA need further investigation, including 393

the potential involvement of sympathetic nervous system activation during increases in BP. 394

Clinical applications of dynamic CA assessment might benefit from the additional information 395

that can be derived with new analytical methods that are able to discriminate between the 396

autoregulatory response to increases or reductions in BP. Further work is also needed to 397

investigate the influence of age, sex and pathology on the directional sensitivity of dynamic 398

CA. 399

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ACKNOWLEDGEMENTS 402

We would like to thank the volunteers for the time dedicated to the study. TGR is an NIHR 403

Senior Investigator. 404

405

GRANTS 406

None to declare 407

408

DISCLOSURES 409

No conflicts of interest, financial or otherwise, are declared by the author(s). 410

411

AUTHOR CONTRIBUTIONS 412

V.J.H., T.G.R. and R.B.P. designed and planned study; S.C.B., N.B. and V.J.H. set up 413

experiment and protocol; S.C.B. and N.B. performed data collection; R.B.P. wrote dedicated 414

software; S.C.B., R.B.P. and M.N. performed data analysis; R.B.P. drafted manuscript; N.B., 415

S.C.B., V.J.H., T.G.R, R.B.P. and M.N. revised and approved final version of the manuscript. 416

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Tables 554

555

556

Table 1. Population mean ± SD for systemic and cerebral hemodynamic parameters. 557

Parameter Visit 1 Visit 2

Baseline Squat Stand Baseline Squat Stand p-value

Visit p-value

SSM

Mean BP (mmHg) 96.5 ± 6.4 111.2 ± 18.9 89.7 ± 14.4 90.0 ± 17.2 109.0 ± 9.5 88.0 ± 4.7 0.71 <0.0001

Heart rate (bpm) 87.2 ± 7.2 87.4 ± 9.7 103.8 ± 11.7 93.7 ± 14.1 92.8 ± 10.6 107.9 ± 12.6 0.35 <0.0001

EtCO2 (mmHg) 36.4 ± 2.1 41.3 ± 3.7 40.9 ± 3.4 37.3 ± 2.6 41.5 ± 3.8 41.3 ± 3.3 0.35 <0.0001

PP (mmHg) 44.8 ± 6.8 65.2 ± 14.8 72.9 ± 15.4 32.0 ± 13.6 59.1 ± 12.5 68.5 ± 15.0 0.51 0.0087

CBV (cm.s-1

) 55.8 ±10.1 71.6 ± 14.3 53.2 ± 8.2 53.6 ± 8.0 67.4 ± 13.1 48.4 ± 7.2 0.15 <0.0001

558

BP, mean arterial blood pressure; EtCO2, end-tidal CO2; PP, pulse pressure; CBV, cerebral blood velocity. 559


Figure legends 560

561

Figure 1 – Representative arterial blood pressure (continuous line, mmHg) and cerebral 562

blood velocity (dashed line, cm.s-1) for 15 squat maneuvers performed by a 21 year-old 563

female subject. Baseline values at t=0 s were removed for both signals. 564

Figure 2 – Cerebral blood velocity step response (continuous line) estimated with ARMA 565

model for the arterial blood pressure and cerebral blood velocity transients in Fig. 1 and best 566

fit Tiecks model (dashed line) for 15 squat maneuvers. Responses are in cm.mmHg-1.s-1. 567

Corresponding values of ARI ranged from 4.9 (transient 1) to 7.5 (transient 3) with median 568

[IQR] of 6.1 [5.9-6.6]. 569

Figure 3 – Corresponding changes in arterial blood pressure (continuous line, mmHg) and 570

cerebral blood velocity (dashed line, cm.s-1) for 15 standing maneuvers performed for the 571

same 21 year-old female subject with data presented in Fig. 1. Baseline values at t=0 were 572

removed from both signals. 573

Figure 4 – Cerebral blood velocity (CBV) step response (continuous line) estimated with 574

ARMA model for the arterial blood pressure and CBV transients in Fig. 3 and best fit Tiecks 575

model (dashed line) for 15 standing maneuvers. Responses are in cm.mmHg-1.s-1. 576

Corresponding values of ARI ranged from 0 (transient 10) to 7.0 (transient 2). The 577

normalised mean square error for fitting Tiecks model to the CBV step response for transient 578

#10 was above the threshold of 0.30 (see Methods) and the corresponding value of ARI was 579

then replaced by the average ARI of transients #9 and #11. The median [IQR] for the 15 580

transients was 5.2 [3.1-6.0]. 581

Figure 5 – Population averages for each of the 15 transient responses during squat (circles, 582

continuous line) and standing (squares, dashed line) maneuvers performed at 0.05 Hz. Error 583

bars represent ±1 SE.( A,B) Mean arterial blood pressure; (C,D) Heart rate; (E,F) End-tidal 584

CO2; (G,H) Pulse pressure. Visit 1 (A,C,E,G); visit 2 (B,D,F,H). 585

Figure 6 – Population averages for each of the 15 transient responses during squat (circles, 586

continuous line) and standing (squares, dashed line) maneuvers performed at 0.05 Hz. Error 587

bars represent ±1 SE.( A,B) Cerebral blood velocity; (C,D) Autoregulation index (ARI). Visit 588

1 (A,C); visit 2 (B,D). 589

590

591

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Directional sensitivity of dynamic cerebral autoregulation ...

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