Elsevier

Biological Psychology

Volume 89, Issue 2, February 2012, Pages 349-364
Biological Psychology

Statistical strategies to quantify respiratory sinus arrhythmia: Are commonly used metrics equivalent?

https://doi.org/10.1016/j.biopsycho.2011.11.009Get rights and content

Abstract

Three frequently used RSA metrics are investigated to document violations of assumptions for parametric analyses, moderation by respiration, influences of nonstationarity, and sensitivity to vagal blockade. Although all metrics are highly correlated, new findings illustrate that the metrics are noticeably different on the above dimensions. Only one method conforms to the assumptions for parametric analyses, is not moderated by respiration, is not influenced by nonstationarity, and reliably generates stronger effect sizes. Moreover, this method is also the most sensitive to vagal blockade. Specific features of this method may provide insights into improving the statistical characteristics of other commonly used RSA metrics. These data provide the evidence to question, based on statistical grounds, published reports using particular metrics of RSA.

Highlights

► Commonly used metrics for quantifying RSA are highly correlated. ► Several RSA metrics violate distributional requirements for parametric analysis. ► The Porges–Bohrer metric (RSAP–B) is appropriate for parametric analyses. ► The relationship between commonly used metrics and RSAP–B is moderated by respiration and nonstationarity in heart period. ► RSAP–B is significantly more sensitive to vagal blockade than other common metrics.

Introduction

Respiratory sinus arrhythmia (RSA) is a frequently measured physiological metric studied in several basic science and clinical disciplines. Although the methods used to quantify RSA are not standardized and vary among laboratories, little effort has been directed at establishing appropriate criteria with which to contrast methods. The published “standards” for quantifying heart rate variability (HRV) in cardiology (Camm et al., 1996) and psychophysiology (Berntson et al., 1997) have not prioritized the methods used to quantify RSA on either statistical or neurophysiological bases. In the absence of critical criteria to evaluate RSA metrics, researchers have assumed, based on reports of high within-subject correlations between common RSA metrics that all methods are equivalent (e.g., Grossman et al., 1990, Goedhart et al., 2007). However, within subject correlation is a deficient methodology to establish statistical equivalence between measures, since the within-subject correlation between biased and unbiased metrics can approach unity and the distributional features of each metric will not influence the strength of the linear relationship (Altman and Bland, 1983, Bland and Altman, 1986).

Physiologists have known for a century that vagal cardioinhibitory fibers have a respiratory rhythm (Hering, 1910). Although the functional impact of this vagal rhythm produces RSA, there has been a continuous debate about the efficacy of using RSA as a dynamic index of cardiac vagal tone. Instead there has been a dependence on using a heart rate measure, often in response to blockade or surgical disruption of vagal efferent outflow to the heart, and not RSA as the criterion measure of the functional impact of vagal cardioinhibitory fibers on the heart (Katona and Jih, 1975, Fouad et al., 1984, Grossman and Kollai, 1993). During the past 30 years, research has expanded our understanding of the neural circuits that regulate cardioinhibitory vagal pathways. It is now accepted that, in humans and other mammals, the primary cardioinhibitory pathways originate in the nucleus ambiguous (Rentero et al., 2002). These vagal pathways are myelinated, have nicotinic preganglionic receptors, and have a respiratory rhythm (see Porges, 2007 for a review). Thus, based on current knowledge of neurophysiology, a strong argument can be made that RSA reflects the dynamic functional impact of the vagal fibers originating in the nucleus ambiguus.

Other vagal cardioinhibitory pathways originate in the dorsal motor nucleus of the vagus. These fibers are unmyelinated. Although the output of these fibers does not have a respiratory rhythm, they contribute to the bradycardia associated with hypoxia and baroreceptor reflexes. Less is known about the preganglionic receptors of the unmyelinated vagal efferents that originate from the dorsal motor nucleus of the vagus and influence heart rate. Since the influence of unmyelinated vagal fibers on heart rate is preserved following nicotinic blockade, it has been proposed that the preganglionic receptors for these pathways are muscarinic (Cheng and Powley, 2000). However, this explanation is still questioned and other mechanisms may be involved in the regulation of the preganglionic receptors of the unmyelinated vagus.

Many psychophysiologists have assumed that the quantitative metrics used by bench physiologists could be generalized and ported to human research. Unfortunately for psychophysiologists, who are applying these physiological measures in experimental paradigms, the physiologists neither evaluated whether statistical parameters of RSA metrics conformed to the assumptions necessary for parametric analyses nor compared the relative sensitivity and specificity of various metrics to vagal manipulation. In addition, although specific quantification technologies for RSA, such as paced breathing protocols, may provide insight into clinical pathologies for physicians (e.g., Low and Sletten, 1997) and into cardiopulmonary interactions for physiologists (e.g., Hirsch and Bishop, 1981), these technologies may be inappropriate when applied to dynamically moving and psychologically active humans.

The research described in this paper provides an important step in identifying criteria to evaluate the relative merits of methods used to quantify RSA. Analyses will be presented that contrast three common methods used to quantify RSA in psychophysiological research and will describe how the different RSA metrics conform to statistical assumptions, are moderated by respiration, distorted by trend, and are sensitive to vagal blockade. To investigate these differences, RSA metrics were quantified during baseline and during infusion to either saline or a cholinergic blockade. These data enable analyses to address nine specific research questions that may contribute to an understanding of the appropriateness of each RSA metric. First, are the measures correlated? Second, are the methods equivalent in test–retest reliability and can reliable estimates be generated over short time periods? Third, do distributions of the measures conform to the assumption of normality? Fourth, do the measures differ in their direct relation to respiratory parameters? Fifth, are the methods differentially influenced by violations of the stationarity assumption? Sixth, is there a difference in sensitivity and statistical power of the RSA measures to a peripheral blockade of vagal outflow to the heart? Seventh, as an additional index of vagal modulation, is the moderation by respiratory parameters of the relation between change in RSA and change in heart rate in response to a saline infusion metric dependent? Eighth, is the relationship among the three RSA metrics moderated by respiration or trend? Ninth, are all methods equivalent when appropriately detrended and the estimates of variance are logarithmically transformed? The answers to these questions will provide insight into the disparate findings in the literature and will identify specific quantitative strategies that may improve the psychometric features of the less robust metrics.

Section snippets

Subjects

Sixty-five male participants between the ages of 18 and 34 (M = 25.48, SD = 3.99) were recruited with flyers, Craigslist advertisements, literature distributed at the UIC Hospital, and the UIC psychology student subject pool. Participants self-identified as Caucasian (58.5%), African-American (21.5%), Asian (10.8%), or other (9.2%) and were excluded from the study if, in the preceding 24 h, they had used a tobacco product, consumed more than 3 alcoholic beverages, taken any non-prescription drugs,

Porges–Bohrer method (RSAP–B)

The Porges–Bohrer method assumes that heart period time series reflect the sum of several component time series. Each of these component time series may be mediated by different neural mechanisms and may have different statistical features. The Porges–Bohrer method applies an algorithm that selectively extracts RSA, even when the periodic process representing RSA is superimposed on a complex baseline that may include aperiodic and slow periodic processes. Since the method is designed to remove

Correlations among RSA metrics

As illustrated in Table 1, the three metrics are highly inter-correlated during the baseline condition in both laboratory settings and all metrics are significantly correlated with heart period.

Test–retest reliability of RSA metrics

The stability of the RSA metrics was evaluated by correlations between the two test sessions. The metrics were significantly correlated between sessions (RSAP–B = 0.617, P2T = 0.320, HF = 0.532). The test–retest correlation was significantly lower for P2T than for HF and RSAP–B.

The internal stability of the

Are commonly used RSA metrics equivalent?

Porges (2007) proposed that several assumptions regarding RSA are based on claims that have not been appropriately tested with sufficiently rigorous methods. Two of these assumptions are challenged in this paper: (1) highly intercorrelated RSA metrics are equivalent, and (2) RSA metrics need to be statistically adjusted for ventilatory parameters to accurately estimate cardiac vagal tone.

In earlier research Grossman et al. (1990) reported that the three frequently used RSA metrics described in

Special thanks

The authors would like to thank Prof. Linda J. Skitka for her valuable instruction and guidance in developing the multiple linear regression models used in this report.

Acknowledgements

The project described was supported, in part, by Award Number R01HD053570 from the Eunice Kennedy Shriver National Institute of Child Health & Human Development and by training grants T32 MH067631 and T32 MH18882 from the National Institute of Mental Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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