Psychophysiology, 51 (2014), 197–205. Wiley Periodicals, Inc. Printed in the USA. Copyright © 2013 Society for Psychophysiological Research DOI: 10.1111/psyp.12163

Low-frequency heart rate variability is related to the breath-to-breath variability in the respiratory pattern

ALESSANDRO BEDA,a DAVID M. SIMPSON,b NADJA C. CARVALHO,a and ALYSSON RONCALLY S. CARVALHOc a

Department of Electronic Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil Institute of Sound and Vibration Research, University of Southampton, Southampton, UK c Laboratory of Respiration Physiology, Carlos Chagas Filho Institute of Biophysics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil b

Abstract Changes in heart rate variability (HRV) at “respiratory” frequencies (0.15–0.5 Hz) may result from changes in respiration rather than autonomic control. We now investigate if the differences in HRV power in the low-frequency (LF) band (0.05–0.15 Hz, HRVLF) can also be predicted by respiration variability, quantified by the fraction of tidal volume power in the LF (VLF,n). Three experimental protocols were considered: paced breathing, mental effort tasks, and a repeated attentional task. Significant intra- and interindividual correlations were found between changes in HRVLF and VLF,n despite all subjects having a respiratory frequency above the LF band. Respiratory parameters (respiratory period, tidal volume, and VLF,n) could predict up to 79% of HRVLF differences in some cases. This suggests that respiratory variability is another mechanism of HRVLF generation, which should be always monitored, assessed, and considered in the interpretation of HRV changes. Descriptors: Heart rate variability, Respiration, Spectral indexes

psychophysiology. However, a consistent body of evidence showed that this paradigm might be inappropriate when the respiratory pattern deviates from the “ideal” condition of regular spontaneous breathing with a respiratory rate within the HF band and no variability in respiratory volume and rate (Eckberg, 1997; Pinna, Maestri, La Rovere, Gobbi, & Fanfulla, 2006). The respiratory modulation of HRV is frequency dependent, and the impact of respiration on HRV analysis is exacerbated when the respiration rate falls within the LF band, which is not uncommon (Beda, Jandre, Phillips, Giannella-Neto, & Simpson, 2007). In the latter case, the respiration-related sinus arrhythmia affects primarily HRVLF rather than HRVHF. One consequence, which has been repeatedly highlighted, is that differences in the respiratory pattern between subjects and tasks can result in differences in the spectral indices of HRV that might be unrelated to changing autonomic control (Beda et al., 2007; Cammann & Michel, 2002). For instance, it is possible that the same cognitive tasks performed with two different breathing patterns (e.g., answering a sequence of questions verbally rather than in written form) might produce discrepant results in terms of HRVLF and HRVHF that are mainly the effect of differences in breathing. Several authors attempted to control for such an effect by imposing controlled breathing on the subjects (Bernardi et al., 2000; Brown, Beightol, Koh, & Eckberg, 1993), but this impacts on the subjects’ ability to perform any other simultaneous mental or physical challenge, which is of paramount importance in several experimental scenarios. Other authors proposed a statistical approach to take into account the effect of respiratory pattern on the differences in HRVHF (or related indices) between tasks and/or

The beat-to-beat variability in heart period (heart rate variability, HRV), has been widely studied, and several indices quantifying HRV have been associated with changes in autonomic control of the heart. One of the conventional and still most common approaches in HRV analysis consists of estimating the power spectral density of the heart period time series (after resampling with constant intervals) and then considering the total power in different frequency bands as indices of autonomic control (Berntson et al., 1997; Camm et al., 1996). In particular, two bands are usually the object of investigation: the power of the high-frequency band (HF: 0.15–0.5 Hz, known also as respiratory frequencies) has been associated with vagal modulation of cardiac activity, while the lowfrequency band (LF: 0.05–0.15 Hz) is considered to reflect a strong (but not exclusive) sympathetic influence (Berntson et al., 1997; Camm et al., 1996; Eckberg & Karemaker, 2009; Parati, Mancia, Rienzo, & Castiglioni, 2006). The direct association of HRV in the HF band (HRVHF) with vagal modulation and of HRV in the LF band (HRVLF) with sympathetic modulation have been used as the interpretative paradigm in many studies performed in a broad range of physiological conditions, especially in the area of

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The simulation, paced breathing, and video game-based studies were performed by Elisson Andrade, Paulo C. N. Granja Filho, and Ana Lúcia N. Diniz, respectively. The experimental studies considered in this paper were partially funded by the CNPq (Brazil), CAPES (Brazil), MRC (UK), and Royal Society (UK). Address correspondence to: Alessandro Beda, Department of Electronic Engineering, Federal University of Minas Gerais, Engineering School, Block I, Room 2613, Avenida Antônio Carlos no. 6627, Pampulha CEP 31270-901, Belo Horizonte (MG), Brazil. E-mail: [email protected] 197

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subjects, by considering the average respiratory period (RP— calculated over the duration of the recording) and mean tidal volume (VT) as covariates or correcting factors in the analysis (Berntson et al., 1997; Grossman, Wilhelm, & Spoerle, 2004; Ritz & Dahme, 2006). Both these approaches suffer by neglecting the breath-to-breath variability in the respiratory pattern (e.g., in RP and VT). Such variability is expected to be present in spontaneous breathing and results in respiration with a broadband spectrum, characterized by significant power in the LF band, even if the average respiratory rate is within the HF band (Beda et al., 2007), as exemplified in Figure 1. Average RP and VT, by definition, are not capable of quantifying this effect. This implies that, hypothetically, two tasks can present the same VT and average respiratory frequency within the HF band, but considerably different levels of respiratory oscillations in the LF band due to breath-to-breath variability in respiration. These oscillations would directly affect HRVLF, without necessarily any changes in autonomic activation (Beda et al., 2007; Bernardi et al., 2000; Sloan, Korten, & Myers, 1991). On the other hand, evidence exists of a relationship between respiration and autonomic control (Wientjes, 1992). This suggests that respirationrelated changes in HRV might carry separate additional information about autonomic control. However, such changes in HRV become confounding when superimposed on those of different origin. From this point of view, while the role of respiration as a possible confounding factor for the assessment of HF cardiovascular oscillations has been extensively investigated and dis-

cussed (Grossman & Taylor, 2007; Ritz, 2009; Ritz & Dahme, 2006), so far this has not been systematically addressed for LF oscillations. The aim of the present study is to assess to what extent the respiratory pattern, and especially its variability, can predict heart rate oscillations in the LF band, even when the average respiratory frequency is in the HF band. The implications of a significant contribution of breath-to-breath variability in the respiratory pattern on HRVLF would be that (a) changes in HRVLF might be a reflection of changes in respiration rather than a direct effect of autonomic control; (b) controlled respiration might not be a sufficient condition to guarantee comparability between subjects and tasks, because in practice it is impossible to avoid small but possibly significant breath-by-breath changes in the respiratory waveform; and (c) parameters quantifying breath-to-breath variability in the respiratory pattern, and not only average RP and VT, should be considered in the statistical assessment of the HRV response. Materials and Method Numerical simulations were used to assess the impact of variability in respiratory patterns on the power spectrum of the respiratory volume signal in conditions where variability can be tightly controlled. Experimental data from healthy volunteers was then used to analyze the impact that the recorded respiratory variability has on HRV. Simulations

Rest

We simulated the respiratory volume waveform as a sequence of triangular waveforms, sampled at 50 Hz, whose shape was determined by three parameters (see an example in Figure 2): respiratory period (RP—average length of the waveform, in the range 3–10 s), VT (as the amplitude of the positive peak of the waveform, in the range 500–1,000 mL), and inspiration to expiration ratio (IEr—ratio of the length of the ascending and descending part of the waveform, in the range 0.5–1). We repeatedly simulated sequences of 200 respiratory cycles, each time using a different combination of RP, VT, and IEr. We then imposed a random breathby-breath variability on one of the three parameters, according to a Gaussian distribution with a coefficient of variation (i.e., standard deviation/mean) in the range 0–1 (negative values were discarded). For each simulation, the power of the volume signal in the HF and LF band (VHF and VLF) was estimated through the Welch’s modified periodogram method (Welch, 1967), using multiple segments corresponding to 128 s of data, a 10% overlap between adjacent segments, and a Hanning windowing. We then computed the difference in the fraction of power of the respiratory volume signal in the LF band (VLF,n) as a quantitative index of the relevance of LF oscillations in the overall breathing pattern:

Talking

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VLF ,n = VLF ( VLF + VHF ) 0.05 0.15 frequency (Hz)

0.5 0.05 0.15 frequency (Hz)

(1)

0.5

Figure 1. Example of the pattern and power spectral density (PSD) of the respiratory volume (V) signal and R-R interval, during regular and irregular breathing (quiet rest and talking, respectively). During irregular breathing, a considerable part of the power of V and R-R interval is located in the low-frequency band (LF), despite the fact that the average respiratory frequency (solid vertical line, computed as the total number of breaths divided by the duration of the task) is located in the high-frequency band (HF).

Experimental Protocol We analyzed data from three different studies involving healthy young adult volunteers, in order to consider a representative range of experimental scenarios: a paced breathing protocol (Lopes, Beda, Granja-Filho, Jandre, & Giannella-Neto, 2011), a mental effort protocol (Beda et al., 2007), and a video game-based attentional protocol. In the first study (Lopes et al., 2011), 18 subjects (18–35 years old, 9 females) performed, among other tasks, a

HRVLF is related to respiration variability a)

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Figure 2. Examples of the simulations of the respiratory volume signal with different levels of variability. a: no variability in the pattern; b, c, d: 30% variability in respiratory period (RP), tidal volume (VT), and inspiration to expiration ratio (IEr), respectively; e, f, g: 60% variability in RP, VT, and IEr, respectively. For all data shown, the average VT is 500 ml, the average RP is 5 s, and the average IEr is 0.5, which are compatible with the breathing patterns of young healthy adults. VLF,n = percentage of power in the low-frequency band resulting from the simulated signal.

sequence of paced breathing (PB) trials, using different combinations of respiratory frequency (RF (12 and 20 bpm) and IEr (1:1 and 1:2). The control of breathing was performed using interactive custom-built software, which displays the respiratory volume pattern of the target respiratory cycle and a cursor that follows in real time the actual respiratory volume signal of the volunteer (estimated through numerical integration of the respiratory flow). The volunteers were instructed to make the cursor follow the target pattern closely and also to pace their respiratory rhythm observing a bar on the side of the screen, which increased in size during inspiration and decreased during expiration. In this study, the target VT to be achieved by the subject was computed for each task to maintain the same alveolar ventilation obtained by the subject during spontaneous breathing at rest, as described elsewhere (Lopes et al., 2011). The repeated PB trials lasted 5 min and were separated by a recovery period of approximately 2 min. In the second study (Beda et al., 2007), 25 subjects (mean age [SD] 26.5 [4.0], 12 women) undertook the following protocol: baseline rest—relaxed and without talking; read—reading aloud a text of a neutral nature (part of the summary of a book); talk— talking about a topic of choice from a short list, such as daily routine or a favorite book; maths silent—performing a series of subtractions (repeatedly subtracting seven from three-digit random numbers) presented on sheets on which also the answers had to be written; maths aloud—same as maths silent, but having to read all the questions and answers aloud. The tasks had a duration of 5 min and were separated by rest periods of 5–7 min. In the third study, 20 volunteers (18–35 years old, 9 females) underwent an initial baseline rest period (duration 5 min), followed

Electrocardiogram (ECG) and respiratory signals were monitored continuously with a sampling frequency of 512 Hz for the paced breathing study and 1000 Hz for the others. Respiratory volume was estimated from pneumotacography (paced breathing protocol), airways pressure (mental effort protocol), or a plethysmographic belt (video game-based protocol), and then resampled at 50 Hz. The R-R interval time series was extracted from the ECG using an automated algorithm followed by manual editing (Beda et al., 2007) and interpolated at 4 Hz using cubic splines. Then, the power of this interpolated signal and of the respiratory volume signal were estimated in the LF (0.05–0.15Hz) and HF band (0.15–0.5 Hz) (HRVLF, HRVHF, VLF, and VHF, respectively) through Welch’s modified periodogram method (Welch, 1967), using multiple segments corresponding to 128 s of data, a 10% overlap between adjacent segments, and a Hanning windowing. The RP was estimated as the average interval between the onset of consecutive inspirations, detected using a zero-crossing algorithm applied to the derivative of the respiratory volume, signal followed by manual editing to remove erroneous detections, as in Beda et al. (2007). VT was estimated as the average amplitude of the respiratory volume signal for each breath, and VLF,n was computed according to Equation 1.

horizontal trajectory

vertical line

time (s)

50

vertical trajectory

0 0

by performing a sequence of 4 video game-based attentional tasks (duration 4 min) separated by recovery periods (duration 5 min). The content of the video game is illustrated in Figure 3. A circle oscillates on a vertical trajectory on the left edge of the screen for a few seconds. Then, it starts moving along a horizontal trajectory at a fixed velocity toward the right side of the screen. The subject has to click the mouse button when the circle crosses a vertical line located on the right side of the screen. Each 4-min trial involved 60 repetitions of this sequence (each lasting on average 4 s). In order to reduce predictability, the duration of the oscillation in the vertical trajectory and the speed of the circle in its horizontal trajectory changed randomly at each repetition (either 0.6, 0.9, or 1.2 screen lengths per second). All protocols were performed in a sitting position and with a randomized task sequence, and were approved by a local Ethical Committee.

Figure 3. Scheme of the video game-based attentional task. The circle oscillates on a vertical trajectory on the left edge of the screen for a few seconds. Then, it starts moving on a horizontal trajectory at fixed velocity toward the right side of the screen. The subject has to click the mouse button when the circle crosses a vertical line located on the right side of the screen. This trial is repeated several times with the duration of the oscillation in the vertical trajectory, and the speed of the circle on the horizontal trajectory changed randomly at each repetition (within a given range).

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Statistical Analysis

RP: variable VT: fixed a) IEr: fixed

Prior to the statistical analysis, a logarithmic transformation was applied to HRVLF, HRVHF, and VLF,n to achieve an approximately normal distribution of the data. Then, we considered the change of each parameter from baseline rest elicited by each task, identified by the prefix “Δ.” For each protocol, a multiple linear regression approach was applied to predict ΔHRVLF or ΔHRVHF for each task based on ΔRP, ΔVT, and ΔVLF,n, which are the descriptors of ventilation considered in this study, using the following equation: (2)

where α, β, and γ are the coefficients of the regression to be identified, X is either LF or HF, and δ is a residual error term. Then, the within-subject squared Pearson correlation coefficient of ΔHRVLF or ΔHRVHF with either ΔRP, ΔVT, ΔVLF,n, or their combination (i.e., squared multiple correlation coefficient, Cohen, 2003) was computed using the results from all the different tasks. To this end, the method introduced by Bland and Douglas (1995b) was employed, which accounts for the effect of repeated measures (i.e., tasks) for each subject and of other covariates, through a multiple linear regression approach. This analysis was performed to investigate to what extent, for a given subject, the changes in HRV indices between tasks can be explained by differences in ventilation. The sequential order of the tasks was used as covariate for all protocols, while target RP and IEr were evidently only used for the paced breathing protocol. The between-subjects squared correlation coefficients of ΔHRVLF or ΔHRVHF with either ΔRP, ΔVT, ΔVLF,n, or their combination were estimated using the method also introduced by Bland and Douglas (1995a), considering only the average value of each parameter across all the tasks performed by each subject during a protocol and gender as a covariate. The aim of this analysis was to elucidate if the differences between individuals in HRVLF or HRVHF can be predicted by differences in ventilation. In all statistical tests concerning the video game-based attentional protocol, the VT parameter was not considered because the respiratory signal acquired (plethysmographic belt) was not calibrated and hence cannot be compared between subjects. In the video game-based protocol, one subject maintained a respiratory rate within the LF band throughout the protocol and was for this reason discarded from the analysis as an outlier. Results were considered statistically significant when p < .05. Results Figure 2 shows an example of the simulated respiratory pattern using different configurations of the respiratory parameters. It shows that while, as expected, repeating a fixed waveform generates a pattern without power in the LF band (Figure 2a), increasing variability in RP, VT, or IEr quickly results in increasing levels of power in the LF band. Figure 4 shows the main simulation results regarding the effect of respiratory variability on VLF,n. As expected, the average value of RP strongly affects VLF,n: when RP increases above 6.67s (i.e., average respiratory rate enters the LF band), VLF,n rapidly increases above 80%, independently of the respiratory variability being present or not. However, when the respiratory period is below 6.67s (i.e., average respiratory rate is within the HF band), it is evident that the value of VLF,n is strongly dependent on the level of variability in RP, VT, or IEr. In this condition, the VLF,n induced by the variability in IEr is modest compared to that resulting from variable VT and RP. It is worth noting that, for a given value of VT

RP: fixed VT: fixed c) IEr: variable

100 90 80 70

VLF,n (%)

ΔHRVX = α ⋅ ΔRP + β ⋅ ΔVT + γ ⋅ ΔVLF ,n + δ

RP: fixed VT: variable b) IEr: fixed

60 50 40 30 20 10 0

3 4 5 6 7 8 9 10 3 4 5 6 7 8 9 10 3 4 5 6 7 8 9 10 average RP (s) average RP (s) average RP (s) variability: 0% 60% 20% 40% 80% 100% Figure 4. Percentage of power of the volume signal in the low-frequency band (VLF,n) in the simulated signals, at different levels of variability (coefficient of variation: 0-20-40-60-80-100%) of respiratory period (RP), tidal volume (VT), and inspiration to expiration ratio (IEr), and at different values of average RP (in the range 3–10 s). For all data shown, the average VT is 500 ml, and the average IEr is 0.5, which are compatible with the breathing patterns of young healthy adults.

variability, the resulting VLF,n was basically the same for all values of mean VT tested; the same was observed for IEr variability as well (results not shown). The overall response of respiration and HRV for the experimental data is shown in Figure 5 for the three protocols considered. All tasks elicited a significant respiratory change from baseline rest in terms of RP, VT, or VLF,n (except for paced breathing at 12 rpm), and a decrease in HRVLF and HRVHF, but not always significant. A non-negligible level of variability in the respiratory pattern was present in all protocols: VLF,n was on average around 5% of the total power for the paced breathing protocol, and up to more than 50% for the other protocols. Comparing the tasks within the same protocol, a significant overall task effect was detected for all respiratory parameters (with the exception of VLF,n for the video game-based protocol), while for the HRV parameters a significant task effect was detected only for the mental effort and the paced breathing protocols. The post hoc analysis showed that, in the mental effort protocol RP, VT, VLF,n, HRVLF, and HRVHF were lower during the math silent task than all the other tasks (p < .05). In the paced breathing protocol, while RP, VT, and HRVHF were lower when the respiratory frequency was 20 bpm compared to 12 bpm, there were no significant changes in VLF,n and HRVLF. During the video gamebased protocol, the first two repetitions of the video game resulted in a significantly lower RP than the other ones. Males showed generally larger values for VT, HRVLF, and HRVHF (the latter for the mental effort protocol only) and lower values for VLF,n. Figure 6 depicts the results of multiple linear regressions to predict the rest-task change in HRVLF and HRVHF (ΔHRVLF and

HRVLF is related to respiration variability

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HRV (ms2) LF task effect: p=0.000

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f) RF effect: p=0.159 IEr effect: p=0.308

R MS MA RE TA g) RF effect: p=0.000 IEr effect: p=0.160

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R MS MA RE TA h) RF effect: p=0.520 IEr effect: p=0.441

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task effect: p=0.001 ** ** * * * * †† †† ††† †††

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R T1 T2 T3 T4 T5

Figure 5. Overall response (median and 5–95% range) of respiration and heart rate variability indices for the three protocols considered. Mental effort protocol tasks: baseline rest (R), math silent (MS), math aloud (MA), reading (RE), talk aloud (TA). Paced breathing protocol tasks: baseline rest (R) paced breathing at 12 and 20 rpm with inspiration to expiration ratio of 1:1 or 1:2 (121:1, 121:2, 201:1, 201:2). Video game-based protocol tasks: baseline rest (R), repeated video game trials (T1–T5). HRVLF and HRVHF = power of heart rate variability in the low- and high-frequency band; VT = tidal volume; RP = respiratory period; VLF,n = percentage of power of the volume signal in the low-frequency band. The significance of the effect of the factors gender, task, respiratory frequency, and IEr was computed using a repeated measures ANOVA approach. P values of paired t tests versus baseline rest: †††p < .001; †† p < .01; †p < .05. A similar notation was adopted for paired t tests comparing with other tasks (*).

ΔHRVHF, respectively) based on the respiratory parameters considered. In panels (a) and (b), a small dispersion of the scatter plot from the identity line is evident, which reflects a considerable overall correlation between ΔHRVLF and respiration for the mental effort and paced breathing protocols. The quantitative results of the correlation analysis are reported in Table 1. A significant within-subject correlation of ΔVLF,n with ΔHRVLF was found for all protocols, but for ΔHRVHF this was found for the mental effort protocol only. Such correlation remained significant when the effect of RP and VT was removed from ΔHRVLF. The overall multiple within-subject correlation between ΔHRVLF or ΔHRVHF and respiratory parameters was in many cases considerable: in the mental effort protocol, 65% of the differences between tasks in the HRVLF response could be predicted from respiration (RP, VT, and ΔVLF,n together) and 39% for HRVHF. Also, 73% or more of the interindividual differences in ΔHRVLF could be predicted by respiratory parameters for the mental effort and paced breathing protocols, mainly due to a significant correlation of ΔHRVLF with ΔRP (and a residual correlation with ΔVLF,n for the mental effort protocol only). Gender did not affect significantly the correlations found, with the only notable exception of the between-subject correlation of ΔHRVHF with ΔVT, which was significant only when neglecting the effect of gender. Table 2 reports the results of the statistical test for significant differences between tasks in the HRV response for the mental effort protocol (using a general linear model approach). When the com-

parison is made without taking into account the respiratory parameters, the between-task differences in both HRVLF and HRVHF are highly significant (p < .001), and this continues to hold when only mean values of RP and VT are considered as covariates (for HRVHF, the p value increases to .044). However, when the changes in VLF,n are also accounted for as a covariate, the difference is far from significant (even when compensating for the reduction of 3 degrees of freedom resulting from introducing the three covariates in the model).

Discussion The main results found are: (a) both in the simulations and experimental protocols, it is evident that considerable respiratory oscillations in the LF band are present (of about 5% or more) even if the respiratory rate is within the HF band; surprisingly, even in paced breathing the phenomenon is not negligible; (b) VLF,n, which is an index of such oscillations, is an independent predictor of differences in HRVLF between tasks for a given individual, and between individuals; (c) in some of the experimental scenarios considered in the study, the combination of RP, VT, and VLF,n can predict a large part of the intra- and interindividual differences in HRVLF (up to 79%), but also in HRVHF (see Table 1); (d) statistically significant changes in HRVLF or HRVHF between tasks characterized by different respiratory patterns can cease to be significant if respiratory parameters are considered as covariates in the statistical analysis

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mental effort protocol

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-3 -2 -1 0 1 predicted value from respiration (α=0.13, β=1.24, γ=0.51, δ=-0.13)

-1.5 -1 -0.5 0 0.5 predicted value from respiration (α=0.71, β=0.00, γ=0.09, δ=-0.13)

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-1 -2 -1.2 -1 -0.8 -0.6 -0.4 -0.2 predicted value from respiration (α=-0.11, β=-0.31, γ=0.19, δ=-0.78)

T1 T2 T3 T4 T5

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121:1 121:2 201:1 201:2

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-2 -1 0 1 predicted value from respiration (α=0.26, β=-0.04, γ=0.36, δ=-0.10)

videogame-based protocol

1

-2 -2 -1.5 -1 -0.5 0 predicted value from respiration (α=0.86, β=-4.68, γ=-0.24, δ=-0.52)

-0.6 -0.4 -0.2 0 0.2 predicted value from respiration (α=0.31, β=0.00, γ=0.00, δ=-0.12)

Figure 6. Results of multiple linear regression to predict the change in heart variability indices (vertical axes) from respiratory parameters (horizontal axes). Each point refers to a subject during a specific task of the protocol. The identity line is shown in all figures. α, β, γ, δ = estimated coefficients of the multiple regression equation (see Equation 2). Δlog(HRVLF), Δlog(HRVHF) = rest-task change of the log-transformed value of heart rate variability power in the lowand high-frequency bands, respectively. Mental effort protocol tasks: baseline rest (R), math silent (MS), math aloud (MA), reading (RE), talk aloud (TA). Paced breathing protocol tasks: baseline rest (R) paced breathing at 12 and 20 rpm with inspiration to expiration ratio of 1:1 or 1:2 (121:1, 121:2, 201:1, 201:2). Video game-based protocol tasks: baseline rest (R), repeated video game trials (T1–T5).

(under the commonly adopted but possibly questionable paradigm that respiration acts as a confounding factor uncorrelated with the effect of interest). Our results confirm that respiratory breath-to-breath variability is likely to be present in experimental conditions that are usually

considered for the assessment of cardiovascular oscillations (Figure 5c, h, m). This is in agreement with previous studies regarding the natural variability of the respiratory pattern and the changes in respiration elicited by different tasks, especially those involving speech (Beda et al., 2007; Bendixen, Smith, & Mead,

Table 1. Within- and Between-Subject Correlation of HRV Indices with Respiratory Parameters ΔHRVLF Within-subject correlation r2 with ΔVLF,n r2 with ΔVT r2 with ΔRP r2 with ΔVT after removing ΔRP effect from ΔHRV r2 with ΔVLF,n after removing ΔRP, ΔVT effects from ΔHRV multiple r2 (ΔRP, ΔVT, ΔVLF,n effects combined)

ME .56*** .18*** .49*** .04 .12** .65***

PB .06* .00 .02 .00 .08* .10

ΔHRVHF VB

ME

PB

VB

.10** – .01 – .09** .10**

.26*** .26*** .34*** .01 .04 .39***

.02 .23*** .01 .23*** .01 .25**

.00 – .00 – .02 .02

ΔHRVHF

ΔHRVLF Between-subjects correlation r with ΔVLF,n r2 with ΔVT r2 with ΔRP r2 with ΔVT after removing ΔRP effect from ΔHRV r2 with ΔVLF,n after removing ΔRP, ΔVT effects from ΔHRV multiple r2 (ΔRP, ΔVT, ΔVLF,n effects combined) 2

ME

PB

VB

.59*** .33*** .59*** .01 .13* .73***

.57*** .37*** .74*** .02 .03 .79***

.08** – .25* – .01 .26

ME .05 .00 .01 .02 .07 .10

PB .01 .08 .00 .19 .05 .24

VB .01 – .08 – .01 .09

Note. Results are obtained either using Pearson’s correlation of the residuals after multiple linear regression (residual partial correlation), or multiple correlation (Cohen, 2003). r2 = squared correlation coefficient; ΔHRVLF, ΔHRVHF = rest-task change of heart rate variability in the low- and high-frequency band, respectively; ΔVT, ΔRP, ΔVLF,n = rest-task change in tidal volume, respiratory period, and fraction of power of the volume signal in the LF band, respectively; ME, PB, VB = mental effort, paced breathing, and video game-based protocols, respectively. ***p < .001. **p < .01. *p < .05.

HRVLF is related to respiration variability

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Table 2. Statistical Significance of the Effect of the Factor Task for the Mental Effort Protocol

Without correcting for respiration Using RP and VT as covariates Using RP, VT, and VLF,n as covariates Using RP, VT, and VLF,n as covariates, correcting for the reduced degrees of freedom

HRVLF p

HRVHF p

< .001 < .001 .194 .180

< .001 .044 .318 .302

Note. The results are obtained using a two-way repeated measures ANOVA (factors: task; order of execution). HRVLF, HRVHF = heart rate variability in the low- and high-frequency band, respectively; VT, RP, VLF,n = tidal volume, respiratory period, and fraction of power of the volume signal in the LF band, respectively.

1964; Bernardi et al., 2000; Hoit & Lohmeier, 2000; Reilly & Moore, 2003; Tobin, Mador, Guenther, Lodato, & Sackner, 1988). The magnitude of breath-to-breath variability varies considerably: it is small (VLF,n ≈ 5% of spectral power but consistently present) during paced breathing, larger during spontaneous breathing at rest, and very pronounced during tasks involving speech (VLF,n > 50% in some cases). Our main aim was to investigate to what extent these LF respiratory oscillations can predict heart rate oscillations in the same frequency band. The HRVLF index has been previously suggested as a marker of changes in phasic sympathetic modulation for a given individual (Malliani, Pagani, Lombardi, & Cerutti, 1991; Pagani et al., 1997), and is still commonly used, especially in psychophysiological investigations. However, several studies questioned this interpretation, considering the lack of correlation between change in HRVLF and sympathetic activation (Berntson et al., 1997), and the relevance of baroreflex modulation in the generation of LF cardiovascular oscillations (Malpas, 2002; Parati et al., 2006). While the role of respiratory modulation for the assessment of cardiovascular oscillations at higher frequencies has been extensively investigated and discussed (Grossman & Taylor, 2007; Ritz, 2009; Ritz & Dahme, 2006), this has not been the case for the less obvious impact of respiration on the LF band. In this context, it is worth noting that there are two distinct (but in many cases concomitant) situations in which respiration directly modulates HRVLF. The first occurs when the average respiratory rate falls below the HF threshold (0.15 Hz ≈ 6.7 bpm), which results in the respiration-related part of HRV spectrum to be partly or totally within the LF band. This is common during spontaneous breathing—≥ 20% of subjects breathe with a rate in the LF band, according to previous investigations (Beda et al., 2007; Hoit & Lohmeier, 2000; Pinna et al., 2006)—and breaks the assumption that respiratory sinus arrhythmia (RSA) is limited to the HF band, which is fundamental for the usual interpretative paradigm that HRVHF (RSA) reflects vagal modulation while HRVLF reflects sympathetic modulation (Brown et al., 1993). The second situation, possibly more subtle, is the presence of breath-to-breath variability in VT and RP, which generates LF respiratory oscillations, even if the respiratory frequency is well within the HF band. These oscillations, which cannot be assimilated into the usual conception of respiratory sinus arrhythmia, affect HRVLF through the known respiratory modulation mechanisms acting on heart rate (Berntson et al., 1997; Camm et al., 1996; Eckberg, 2003). In this study, we used VLF,n as an index of this respiratory variability and investigated its correlation with HRV indices, also

considering other common descriptors of respiration, such as RP and VT. By definition, VLF,n quantifies the relative distribution of respiratory power between LF and HF, providing a rough indication of how variable (how broadband) respiration is. The novel results found clearly indicate that VLF,n is consistently an independent predictor of within-individual changes in HRVLF (see Table 1). This correlation is considerable for tasks eliciting very different respiratory patterns (e.g., mental effort protocol), and less pronounced but still significant when performing controlled ventilation (e.g., paced breathing task), or when the same task is repeated (e.g., video game-based protocol). The differences between individuals in the VLF,n response were found to be independent predictors of HRVLF response only for the mental effort task. Combined together, RP, VT, and VLF,n are capable of predicting a large proportion (up to 79%) of the inter- and intraindividual difference in HRVLF response to the stimuli considered in the study. It is worth noting that in preliminary tests other indexes of respiratory variability (i.e., standard deviation of RP, VT, and IEr) were combined with VLF,n, but added only marginal improvements in prediction of HRV response (even if statistically significant in some cases, results not reported). The implications of these results are multiple. First, the possibility of respiratory modulation should be taken into account when analyzing the LF cardiovascular oscillation response. The common implicit or explicit assumption that respiration does not interfere with HRV indices (Ritz & Dahme, 2006), and with HRVLF in particular, is not safe, even under tightly controlled conditions such as paced breathing. Careful and systematic measurement and assessment of respiratory changes should be routinely used in any study dealing with cardiovascular oscillations, even if only LF components are of interest. Correlation analysis should always be performed to assess the possible effects of respiration on HRV indexes. Secondly, the concept of RSA may need to be reconsidered or more clearly defined in view of the results obtained. The slow oscillation in heart rate related to a single very long breath is naturally recognized as RSA. However, should we also label as RSA the slow oscillation in HR related to the change in RP and VT across consecutive breaths? This is not just a simple matter of nomenclature, but also raises the question whether HRVLF has the same physiological origin in both cases. In other words, do the slow respiratory variations within a single slow breath modulate heart rate through the same physiological control path/sequence as the slow respiratory oscillations occurring across several breaths? Further investigations are required to answer these questions. Furthermore, the results obtained challenge the current paradigm for the analysis of HRV using spectral indices, which is based on the assumption that (vagally mediated) respiratory modulation is restricted to the HF band, while LF modulation is the result of a complex interaction of sympathetic and parasympathetic modulation, not directly related to respiration (Berntson et al., 1997; Camm et al., 1996). This assumption does not appear to hold in a range of experimental scenarios considered in this study, since a significant correlation between respiration and HRV in the LF band can be detected. Ours and others’ investigations (Cammann & Michel, 2002) suggest that splitting the HRV spectrum into “rigid” frequency bands was indeed useful and appropriate for demonstrating basic properties of autonomic modulation of HRV, but might be insufficient when it comes to assessing more subtle changes in such modulation occurring in complex experimental conditions where respiration is difficult to control (e.g., psychophysiological tasks). It should be noted that the results found do not seem to be affected

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by our specific choice of 0.15 Hz as the edge between the LF and HF band: adopting a lower value of 0.12 Hz—as in some previous works (Galletly, Westenberg, Robinson, & Corfiatis, 1994)—also resulted in no negligible values for VLF,n in all conditions (results not shown). The literature consistently shows that RSA amplitude is directly related to RP and VT, but these relationships might be not linear and are influenced by changes in other physiological factors (e.g., arterial partial pressure CO2 , state of activity; Berntson & Stowell, 1998; Denver, Reed, & Porges, 2007; Ritz & Dahme, 2006; Tzeng, Larsen, & Galletly, 2007). These respiratory influences have been repeatedly shown when comparing the same subject under different conditions (i.e., within-subject correlations), but the results are controversial when comparing different individuals (i.e., betweensubject correlations; Denver et al., 2007; Grossman & Taylor, 2007). In agreement with the existing literature, our results indicate that a significant within-subject correlation exists between the HRVHF response and RP or VT (Brown et al., 1993; Grossman & Taylor, 2007; Poyhonen, Syvaoja, Hartikainen, Ruokonen, & Takala, 2004). However, such correlations are not consistent throughout all protocols considered. It is worth noting that gender should not be overlooked in this context. Males and females show different HRV and breathing patterns (Jensen-Urstad et al., 1997; Kuo et al., 1999), such that neglecting gender in correlation analysis could result in significant between-subject correlation between HRV indexes and respiration—as was indeed observed for ΔHRVHF with ΔVT in the present study. One might also speculate that our findings on the effect of slow respiratory oscillations related to breath-by-breath variability might also apply to arterial blood pressure variability indexes, further raising concerns about their interpretation in terms of autonomic control (Parati et al., 2006). In fact, the effect might be even more evident than for HRV, since the respiratory modulation of arterial pressure has a strong mechanical component: cyclic respiratory-related changes in volume and pressure in the thoracic cavity mechanically induce cyclic arterial pressure changes mediated by changes in stroke volume (as summarized in Fontecave Jallon, Abdulhay, Calabrese, Baconnier, & Gumery, 2009). However, further investigation of this issue is required. In conclusion, in agreement with the considerations of other authors (Grossman & Taylor, 2007; Ritz & Dahme, 2006), these results indicate that interpreting HRV response to different tasks as necessarily reflections of autonomic control uncorrelated to respiration can lead to misleading results, both for HF and LF indices. This possibility is exemplified in Table 2: in the mental effort protocol, the significance of the overall difference of HRV response between tasks is reduced when considering VT and RP as covariates, and disappears when VLF,n is included. The complex reciprocal interaction between HRV, respiration, and autonomic control is far from being completely understood. Previous studies suggested that respiratory changes in response to different stimuli are associated with autonomic response (Wientjes, 1992), so respiratory-mediated changes in HRV might carry relevant information about autonomic control, which should not necessarily be removed. On the other hand, respiration may change without it

directly implying altered autonomic function (e.g., voluntary respiratory control, speech, etc.), and the impact of this component may be confounding in the simplistic interpretation of HRV. Hence, considering changes in respiration simply as a confounding factor for the assessment of autonomic response might constitute a misleading oversimplification, but there is currently no interpretative paradigm that allows to disentangle all the possible pathways of respiratory modulation of HRV, and further investigations in this regard are needed. Until such a paradigm is available and validated, multiple linear regression approaches, with respiratory parameters as covariates, should not be applied uncritically to statistically control for the “effect” of respiration, as has already been highlighted (Allen, Chambers, & Towers, 2007). Possibly, the “change of perspective” suggested by Allen et al. (2007) is the most sensible approach to adopt: instead of trying to remove respiratory effects from HRV response using respiratory parameters as covariates, the approach should be to test if respiratory-related covariates can explain the changes in HRV. If so, any inference of autonomic change stands on weak ground, and should be avoided. If respiration is not found to explain the change in HRV, the inference of autonomic change seems more robust—though the lack of significance might also be related to an insufficient sample size, or to a nonlinear relationship between respiration and HRV, so the model does not adequately fit the physiological phenomenon, as suggested by Denver et al. (2007). Conclusions We found significant within-subject correlations between the changes in HRVLF and VLF,n for all protocols considered in this study, and a significant between-subject correlation between HRVLF and VLF,n for the mental effort protocol, which persist when correcting for the differences in respiratory period and tidal volume. When considering together VLF, respiratory period, and tidal volume, respiratory parameters could predict a large part of the intra- and interindividual differences in HRVLF (up to 79%), but also in HRVHF in some of the protocols considered. We emphasize that these correlations were found despite all subjects having a respiratory frequency above the LF band, including the case of the paced breathing protocol. It is thus possible that in many cases respiration changes can account for some of HRVLF variations without necessarily implying changes in autonomic control, and this should always be assessed (statistically or otherwise, including indices of respiration variability in the analysis). On the other hand, since changes in respiration can be related to autonomic response, respiratory-related changes in HRV indexes might carry additional information about autonomic modulation, which deserves further investigation. The evidence also suggests that paced breathing might not be sufficient to avoid the confounding effect of respiration on HRVLF, as previously hypothesized. Furthermore, the results strengthen the necessity of monitoring respiration during studies dealing with HRV, and highlight that respiratory variability should be added to the potential mechanisms of generating slow HRV oscillations.

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