# Causal analyses to study autonomic regulation during acute head-out water immersion, head-down tilt and supine position

Christophe Corbier, Florian Chouchou, Frédéric Roche, Jean-claude

Barthélémy, Vincent Pichot

► To cite this version:

Christophe Corbier, Florian Chouchou, Frédéric Roche, Jean-claude Barthélémy, Vincent Pichot. Causal analyses to study autonomic regulation during acute head-out water immersion, head-down tilt and supine position. Experimental Physiology, 2020, 105 (8), pp.1216-1222. 10.1113/EP088640. hal-04806654

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# Causal analyses to study autonomic regulation during acute head-out water immersion, head-down tilt and supine position

Christophe Corbier<fnref n="1" />

Florian Chouchou<fnref n="2" />

Frédéric Roche<fnref n="3" />

Jean-Claude Barthélémy<fnref n="3" />

Vincent Pichot<fnref n="3" /> ID

**Correspondence**

Vincent Pichot, Service EFCR - Niveau 6, Hôpital Nord, F-42055 Saint-Etienne, France.
Email: vincent.pichot@univ-st-etienne.fr

Edited by: Shigehiko Ogoh

**Abstract**

Thermoneutral head-out water immersion (WI) and 6 deg head-down tilt (HDT) are used to simulate SCUBA diving, swimming and microgravity, because these models induce an increase in central blood volume. Standard methods to analyse autonomic regulation have demonstrated an increase in parasympathetic activity and baroreflex sensitivity during these experimental conditions. However, such methods are not adapted to quantify all closed-loop interactions involved in respiratory and cardiovascular regulation. To overcome this limitation, we used Granger causality analysis between R-R intervals (RR), systolic blood pressure (SBP) and respiration (RE) in eight young, healthy subjects, recorded during 30 min periods in the supine position, WI and HDT. For all experimental conditions, we found a bidirectional causal relationship between RE and RR and between RR and SBP, with a dominant direction from RR to SBP, and a unidirectional causality from RE to SBP. These causal relationships remained unchanged for the three experimental tests. Interestingly, there was a lower causal relationship from RR to RE during WI compared with HDT. This causal link from RR to RE could be modulated by peripheral resistances. These results highlight differences in cardiovascular regulation during WI and HDT and confirm that Granger causality might reveal physiological mechanisms not accessible with standard methods.

**KEYWORDS**

autonomic regulation, Granger causality analysis, water immersion

## 1 | INTRODUCTION

Thermoneutral water immersion (WI) and 6 deg head-down tilt (HDT) can be used to simulate cardiovascular and respiratory adaptations to SCUBA diving, swimming or microgravity (Chouchou et al., 2020; Chouchou, Pichot, Garet, Barthelemy, & Roche, 2009; Regnard, Heer, Drummer, & Norsk, 2001; Shattock & Tipton, 2012). Such manoeuvres result in a shift of blood volume into mediastinal vessels and the heart (Norsk, 1997), associated with a decrease in peripheral blood pressure and heart rate (Miwa, Sugiyama, Mano, Iwase, & Matsukawa, 1997;

Shiraishi, Schou, Gybel, Christensen, & Norsk, 2002). However, some mechanisms are different between the two situations, because the external hydrostatic pressure increases pleural pressure and decreases vascular capacitance through the skin and muscles during WI (Miki, Klocke, Hong, & Krasney, 1989), whereas the blood flow is driven from the lower limbs to the thorax without external constraint during HDT because the gravity is applied on a plane perpendicular to the body (Regnard et al., 2001).

In these simulations, the vascular changes involve several autonomic adaptations that can be evaluated by heart rate variability (HRV).

<footnote n="1">¹ Saint-Etienne Jean Monnet University, Roanne Technology University Institute, University of Lyon, LASPI (EA3059), Roanne F-42334, France</footnote>

<footnote n="2">² University of La Réunion, UFRSHE, IRISSE Laboratory (EA4075), Le Tampon F-97430, France</footnote>

<footnote n="3">³ Saint-Etienne Jean Monnet University, CHU de Saint-Etienne, Department of Clinical and Exercise Physiology, University of Lyon, SNA-EPIS (EA4607), Saint-Etienne F-42055, France</footnote>
(Anon, 1996) and baroreflex sensitivity (Hughson, Quintin, Annat, Yamamoto, & Gharib, 1993) analyses. Using such methods, previous studies have shown an increase in parasympathetic activity (Chouchou et al., 2009; Miwa, Sugiyama, Mano, Iwase, & Matsukawa, 1996; Mourot et al., 2008) and in stimulation of the cardiac baroreceptors during both HDT and WI (Chouchou et al., 2020; Fu, Sugiyama, Kamiya, & Mano, 2000; Iida et al., 1999; Pump, Videbaek, Gabrielsen, & Norsk, 1999; Shiraishi et al., 2002), whereas another study suggested a higher baroreflex sensitivity and parasympathetic activation during HDT compared with WI (Shiraishi et al., 2002). However, the physi- ological adaptations to WI and HDT cannot be described entirely with HRV and baroreflex sensitivity indices. Indeed, HRV analysis allows the evaluation of sympathetic and parasympathetic activities and autonomic balance, and baroreflex sensitivity analysis allows the quantification of the effect of blood pressure variations on heart rate, but these methods do not consider all closed-loop interactions between heart rate, blood pressure and respiration involved during cardiovascular regulation.

To gain a better understanding of the cardiovascular adaptations to WI and HDT, we proposed the use of the time-domain Granger causality (G-causality) approach (Granger, 1969) from R-R intervals (RR), systolic arterial blood pressure (SBP) and respiration (RE), measured during supine horizontal position (HOR), HDT and WI challenges. This mathematical method was introduced in the cardio- vascular domain in the last decade to study the causal relationship among physiological mechanisms involved during cardiovascular regulation, thus providing additional information to that arising from classical methods cited above (Faes, Nollo, & Porta, 2011, 2013; Porta et al., 2013, 2014, 2015).

As described in these pioneering studies, each RR-SBP, RR-RE and SBP-RE causal relationship is supported by specific physi- ological regulatory mechanisms. The causal interaction from RR to SBP is attributable to Starling's law and the arterial Windkessel effect, whereas the one from SBP to RR is caused by baroreflex regulation. The causal link from RE to RR is attributable to direct coupling between respiratory centres and vagal efferent activities, in addition to the activation of cardiopulmonary reflexes and direct stimulation of the sinus node tissue. The causal interaction from RR to RE results from the variation in vagal firing preceding modifications of RE by central respiratory drive, and several authors have suggested a role of peri- pheral resistance in this regulation (Porta et al., 2013). Finally, RE affects SBP through mechanical effects on intrathoracic pressure and stroke volume and inhibition of central vagal outflow by respiratory neuron firing, whereas SBP does not have any influence on RE (Porta et al., 2012).

Accordingly, the main differences between WI and HDT are the greater redistribution of blood volume in the pulmonary circulation (Regnard et al., 2001) and the stimulation of cardio- pulmonary baroreflex that might play a pivotal role in these autonomic adaptations. Given that peripheral resistance is under the control of the cardiopulmonary baroreflex, we can expect a greater decrease in peripheral resistance during WI than during HDT (Salman, 2016). For the present study, we hypothesized that G-causality analysis could

New Findings

* What is the central question of this study?

Can Granger causality analysis of R-R inter- vals, systolic blood pressure and respiration provide evidence for the different physiological mechanisms induced during thermoneutral water immersion, 6 deg head-down tilt and supine position tests that are not accessible using traditional heart rate variability and baroreflex methods?

* What is the main finding and its importance?

The Granger analysis demonstrated a significant difference in the causal link from R-R intervals to respiration between water immersion and head- down tilt. The underlying physiological mechanism explaining this difference could be the variation in peripheral resistances.

show differences in the cardiovascular physiological mechanism of adaptation during WI and HDT, particularly owing to the potential impact of the peripheral resistances on the relationship between RR and RE (Porta et al., 2013).

2 | METHODS

The protocol has been described in detail in a previously published article, to which readers can refer (Chouchou et al., 2020).

2.1 | Ethical approval

This study was approved by the Regional Ethic Committee (approval no. 2007-21, CCP Loire, France), was conducted in accordance with the Declaration of Helsinki and was registered in the Clinical Trials database with the reference NCT00515645. In order to be included, each sub- ject signed a written informed consent.

2.2 | Subjects

The original study included 10 healthy male subjects, free of medication and cardiothoracic abnormality (Chouchou et al., 2020), but for the present analyses two subjects were excluded owing to poor ECG quality that did not enable the RE signal to be rebuilt using an ECG-derived respiration method. Finally, eight healthy men (mean ± SD: age, 26.2 ± 5.1 years; height, 180.2 ± 7.1 cm; and weight, 75.9 ± 7.9 kg) were included in the present study.
## 2.3 | Experimental protocol

The protocol consisted of three randomized experimental sessions performed separately on 3 days, separated by ≥1 week, in a quiet room with comfortable temperature (20–24°C). Initially, the subject was placed in the sitting position for 15 min, considered as the baseline, and then the subject underwent HDT, WI or HOR for 30 min, which was followed by 15 min of seated recovery. A posture change to 6 deg HDT was performed by passively tilting a back support to 6 deg head down. For WI, subjects were immersed in a sitting position in a water level higher than the nipples (midchest) at a temperature of 34–35°C.

## 2.4 | Continuous ECG and blood pressure recordings

The ECG and blood pressure were recorded continuously during baseline, experimental tests and recovery using a six-lead ECG and a non-invasive left finger arterial blood pressure device (Portapres; TNO TPD Biomedical Instrumental, Amsterdam, The Netherlands). Data were saved using data-acquisition software on LabView (Austin, TX, USA) at a sampling frequency of 500 Hz.

The data were analysed using free software HRVanalysis v.1.2 (Pichot, Roche, Celle, Barthelemy, & Chouchou, 2016) and in-house toolboxes developed with Matlab 2016a (The MathWorks Inc., Natick, MA, USA). Initial automatized extraction of ECG data was subsequently checked by visual inspection, meaning that undetected isolated QRS, ectopic beats or artefacts were corrected manually. If such correction was not possible, a cubic spline interpolation was used to correct for isolated artefacts and ectopic beats. Maximal blood pressure values after the R-wave peaks were classified as SBP (Goreham, Kimmerly, & Ladouceur, 2017). The RE signal was derived from the ECG signal by analysing the distortion of the R-wave peak attributable to induced respiratory thoracic motion (Moody, Mark, Zoccola, & Mantero, 1985). Thus, successive RR, SBP and RE values were calculated on a beat-by-beat basis and resampled at 2 Hz using cubic spline interpolation for all continuous recordings for each of the experimental conditions.

Causality analyses were performed on 10 min epochs selected at the end of the 30 min periods for HOR, HDT and WI.

## 2.5 | Causal analyses

In 1969, Granger introduced the G-causality approach in terms of multivariate linear regression modelling (Granger, 1969). For example, according to G-causality, $X_2(t)$ causes $X_1(t)$ if the inclusion of past observations of $X_2(t)$ reduces the prediction error of $X_1(t)$ in a bivariate linear regression model of $X_1(t)$ and $X_2(t)$, in comparison to a model including only previous observations of $X_1(t)$.

In our context, G-causality focuses on a trivariate linear regression modelling (unrestricted model), such that $X_1(t) \equiv \text{RR}$, $X_2(t) \equiv \text{SBP}$ and $X_3(t) \equiv \text{RE}$. Let $X(t)$ be the signal vector such that $X(t) = [X_1(t) \ X_2(t) \ X_3(t)]^T$ and $X(t) = \dot{X}(t, \theta) + \varepsilon(t, \theta)$, where $\dot{X}(t, \theta)$ and

$\varepsilon(t, \theta)$ are the prediction model and prediction error signal vectors, respectively. The prediction model can be written as $\dot{X}(t, \theta) = \sum_{j=1}^{d} A_j X(t-j)$, where $A_j = (A_j^1 \ A_j^2 \ A_j^3)^T$ with $A_j^k = (a_j^k \ b_j^k \ c_j^k)$, $d$ the order of prediction model, and $\theta \in \mathbb{R}^{3d \times 3}$ the matrix of parameters $a_j^k$, $b_j^k$, $c_j^k$ to be estimated. Let $\hat{\theta}_N$ be the estimated parameter matrix from the estimation criterion and $\hat{\varepsilon}(\hat{\theta}_N) = [\varepsilon_1(\hat{\theta}_N) \ \varepsilon_2(\hat{\theta}_N) \ \varepsilon_3(\hat{\theta}_N)]^T$ the estimated prediction error vector. Best model order, $d$, is given from a minimum of quality criteria, such as the Akaike information criterion, Bayesian information criterion or Hannan-Quinn information criterion. Let $\Sigma(\Sigma_{ij})$ and $\varphi(\rho_{ij})$ [$\varphi'(\rho'_{ij})$ and $\varphi''(\rho''_{ij})$] be the variance/covariance matrices of the unrestricted and restricted models (omitting a different predictor variable). The G-causality from $X_2$ to $X_1$ conditioned on $X_3$ is $F_{2 \to 1|3} = \text{In} \frac{\rho_{11}}{\Sigma_{11}}$. The remainder is $F_{1 \to 2|3} = \text{In} \frac{\rho''_{11}}{\Sigma_{22}}$, $F_{2 \to 3|1} = \text{In} \frac{\rho''_{22}}{\Sigma_{33}}$, $F_{3 \to 2|1} = \text{In} \frac{\rho'_{11}}{\Sigma_{22}}$, $F_{1 \to 3|2} = \text{In} \frac{\rho''_{22}}{\Sigma_{33}}$ and $F_{3 \to 1|2} = \text{In} \frac{\rho'_{22}}{\Sigma_{11}}$. A dominant causality can be based on a direct comparison between G-causality values assessed over an opposite causal direction. The directionality index is defined as $D_{ij} = F_{ij} - F_{ji}$.

## 2.6 | Statistical analysis

The significance of directionality index was calculated using a Wilcoxon signed-rank test (Faes et al., 2011). A Kolmogorov–Smirnov test showed that none of the G-causality data was normally distributed. Thus, a non-parametric Friedman test with a Dunn–Sidak post hoc test for multiple comparisons was used to check whether causality indices were different during HOR, HDT and WI. A value of $P < 0.05$ was considered statistically significant. Data were analysed using Matlab Statistics and Machine learning Toolbox 10.2 and presented as the mean ± SD.

## 3 | RESULTS

Consistent with our previously published article (Chouchou et al., 2020), there were no differences in mean RR (HOR, 924.4 ± 132.4 ms; HDT, 943.5 ± 98.6 ms; and WI, 910.3 ± 139.8 ms), in SBP (HOR, 115.2 ± 21.5 mmHg; HDT, 112.2 ± 11.1 mmHg; and WI, 100.6 ± 16.9 mmHg) or in HRV indices, such as powers in high frequency (HOR, 4369 ± 1322 ms²; HDT, 4414 ± 1112 ms²; and WI, 5418 ± 1135 ms²), low frequency (HOR, 5930 ± 954 ms²; HDT, 7793 ± 1451 ms²; and WI, 9220 ± 1577 ms²), low- to high-frequency ratio (HOR, 2.2 ± 0.5 ms²; HDT, 2.0 ± 0.3 ms²; and WI, 2.0 ± 0.2 ms²) and arterial baroreflex sensitivity (HOR, 17.2 ± 2.4 ms mmHg⁻¹; HDT, 18.3 ± 1.9 ms mmHg⁻¹; and WI, 17.2 ± 5.1 ms mmHg⁻¹) for the three sets of experimental conditions.

The G-causality results are summarized in Figure 1 with bar graphs and a graphical representation, as introduced by Faes et al. (2011). On the whole, during all experimental conditions, there was a bidirectional causal relationship between RR and SBP, with a predominance of the link from RR to SBP, a bidirectional relationship between RR and RE without any predominance, and an almost unidirectional causal link
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** Granger causality interactions during horizontal supine position (HOR), 6 deg head-down tilt (HDT) and thermoneutral water immersion (WI) represented using bar graphs and graphics (mean ± SD). (a) Causality between R–R intervals (RR) and systolic blood pressure (SBP). (b) Causality between respiration (RE) and SBP. (c) Causality between RR and RE. (d) Graphical representation of the overall results of causality analysis in the three experimental conditions. The thicker the arrow line, the more important the causality link. The statistical significance between the two causal directions for a given condition is marked with asterisks. \*P < 0.05 and \*\*P < 0.01 when a predominant causal directionality exists between SBP and RR, SBP and RE, or RE and RR. †P < 0.05 between WI and HDT</figcaption></figure>
from RE to SBP. These causal relationships remained unchanged for the three different tests.

Interestingly, there was a significant difference in the RR to RE link (*P* < 0.05) between HDT and WI, with a causality index inferior for the WI conditions compared with the HDT conditions, while this index had an intermediate value for HOR.

4 | DISCUSSION

Our results showed a significant difference between HDT and WI for the causal interaction from RR to RE, while the other causal relationships between RR, SBP and RE remained similar for the three experimental conditions.

Previous studies have shown an increase in parasympathetic activity and baroreflex sensitivity underpinning cardiovascular adaptations to acute HDT and WI (Chouchou et al., 2020; Fu et al., 2000; Iida et al., 1999; Miwa et al., 1996; Mourot et al., 2008; Pump et al., 1999). These adaptations were found to be similar during these two experimental conditions, except for Shiraishi et al. (2002), who reported a greater activation of the parasympathetic system during HDT compared with WI. To overcome these discrepancies, Porta et al. (2015) applied predictive entropy and its decomposition to study oscillations in RR and the relationship from RE and SBP to RR during 10 min in the supine position and –25 deg HDT. They reported a decrease in predictive entropy of RR but no difference in the relationship from RE and SBP to RR between HDT and the supine position. Using a more uncomfortable HDT than in our study (–25 deg, compared with 6 deg in the present study), they concluded that there was no difference in respiratory and baroreflex controls on cardiac activity between the supine position and HDT. Moreover, these results suggested that the cardiac control during a –25 deg HDT was unrelated to the baroreflex control and cardiopulmonary reflex but seemed to be related to central commands. Here, we used G-causality analysis to study the interaction between RR, SBP and RE. The causal interaction from SBP to RR does not give information on the baroreflex sensitivity but quantifies the implication of the closed-loop reflex in the regulation of blood pressure (Bassani et al., 2013; Porta et al., 2013). In accordance with the study by Porta et al. (2013), this index remained similar during the three experimental conditions, thus confirming that there is no difference in the baroreflex contribution, especially between WI and HDT. Also, the unidirectional causality index from RE to SBP found in our study confirmed that RE can be considered as exogenous for blood pressure (Porta et al., 2012).

The only difference we found between HDT and WI was the G-causality from RR to RE, with an intermediate value for HOR. Central respiratory drive might contribute to changes in RR through variations in the vagal firing preceding modifications of RE (Larsen, Tzeng, Sin, & Galletly, 2010). During WI, the decrease in the causality from RR to RE could be attributable to some changes in respiratory afferent signals and the central respiratory drive itself owing to a decline in vital capacity (Pendergast, Moon, Krasney, Held, & Zamparo, 2015). However, in a study using autonomic blockade, Porta

et al. (2013) observed that the causal relationship from RR to RE remained significant during administration of atropine or propranolol. This observation led them to formulate the hypothesis that this causal relationship could be modulated by peripheral resistance. This hypothesis could be consistent with our results, because peripheral resistances are not affected in the same way during HDT and WI owing to the different actions on arteries of gravitational and hydrostatic pressure. Indeed, during WI, the hydrostatic pressure limits vascular and venous capacitance and thus increases peripheral resistance. On the contrary, HDT induces a decrease in peripheral resistance owing to gravitational pressure (Fu et al., 2000).

A limitation of the study is the utilization of ECG-derived respiration as a surrogate of respiratory signal. Although the method is frequently used and validated (Helfenbein, Firoozabadi, Chien, Carlson, & Babaeizadeh, 2014), such a signal remains an indirect measure of RE, which results in lower performance of the RE modelling compared with SBP and RR, which can impact the global calculation of RR-SBP-RE causality indices. The same problem has been pointed out in previous studies (Porta et al., 2012, 2013). In the study by Porta et al. (2013), the results were consistent with studies based on respiratory measurements that were based on belt inductance plethysmography (Perrott & Cohen, 1996; Yana, Saul, Berger, Perrott, & Cohen, 1993), suggesting the reliability of this method in the present study and in others. However, a protocol studying the adequacy of the respiratory method, by comparing the model performance for ECG-derived respiration, thorax and abdomen belts, pneumotachograph and nasal cannula is necessary to improve the usefulness of G-causality in the cardiovascular domain. Additionally, some studies that will take into account other physiological mechanisms are still lacking for the global interpretation of RR-SBP-RE causal analyses.

In conclusion, the present study confirmed that the use of G-causality analyses is appropriate for cardiovascular signal analyses and provides complementary results to more traditional methods that are focused mainly on parasympathetic and sympathetic autonomic activities or baroreflex sensitivity. Here, the use of the G-causality method supported the hypothesis of different regulation between WI and HDT that cannot be demonstrated by the usual methods.

AUTHOR CONTRIBUTIONS

The experiments were performed in the Système Nerveux Autonome - Epidémiologie Physiologie Ingénierie Santé (SNA-EPIS) Laboratory in Saint-Etienne, France. F.C., F.R. and J.-C.B. were in charge of the conception and design of the work. F.C. and V.P. acquired the data. C.C., F.C. and V.P. analysed the data. All authors interpreted the data and drafted the work or revised it critically for important intellectual content. All authors approved the final version of the manuscript and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed.
DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available in the Appendix of this article.

ORCID

Vincent Pichot https://orcid.org/0000-0002-0832-4961

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How to cite this article: Corbier C, Chouchou F, Roche F, Barthélémy J-C, Pichot V. Causal analyses to study autonomic regulation during acute head-out water immersion, head-down tilt and supine position. *Experimental Physiology.* 2020;105:1216-1222. https://doi.org/10.1113/EP088640
# APPENDIX

## Granger causality data

<table><thead><tr><th>Subject</th><th>Experimental conditions</th><th>RR -&gt;SBP</th><th>SBP -&gt;RR</th><th>RR -&gt; SBP</th><th>RE -&gt; SBP</th><th>SBP -&gt; RE</th><th>RE -&gt; SBP</th><th>RE -&gt; RR</th><th>RR -&gt; RE</th><th>RE -&gt; RR</th></tr></thead><tbody><tr><td>Subject_1</td><td>HOR</td><td>0.34623</td><td>0.030409</td><td>0.04121</td><td>0.063724</td><td>0.043507</td><td>0.010084</td><td>0.045183</td><td>0.079763</td><td>0.017345</td></tr><tr><td>Subject_2</td><td>HOR</td><td>0.13655</td><td>0.014217</td><td>0.050128</td><td>0.12931</td><td>0.012084</td><td>0.0010624</td><td>0.075639</td><td>0.1537</td><td>0.0017513</td></tr><tr><td>Subject_3</td><td>HOR</td><td>0.2488</td><td>0.036324</td><td>0.25454</td><td>0.16738</td><td>0.0052462</td><td>0.034613</td><td>0.11867</td><td>0.021863</td><td>0.12187</td></tr><tr><td>Subject_4</td><td>HOR</td><td>0.21654</td><td>0.034913</td><td>0.060806</td><td>0.026253</td><td>0.0071993</td><td>0.00024247</td><td>0.064591</td><td>0.041483</td><td>0.034557</td></tr><tr><td>Subject_5</td><td>HOR</td><td>0.21849</td><td>0.10675</td><td>0.046172</td><td>0.1369</td><td>0.030395</td><td>0.022082</td><td>0.10756</td><td>0.057202</td><td>0.043467</td></tr><tr><td>Subject_6</td><td>HOR</td><td>0.23345</td><td>0.050115</td><td>0.0054025</td><td>0.34229</td><td>0.024098</td><td>0.013037</td><td>0.11414</td><td>0.036342</td><td>0.01294</td></tr><tr><td>Subject_7</td><td>HOR</td><td>0.34837</td><td>0.070961</td><td>0.091364</td><td>0.038439</td><td>0.023001</td><td>0.013239</td><td>0.036456</td><td>0.023622</td><td>0.054614</td></tr><tr><td>Subject_8</td><td>HOR</td><td>0.087874</td><td>0.022292</td><td>0.084372</td><td>0.072304</td><td>0.0095291</td><td>0.0016026</td><td>0.12014</td><td>0.041405</td><td>0.0053592</td></tr><tr><td>Subject_1</td><td>HDT</td><td>0.26754</td><td>0.042667</td><td>0.021625</td><td>0.065866</td><td>0.0057574</td><td>0.0080131</td><td>0.023133</td><td>0.04745</td><td>0.20139</td></tr><tr><td>Subject_2</td><td>HDT</td><td>0.24874</td><td>0.014797</td><td>0.058443</td><td>0.24536</td><td>0.063036</td><td>0.073529</td><td>0.078088</td><td>0.22742</td><td>0.015887</td></tr><tr><td>Subject_3</td><td>HDT</td><td>0.27435</td><td>0.069322</td><td>0.056788</td><td>0.075057</td><td>0.026296</td><td>0.010134</td><td>0.083581</td><td>0.10484</td><td>0.01956</td></tr><tr><td>Subject_4</td><td>HDT</td><td>0.21976</td><td>0.041467</td><td>0.13814</td><td>0.10695</td><td>0.006929</td><td>0.0010765</td><td>0.037769</td><td>0.016552</td><td>0.039903</td></tr><tr><td>Subject_5</td><td>HDT</td><td>0.15858</td><td>0.026529</td><td>0.028751</td><td>0.0858</td><td>0.020018</td><td>0.00071098</td><td>0.14355</td><td>0.06741</td><td>1.07 x 10<sup>-6</sup></td></tr><tr><td>Subject_6</td><td>HDT</td><td>0.28697</td><td>0.019803</td><td>0.090514</td><td>0.12062</td><td>0.032601</td><td>0.058175</td><td>0.074463</td><td>0.036451</td><td>0.027813</td></tr><tr><td>Subject_7</td><td>HDT</td><td>0.17246</td><td>0.047922</td><td>0.17817</td><td>0.030657</td><td>0.025396</td><td>0.079234</td><td>0.074919</td><td>0.029367</td><td>0.12362</td></tr><tr><td>Subject_8</td><td>HDT</td><td>0.14831</td><td>0.0064463</td><td>0.044578</td><td>0.042244</td><td>0.0064688</td><td>0.0007587</td><td>0.10648</td><td>0.068667</td><td>0.0083809</td></tr><tr><td>Subject_1</td><td>WI</td><td>0.3213</td><td>0.013141</td><td>0.028861</td><td>0.11032</td><td>0.030292</td><td>0.075864</td><td>0.076517</td><td>0.023225</td><td>0.064137</td></tr><tr><td>Subject_2</td><td>WI</td><td>0.16568</td><td>0.028638</td><td>0.14288</td><td>0.0043299</td><td>0.0051186</td><td>0.0030442</td><td>0.001299</td><td>0.026857</td><td>0.020112</td></tr><tr><td>Subject_3</td><td>WI</td><td>0.45383</td><td>0.10376</td><td>0.026293</td><td>0.086849</td><td>0.027771</td><td>0.0028419</td><td>0.032749</td><td>0.087987</td><td>0.0043243</td></tr><tr><td>Subject_4</td><td>WI</td><td>0.14532</td><td>0.020876</td><td>0.10615</td><td>0.060647</td><td>0.019387</td><td>0.08607</td><td>0.19219</td><td>0.027517</td><td>0.088294</td></tr><tr><td>Subject_5</td><td>WI</td><td>0.21019</td><td>0.10507</td><td>0.053042</td><td>0.13339</td><td>0.032674</td><td>0.0094808</td><td>0.068118</td><td>0.054516</td><td>0.0070079</td></tr><tr><td>Subject_6</td><td>WI</td><td>0.22517</td><td>0.0093845</td><td>0.10914</td><td>0.18688</td><td>0.012588</td><td>0.009506</td><td>0.06422</td><td>0.0096943</td><td>0.011773</td></tr><tr><td>Subject_7</td><td>WI</td><td>0.13675</td><td>0.013098</td><td>0.098986</td><td>0.12247</td><td>0.0074861</td><td>0.036836</td><td>0.032001</td><td>0.014204</td><td>0.046994</td></tr><tr><td>Subject_8</td><td>WI</td><td>0.012851</td><td>0.00038846</td><td>0.016303</td><td>0.0079423</td><td>0.0079026</td><td>0.0034748</td><td>0.066276</td><td>0.026189</td><td>0.1488</td></tr></tbody></table>

Abbreviations: HDT, 6 deg head-down tilt; HOR, horizontal supine position; RE, respiration; RR, R-R interval; SBP, systolic blood pressure; and WI, thermoneutral water immersion.