Respiratory Sinus Arrhythmia and Hierarchical Dimensions of Psychopathology

Respiratory Sinus Arrhythmia and Hierarchical Dimensions of Psychopathology | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 20 November 2025 V1 Latest version Share on Respiratory Sinus Arrhythmia and Hierarchical Dimensions of Psychopathology Authors : Elise Adams 0000-0001-8586-6193 [email protected] , Clare Beatty , and Brady D. Nelson 0000-0003-3214-8977 Authors Info & Affiliations https://doi.org/10.22541/au.176362227.70765825/v1 209 views 147 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Lower resting respiratory sinus arrhythmia (RSA), a psychophysiological index of parasympathetic nervous system functioning, has been linked to internalizing, externalizing, and thought disorders. Consistent with the Hierarchical Taxonomy of Psychopathology (HiTOP), these findings suggest lower RSA may be associated with general psychopathology (i.e., p-factor). In a sample of 215 18-36 year-olds oversampled for psychopathology, the present study utilized higher-order mediation modeling to examine associations between RSA and psychopathology at the p-factor and spectra levels, using both categorical (Structured Clinical Interview for DSM-5, SCID-5) and dimensional (Comprehensive Assessment of Traits relevant to Personality Disorders, CAT-PD) measures of psychopathology. Across both the SCID-5 and CAT-PD models, RSA was negatively associated with the p-factor. In the CAT-PD model, RSA was also negatively associated with the internalizing spectrum, independent of the p-factor. These findings suggest that lower parasympathetic nervous system functioning relates to general psychopathology, but it also demonstrates unique associations with internalizing psychopathology. Respiratory Sinus Arrhythmia and Hierarchical Dimensions of Psychopathology *Elise M. Adams, *Clare C. Beatty, & Brady D. Nelson *Co-First Authors Department of Psychology, Stony Brook University Author Information: Elise M. Adams ( [email protected] ; corresponding author) Clare C. Beatty ( [email protected] ) Brady D. Nelson ( [email protected] ) CRediT Author Contributions: Elise M. Adams served as lead for formal analysis, writing–original draft, and writing–review and editing. Clare C. Beatty served as lead for writing–original draft, and writing–review and editing. Brady D. Nelson served as lead for data curation and supervision and served in a supporting role for writing–original draft and writing–review and editing. Elise M. Adams, Clare C. Beatty and Brady D. Nelson contributed equally to conceptualization. Data and code availability statement: The study was not preregistered. All data and code used for analyses can be found at https://osf.io/dsqac. Funding statement: Funding for this study was provided by the College of Arts and Sciences at Stony Brook University. Conflict of interest disclosure: The authors have no conflicts of interest to declare. Ethics approval statement: The study procedures were approved by the Stony Brook University Institutional Review Board (Protocol 1106252). Abstract Lower resting respiratory sinus arrhythmia (RSA), a psychophysiological index of parasympathetic nervous system functioning, has been linked to internalizing, externalizing, and thought disorders. Consistent with the Hierarchical Taxonomy of Psychopathology (HiTOP), these findings suggest lower RSA may be associated with general psychopathology (i.e., p-factor). In a sample of 215 18-36 year-olds oversampled for psychopathology, the present study utilized higher-order mediation modeling to examine associations between RSA and psychopathology at the p-factor and spectra levels, using both categorical (Structured Clinical Interview for DSM-5, SCID-5) and dimensional (Comprehensive Assessment of Traits relevant to Personality Disorders, CAT-PD) measures of psychopathology. Across both the SCID-5 and CAT-PD models, RSA was negatively associated with the p-factor. In the CAT-PD model, RSA was also negatively associated with the internalizing spectrum, independent of the p-factor. These findings suggest that lower parasympathetic nervous system functioning relates to general psychopathology, but it also demonstrates unique associations with internalizing psychopathology. Keywords: respiratory sinus arrhythmia; psychopathology; Hierarchical Taxonomy of Psychopathology; p-factor; emotion regulation Respiratory Sinus Arrhythmia and Hierarchical Dimensions of Psychopathology Progress in identifying biological indicators of psychopathology has been modest, with associations often being small in magnitude (Marek et al., 2022) and difficult to replicate (Poldrack et al., 2017). These challenges are largely due to traditional diagnostic approaches that rely on categorical classifications, which fail to capture the dimensional nature of mental illness (Conway et al., 2022; Kotov et al., 2017, 2021, 2022; Latzman et al., 2020; Michelini et al., 2021; Perkins et al., 2020). The limitations of categorical diagnoses - including arbitrary thresholds, extensive comorbidity, and within-disorder heterogeneity - have complicated efforts to link biological processes to clinical phenomena (Krueger et al., 2018; Latzman et al., 2020). Moreover, growing evidence suggests that many biological mechanisms show similar patterns of association across multiple disorders (Zald & Lahey, 2017). This pattern underscores the need to examine how biological indicators of core regulatory functions span various forms of psychopathology. One such core function is emotion regulation, which is implicated across the spectrum of psychopathology. Difficulties with emotion regulation are strongly linked to diagnostic comorbidity, suggesting that these regulatory deficits may reflect broader dimensions of dysfunction rather than disorder-specific impairments (Aldao et al., 2016; Brenning et al., 2022; Fernandez et al., 2016; Sloan et al., 2017). These patterns support the hypothesis that emotion dysregulation may be a general liability factor conferring vulnerability to psychopathology, rather than being specific to any one disorder (Beauchaine & Zisner, 2017; Carver et al., 2017). Emotion regulation has traditionally been assessed using behavioral tasks and self-report measures. However, these approaches have notable limitations, including poor psychometric properties (Bardeen et al., 2012), limited ecological validity due to reliance on standardized stimuli lacking personal relevance (Speed et al., 2020), and discrepancies between subjective reports and physiological responses (Connelly & Denney, 2007). These limitations highlight the need for more objective measures of emotion regulatory capacity. Respiratory sinus arrhythmia (RSA) is one such psychophysiological measure, characterized by naturally occurring patterns of heart rate variation that fluctuate with respiration (Berntson et al., 1993). RSA provides a measure of parasympathetic nervous system activity through vagal influence on cardiac function (Porges, 2007; Thayer & Lane, 2009). RSA measurements can be obtained both at rest, reflecting baseline parasympathetic activity, and during various challenges, reflecting dynamic regulatory responses (Beauchaine, 2001; Thayer et al., 2012). This methodology allows researchers to examine individual differences in physiological flexibility and regulatory capacity (Beauchaine & Thayer, 2015). Two dominant theoretical models explain RSA’s relationship to emotion regulation. The Neurovisceral Integration Model posits that RSA reflects the effectiveness of prefrontal brain regions coordinating with autonomic functions to regulate physiological states (Thayer & Lane, 2000, 2009). According to this framework, higher RSA reflects more effective neural control over physiological responses. In contrast, the Polyvagal Theory frames RSA as a measure of vagal control over cardiac activity, likening the vagal influence to a ”vagal brake” that can be engaged or withdrawn to modulate physiological arousal (Porges, 1995, 2007). Both models share the view that RSA reflects regulatory capabilities (Beauchaine, 2015; Beauchaine & Thayer, 2015), with research supporting RSA’s relationship to emotion regulation effectiveness and stress response modulation (Thayer et al., 2012). Meta-analyses have consistently documented lower resting RSA across mood and anxiety disorders, with robust evidence linking lower RSA to both current symptoms and future illness trajectories (Chalmers et al., 2014; Koenig et al., 2016; Rottenberg et al., 2007; Wen et al., 2024). In depression, resting RSA serves as both a predictor of symptom development (Gentzler et al., 2012; Kovacs et al., 2016; Yaptangco et al., 2015) and a marker of treatment response (Chambers & Allen, 2002; Hartmann et al., 2018). Clinical studies have demonstrated lower resting RSA in individuals with depression (Kemp et al., 2010, 2012; Koch et al., 2019), as well as in individuals with suicidal ideation, behaviors, and past attempts (Crowell et al., 2005; Rottenberg et al., 2002; Tsypes et al., 2018). RSA also shows utility across anxiety disorders (Chalmers et al., 2014), including social anxiety (Kiel & Aaron, 2024; Sharma et al., 2011) generalized anxiety, and posttraumatic stress disorder (PTSD; Campbell et al., 2019). Together, these findings suggest that resting RSA is a general biological marker for internalizing psychopathology (Beauchaine, 2015; Beauchaine & Thayer, 2015). Low resting RSA has also been consistently associated with externalizing problems, such as conduct disorder (Beauchaine et al., 2007; Shader et al., 2018), attention problems (Rash & Aguirre-Camacho, 2012), callous-unemotional traits (de Wied et al., 2012), and psychopathy (Hansen et al., 2007). Children with lower resting RSA have been found to exhibit higher levels of externalizing problems, such as aggression, delinquency, and conduct issues, with boys showing stronger associations between RSA and externalizing behaviors than girls (Calkins & Dedmon, 2000; Hinnant & El-Sheikh, 2013). Finally, RSA has also been associated with thought disorders, such as schizophrenia, where lower RSA may serve as an endophenotype (Clamor et al., 2016). The cumulative evidence linking low RSA with internalizing, externalizing, and thought disorders suggests it may serve as a generalized biological marker of emotion dysregulation and vulnerability to psychopathology (Beauchaine, 2015; Beauchaine et al., 2019; Zhang et al., 2017). These spectra share deficits in emotion regulation, but manifest differently—externalizing behaviors involve difficulties in emotional control, such as impulsive aggression and disinhibition, while internalizing disorders are characterized by issues like excessive rumination and impaired attention (Eisenberg et al., 2001; Zhang et al., 2017). Similarly, the thought disorder spectra involves difficulties in emotion regulation manifested through blunted affect and impaired ability to amplify emotional responses, despite intact capacity for emotional experience (Clamor et al., 2016; Henry et al., 2007). These diverse manifestations across diagnostic categories all point to disrupted regulatory processes that could be measured via RSA. While low RSA is linked to specific disorders, the patterns across both domains are largely consistent and suggest the need for a broader, dimensional framework—one that focuses on shared mechanisms, such as autonomic dysregulation, rather than being confined to individual disorders. This broader perspective aligns with the Hierarchical Taxonomy of Psychopathology (HiTOP) model, which structures psychopathology along dimensional spectra, allowing for the measurement of shared vulnerabilities across a range of disorders (Kotov et al., 2017). At the highest level of HiTOP is the general factor of psychopathology, also referred to as the p-factor, which captures a general proneness to any form of psychopathology, transcending diagnostic boundaries (Caspi et al., 2014; Caspi & Moffitt, 2018). However, to date, no study has examined RSA in relation to broader versus narrower psychopathology dimensions that span both internalizing, externalizing, and thought disorder spectra. The present study examined relationships between RSA and hierarchical dimensions of psychopathology. A sample of 215 18-36-year-old young adults who were oversampled for psychopathology completed a resting heart rate recording to measure resting RSA. Participants’ lifetime psychiatric diagnoses were determined using the Structured Clinical Interview for DSM-5 (SCID-5). Participants also self-reported on their pathological personality traits using the Comprehensive Assessment of Traits Relevant to Personality Disorders (CAT-PD). We used structural equation modeling to examine the association between RSA and psychopathology at the general p-factor and spectra levels (i.e., internalizing, externalizing, thought disorder) for both categorical SCID-5 diagnoses and dimensional CAT-PD traits. We hypothesized that, across categorical and dimensional models, lower RSA would be associated with greater general psychopathology. Moreover, we hypothesized that specific spectra loading onto the general factor would not be independently associated with RSA once accounting for the broader general factor. Participants The sample included 215 18-36 year-olds ( M age =22.9, SD age =3.7) recruited from the community who were oversampled for psychopathology, such that participants were eligible only if they endorsed at least one cardinal symptom within the Structured Clinical Interview for DSM-5 (SCID-5) screening questions (First et al., 2015) and had a T -score of 65 or greater on the corresponding subscale from the Inventory of Depression and Anxiety Symptoms–Expanded Version (IDAS-II; Watson et al., 2012), Externalizing Spectrum Inventory–Brief Form (ESI-BF; Patrick et al., 2013), or Comprehensive Assessment of Traits relevant to Personality Disorder (CAT-PD; Simms, Goldberg, Roberts, et al., 2011). Participants were eligible if they spoke English and did not have any significant developmental or medical disabilities. All study procedures were approved by the Institutional Review Board at Stony Brook University and were carried out in accordance with the Declaration of Helsinki. For more details regarding the sampling procedures and recruitment strategy, see Trayvick (2024). From the overall study sample (N=225), participants were excluded if they did not complete the SCID-5 ( N =7), CAT-PD ( N =1), or RSA measurement ( N =2). Participants provided informed consent and received monetary compensation. Table 1 presents demographic information, including sex, gender, race, ethnicity, education level, country of origin, psychiatric medication use, and psychological treatment history. Socioeconomic status (SES) was determined using the Hollingshead Four-Factor SES index (Hollingshead, 1975). Measures Participants completed a diagnostic interview on the phone and self-report questionnaires and experimental paradigms in-person during a laboratory session. The Comprehensive Assessment of Traits Relevant to Personality Disorders (CAT-PD) The CAT-PD (Simms, Goldberg, & Roberts, 2011) is a measure of 33 pathological personality traits that was administered in its 216-item static form (CAT-PD-SF). Items were assessed using a 5-point response scale ranging from 1 ( very untrue of me ) to 5 ( very true of me ). Thirty-three trait scale scores were created by calculating the mean of each scales’ items. Consistent with a five-factor structure of personality traits (Wright & Simms, 2014), trait scale scores were summed together to create five broader domains: negative emotionality (α=.96), detachment (α=.83), psychoticism (α=.89), disconstraint (α=.87), and antagonism (α=.93). Structured Clinical Interview for DSM-5 (SCID-5) The SCID-5 (First et al., 2015) is a semi-structured diagnostic interview which assesses current and lifetime DSM-5 diagnoses. The SCID-5 was administered to each participant by a trained B.A/B.S. level interviewer who was supervised by a clinical psychologist (B.D.N.). The present study used lifetime SCID-5 diagnoses, including history of a major depressive episode (MDE) in the context of a unipolar or bipolar depressive disorder, persistent depressive disorder (PDD), generalized anxiety disorder (GAD), posttraumatic stress disorder (PTSD), panic disorder and/or agoraphobia (PDA), social anxiety disorder (SAD), obsessive-compulsive disorder (OCD), eating disorder (i.e., anorexia nervosa, bulimia nervosa, binge eating disorder, eating disorder not otherwise specified), alcohol use disorder (AUD), cannabis use disorder (CUD), non-cannabis drug use disorder (DUD), attention-deficit/hyperactivity disorder-inattentive type (ADHD-INT), ADHD-hyperactive/impulsive type (ADHD-HI), delusions (DEL), hallucinations (HAL), and hypomania/mania (MANIA). Interrater reliability estimates were calculated using a random selection of 20 interviews by having a second interviewer listen to the audio recording. Interrater reliability was in the acceptable range for all diagnoses (κ range: .65 for posttraumatic stress disorder to .93 for major depressive episode and attention-deficit/hyperactivity disorder). RSA Recording and Processing During the laboratory session, electrocardiogram (ECG) was recorded using the Biosemi ActiveTwo System (BioSemi, Amsterdam, Netherlands) during four consecutive, alternating (counterbalanced) 90-second rest periods with eyes open or closed, for a total of six minutes. Participants were seated in a sound-attenuated booth and instructed to relax and remain still during data collection. Two sintered Ag/AgCl electrodes were placed on the participant to record heart rate; one electrode was placed on the sternum and the other below the left clavicle. ECG data was collected using a DC-200 Hz bandpass filter and 1024 Hz sampling rate. Raw ECG data were processed using QRSTool (Allen et al., 2007) and all artifacts were identified and corrected manually. After correction, interbeat interval series were extracted for each recording block. Then, each block was entered into CardioEdit (Brain-Body Center, University of Illinois at Chicago; see (Lewis et al., 2012) for visual inspection and artifact correction. After processing, average RSA was calculated using CardioBatch. Data Analysis All analyses were conducted in version 4.3.3 of R Statistical Software (R Core Team, 2024). Specifically, models were constructed using version 0.6.17 of lavaan (Rosseel, 2012). All data and code used for analyses can be found at https://osf.io/dsqac. Measurement Models We used a confirmatory factor analysis (CFA) to establish whether the hierarchical structure of psychopathology explains the pattern of covariation among the observed psychopathology variables. As shown in Figure 1, two separate measurement models were built using dichotomous SCID-5 diagnoses or CAT-PD dimensions as indicators. Both measurement models consisted of first-order internalizing, externalizing, and thought disorder factors, and a second-order p-factor to account for correlations between first-order factors. For the SCID-5 diagnoses measurement model, the internalizing factor included MDE, PDD, GAD, PTSD, PDA, SAD, SP, OCD, and EAT; the externalizing factor included AUD, CUD, DUD, ADHD-INT, and ADHD-HI; and thought disorder factor included DEL, HAL, and MANIA. For the CAT-PD dimensions measurement model, the internalizing factor included negative emotionality and detachment, the externalizing factor included antagonism and disconstraint, and the thought disorder factor was represented by a single indicator, psychoticism. For this thought disorder factor, the measurement model was specified by fixing the factor loading to one. The SCID-5 diagnosis model was fitted using weighted least squares with mean and variance adjusted (WLSMV) to accommodate dichotomous indicators. The CAT-PD dimension model was fitted using robust maximum likelihood estimation to account for non-normality of psychopathology indicators, and indicators were z -scored before inclusion in the model. Higher-order Mediation Models As shown in Figure 2, to examine relationships between RSA and psychopathology across the hierarchy, SCID-5 and CAT-PD-derived factors were regressed on RSA in separate models. Total, direct, and indirect effects of RSA on psychopathology were estimated separately at the p-factor and spectrum levels, consistent with the approach outlined by (Conway et al., 2022). The same approach was used for SCID-5 diagnosis and CAT-PD dimension models. In the first model, direct effects of RSA on the p-factor were estimated by regressing the second-order p-factor on RSA. Because only the p-factor was regressed on RSA in this model, the direct effect was equal to the total effect of RSA on the p-factor, which represented the bivariate association between RSA and the p-factor. In the next three models, the direct effect of RSA on each spectrum (internalizing, externalizing, thought disorder) was estimated by regressing the first-order factors on RSA in separate models. The regression of the second-order p-factor on RSA was included in these models, which allowed for estimation of the indirect effect of RSA on each spectrum via the p-factor. Thus, the direct effect in each model could be interpreted as the unique association between RSA and each spectrum, above and beyond the effect of RSA mediated by the higher-order p-factor. The total effect of RSA on each spectrum was equal to the sum of the direct and indirect effects of RSA on each spectrum and represented the bivariate association between RSA and each spectrum. Consistent with the measurement model, the SCID-5 diagnosis model was fitted using weighted least squares with mean and variance adjusted (WLSMV) to accommodate dichotomous indicators. The CAT-PD dimension model was fitted using robust maximum likelihood estimation to account for non-normality of psychopathology indicators, and indicators were z -scored before inclusion in the model. Results Sample Characteristics Table 2 displays descriptive statistics and correlations among observed variables (RSA, SCID-5 diagnoses, CAT-PD dimensions). Measurement Models Figure 1 displays the final measurement models for SCID-5 diagnoses and CAT-PD dimensions, including factor loadings. SCID-5 Diagnoses Model fit indices suggested that the hypothesized SCID-5 diagnosis model fit the data adequately; χ 2 (116)=218.82, p< .001; comparative fit index (CFI)=.952; Tucker-Lewis index (TLI)=.944; root mean square error of approximation (RMSEA)=.064; standardized root mean square residual (SRMR)=.178. However, modification indices suggested inclusion of the covariance between ADHD-INT and ADHD-HI would improve model fit, so it was included in a second model. Indeed, inclusion of the covariance improved model fit; χ 2 (115)=176.21, p< .001; CFI=.972; TLI=.967; RMSEA=.050; SRMR=.166. In the second model, variance among SCID-5 diagnoses was well represented by the first-order factors (internalizing, externalizing, thought disorder) and second-order general psychopathology factor (p-factor), and was thus retained for subsequent higher-order mediation analyses. CAT-PD Dimensions Model fit indices suggested that the hypothesized CAT-PD model fit the data adequately; first-order factors (internalizing, externalizing, thought disorder) and second-order general psychopathology factor (p-factor) adequately represented shared variance among lower-order personality dimensions: χ 2 (3)=9.822, p =.020; CFI=.984; TLI=.947; RMSEA=.103; standardized root mean square residual (SRMR)=.021. Higher-Order Mediation Models Table 3 displays a summary of all standardized total, direct, and indirect effects of RSA on psychopathology. SCID-5 Diagnoses P-Factor. There was a negative effect of RSA on the p-factor ( β =-0.18, SE =0.08, p =0.017, 95% CI [-0.33, -0.03]), such that lower RSA was associated with greater general psychopathology. Spectrum. There was a negative total effect of RSA on the internalizing spectrum ( β =-0.18, SE =0.08, p =0.025, 95% CI [-0.34, -0.02]), such that lower RSA was associated with greater internalizing psychopathology. The total effect was equally explained by the non-significant, negative direct ( β =-0.09, SE =0.10, p =0.378, 95% CI [-0.29, 0.11]) and indirect effects ( β =-0.09, SE =0.07, p =0.193, 95% CI [-0.23, 0.05]) of RSA on the internalizing spectrum. RSA was not associated with the externalizing spectrum, as evidenced by non-significant total ( β =-0.17, SE =0.10, p =0.076, 95% CI [-0.36, 0.02]), direct ( β =-0.03, SE =0.11, p =0.787, 95% CI [-0.25, 0.19]), and indirect effects ( β =-0.14, SE =0.08, p =0.077, 95% CI [-0.29, 0.01]) of RSA on the externalizing factor. There was a negative indirect effect of RSA on the thought disorder spectrum via the higher-order p-factor ( β =-0.20, SE =0.09, p =0.019, 95% CI [-0.37, -0.03]). However, the total effect of RSA on the thought disorder spectrum was non-significant ( β =-0.04, SE =0.10, p =0.715, 95% CI [-0.24, 0.17]) due to the positive, albeit non-significant direct effect of RSA on the thought disorder spectrum ( β =0.16, SE =0.13, p =0.200, 95% CI [-0.09, 0.41]). CAT-PD Dimensions P-Factor. There was a positive effect of RSA on the p-factor ( β =-0.20, SE =0.07, p =0.008, 95% CI [-0.34, -0.05]), such that lower RSA was associated with greater general psychopathology. Spectrum. There was a negative total effect of RSA on the internalizing spectrum ( β =-0.25, SE =0.07, p <.001, 95% CI [-0.39, -0.11]), such that lower RSA was associated with greater internalizing psychopathology. The total effect was explained by the negative direct effect of RSA on internalizing ( β =-0.13, SE =0.05, p =0.014, 95% CI [-0.24, -0.03]) and the non-significant, negative indirect effect of RSA on internalizing ( β =-0.12, SE =0.07, p =0.072, 95% CI [-0.25, 0.01]). Specifically, 52% of the total effect was explained by the direct effect, and the remaining 48% was explained by the indirect effect. There was a negative indirect effect of RSA on the externalizing spectrum via the higher-order p-factor ( β =-0.20, SE =0.07, p =0.005, 95% CI [-0.33, -0.06]). However, the total effect of RSA on the externalizing spectrum was non-significant ( β =-0.13, SE =0.07, p =0.069, 95% CI [-0.27, 0.01]) due to the positive, albeit non-significant direct effect of RSA on externalizing ( β =0.07, SE =0.06, p =0.251, 95% CI [-0.05, 0.18]). Similarly, there was a negative indirect effect of RSA on the thought disorder spectrum via the higher-order p-factor ( β =-0.16, SE =0.05, p =0.003, 95% CI [-0.27, -0.06]). However, the total effect of RSA on the thought disorder spectrum was non-significant ( β =-0.09, SE =0.06, p =0.175, 95% CI [-0.21, 0.04]) due to the positive, albeit non-significant direct effect of RSA on externalizing ( β =0.08, SE =0.05, p =0.136, 95% CI [-0.02, 0.18]). Discussion The present study examined the relationship between RSA and hierarchical dimensions of psychopathology. In line with our hypothesis, across both categorical (SCID-5) and dimensional (CAT-PD) measures, lower resting RSA was associated with greater general psychopathology (p-factor). This relationship was reflected in indirect effects between RSA and both externalizing and thought disorder spectra, supporting the conceptualization of lower RSA as a shared feature of general psychopathology. These findings align with theoretical models positioning emotion dysregulation as a core transdiagnostic process, with RSA serving as an objective physiological marker of regulatory capacity (Beauchaine & Thayer, 2015; Thayer & Lane, 2009). Both the Neurovisceral Integration Model (Thayer & Lane, 2000, 2009) and Polyvagal Theory (Porges, 1995, 2007) frame RSA as reflecting the efficiency of prefrontal-autonomic connections that support flexible emotional and behavioral regulation, which are precisely the processes that appear compromised across multiple forms of psychopathology. Beyond its association with general psychopathology, resting RSA showed a unique negative relationship with the internalizing spectrum in the dimensional CAT-PD model, independent of indirect effects via the p-factor. Although this relationship was only significant in the CAT-PD model, the standardized estimates were comparable across both models. Approximately 50% of the relationship between RSA and internalizing was explained by indirect effects through general psychopathology, suggesting that while RSA reflects general regulatory deficits, there may be additional mechanisms specifically implicated in internalizing disorders. This is consistent with meta-analyses documenting lower RSA across mood and anxiety disorders (Chalmers et al., 2014; Cheng et al., 2022; Koenig et al., 2016), and with studies linking lower RSA to both current symptoms and future illness trajectories in depression and anxiety (Hinnant & El-Sheikh, 2009; Wetter & El-Sheikh, 2012; Yaroslavsky, Bylsma, et al., 2013; Yaroslavsky et al., 2014; Yaroslavsky, Rottenberg, et al., 2013). Internalizing disorders may involve distinct patterns of emotional dysregulation, such as excessive self-focused attention and rumination (Clark & Wells, 1995), that are uniquely captured by RSA beyond general regulatory deficits. Once the relationship between RSA and the p-factor was accounted for, RSA showed no significant direct relationship with either externalizing or thought disorder spectra. This finding might appear to contradict previous literature linking low RSA to externalizing disorders (Beauchaine, 2012; Beauchaine et al., 2007; Zhang et al., 2017) and thought disorders (Beauchaine et al., 2019; Clamor et al., 2016). However, our results suggest that previous associations between RSA and specific externalizing and thought disorders may be capturing shared variance with general psychopathology rather than variance unique to particular spectra. This discrepancy may reflect developmental differences in the relationship between RSA and psychopathology. This highlights the importance of examining hierarchical models of psychopathology when studying transdiagnostic factors, without accounting for the general factor, researchers may misattribute associations to specific spectra when they actually reflect general liability to psychopathology. The current study has several methodological strengths. First, we employed both interview and self-report assessment of psychopathology, providing convergent evidence for the relationship between RSA and the p-factor across measurement models. This multi-method approach increases confidence that our findings reflect true relationships rather than method artifacts. Second, our use of a hierarchical framework allowed us to parse general and specific associations between RSA and psychopathology, revealing relationships that would be obscured in traditional diagnostic approaches. While our findings support the utility of the p-factor in understanding the relationship between RSA and psychopathology, it is important to acknowledge significant debates in this area (Watts et al., 2020, 2023). In our study, we created two separate higher-order models: one using categorical SCID-5 diagnoses and another using dimensional CAT-PD personality traits. Both models reflected a hierarchical structure where the p-factor emerged as a second-order factor explaining correlations among first-order internalizing, externalizing, and thought disorder factors. The convergence of findings across both measurement approaches is noteworthy, as recent research has shown that general factors can vary considerably depending on which indicators are included and how they are measured (Watts et al., 2022). Alternative interpretations of general factors exist, including views that they may primarily reflect methodological artifacts (Bonifay et al., 2017; Bonifay & Cai, 2017), shared method variance from single-informant assessment (Watts et al., 2022) or simply severity of psychopathology rather than a distinct latent construct (Watts et al., 2023). Despite these interpretive challenges, our consistent findings across both categorical and dimensional models suggest that RSA’s relationship with the p-factor is robust beyond a single measurement approach. This multi-method consistency strengthens confidence that we are capturing a meaningful relationship between autonomic functioning and transdiagnostic vulnerability, though we acknowledge that the exact nature of what the p-factor represents remains an active area of debate (Watts et al., 2020). Our findings have important implications for clinical assessment and intervention. Rather than using RSA to identify risk for particular disorders, clinicians might consider it as an indicator of general regulatory capacity that cuts across diagnostic boundaries. The identification of RSA as a marker of the p-factor suggests that autonomic regulation training might be valuable across diverse clinical presentations. Interventions targeting autonomic regulation might prove effective regardless of presenting symptoms, particularly for individuals with multifaceted clinical presentations. This aligns with the transdiagnostic treatment movement, which emphasizes targeting shared mechanisms rather than disorder-specific symptoms. Previous research has shown that psychological treatments can increase resting RSA (Lipschutz et al., 2017; Mathewson et al., 2013), and that RSA changes during treatment correlate with symptom improvement (Brown et al., 2024; Shenk et al., 2022; Susman et al., 2024). Our findings suggest the importance of examining whether RSA improvements reflect changes in general psychopathology, specific symptom dimensions, or both. Several limitations of the current study should be acknowledged which may impact the generalizability of our findings. First, our sample was predominantly female (84.8%), which limits our ability to examine potential sex differences in RSA and its relationship to psychopathology. While we deliberately oversampled for psychopathology in the community to ensure adequate representation of psychological symptoms, this approach may limit generalizability to non-clinical or more severe clinical populations. Nevertheless, oversampling was necessary to properly examine hierarchical dimensions of psychopathology, and our sample’s diversity in psychopathology severity allows for meaningful dimensional analyses. Our sample was predominantly White (66.0%), with some racial and ethnic diversity (20.0% Hispanic/Latino), but this still represents a limitation in generalizability to more diverse populations. The education level of our sample was skewed toward partial college education (61.4%), which may not represent individuals with either lower or higher levels of educational attainment. Our cross-sectional design prevents causal inferences about the relationship between RSA and psychopathology. Longitudinal studies are needed to determine whether RSA abnormalities precede the development of psychopathology or emerge as a consequence of psychopathological processes. Also, our study did not examine certain forms of psychopathology included in HiTOP, such as somatoform or autism spectrum disorders. Future research should examine how RSA relates to these additional dimensions, providing a more comprehensive test of RSA’s relationship to the complete HiTOP model. Another important avenue for future research is examining how RSA reactivity, the dynamic change in RSA in response to environmental challenges, relates to HiTOP dimensions. While resting RSA shows relatively consistent associations with psychopathology, reactivity findings have been considerably more heterogeneous (Balzarotti et al., 2017; Shader et al., 2018). Emerging research suggests that combining resting RSA with RSA reactivity measures may provide a more comprehensive assessment of autonomic functioning than either measure alone (Yaroslavsky, Bylsma, et al., 2013; Yaroslavsky, Rottenberg, et al., 2013). Conclusions In conclusion, the present study provides evidence that RSA is primarily associated with a general factor of psychopathology, with additional specific associations with the internalizing spectrum. While previous research has established associations between RSA and specific disorders, our study is the first to demonstrate that RSA primarily relates to general psychopathology rather than specific spectra within a hierarchical framework. This suggests that RSA may be most valuable as a marker of broad vulnerability to psychopathology rather than as an indicator of risk for specific disorders. These findings underscore the value of examining biological markers within hierarchical models of psychopathology and support integrating psychophysiological measures like RSA into dimensional frameworks of mental health. Table 1 Demographics Sex Female 183 85.1 Male 32 14.9 Gender Female 178 82.8 Male 31 14.4 Other 6 2.8 Race Asian 18 8.4 Black 15 7.0 Latino 21 9.8 Other 19 8.8 White 142 66.0 Ethnicity Hispanic or Latino 43 20.0 Not Hispanic or Latino 172 80.0 Country of Origin United States 202 94.0 Other 13 6.0 Highest Education Level High school diploma 14 6.5 Partial college/specialized training 132 61.4 College degree 57 26.5 Graduate degree 12 5.6 Current Psychiatric Medication Yes 93 43.3 No 122 56.7 History of Psychological Treatment Yes 129 60.0 No 86 40.0 M ( SD ) Range Age 22.9 (3.7) 18-36 Hollingshead SES Index Score 28.5 (12.8) 17-66 Table 2 Descriptive Statistics and Correlations for Study Variables 1. RSA 6.6 (1.3) 2. NE 18.2 (4.6) -.59 3. DET 15.6 (3.2) -.51 .73 4. PSY 13.1 (3.6) -.31 .67 .35 5. ANT 11.6 (3.2) -.31 .65 .46 .74 6. DIS 15.9 (3.1) -.39 .72 .52 .67 .88 % 7. MDE 77.8 -.45 .04 .03 -.35 -.25 -.21 8. PDD 23.1 -.27 .02 .17 -.35 -.35 -.34 .57 9. GAD 28.2 -.21 .16 -.02 .02 -.14 .07 .26 -.04 10. OCD 7.9 -.24 -.01 -.06 -.37 -.51 -.31 .33 .16 .48 11. PTSD 38.4 -.17 -.08 -.34 -.05 -.33 -.28 .29 .01 .26 .34 12. PDA 9.7 -.23 .13 .00 .06 -.20 -.20 .22 -.03 .56 .60 .22 13. SAD 10.6 -.24 .14 .26 -.36 -.35 -.31 .49 .41 .10 .65 .01 .48 14. SP 13.9 -.53 .24 -.06 .32 .24 .16 .15 -.14 .05 .30 .31 .47 -.01 15. EAT 32.9 -.36 .09 -.13 .25 .25 .25 .17 -.39 .32 .01 .39 .40 -.21 .58 16. AUD 25.5 -.35 .35 .15 .31 .52 .54 .01 -.11 -.39 -.38 -.01 -.44 -.18 .18 .18 17. DUD 12.0 -.22 -.08 -.20 .13 .10 .13 .17 -.11 -.08 -.35 .11 -.17 -.33 .07 .31 .58 18. CUD 21.3 -.12 -.11 -.14 .05 .06 .10 .23 .01 -.06 -.38 -.07 -.20 -.27 -.11 .13 .47 .95 19. INT 23.1 -.09 -.05 -.25 .24 .12 .28 -.01 -.17 .40 -.24 .20 -.13 -.74 .17 .41 .10 .40 .35 20. HI 14.4 -.15 -.02 -.22 .34 .21 .35 -.15 -.20 .30 -.27 .11 -.15 -.78 .26 .39 .19 .43 .36 .97 21. DEL 14.3 -.14 .32 .01 .63 .39 .14 -.24 -.34 -.08 -.31 .23 .28 -.24 .45 .41 .11 .13 -.02 -.02 .06 22. HAL 6.5 .10 .08 -.13 .63 .26 .10 -.50 -.38 -.17 -.55 .22 -.12 -.66 .14 .25 .06 .23 .10 .35 .42 .74 23. MANIA 14.4 -.23 .31 -.08 .63 .63 .61 -.19 -.72 .04 -.30 .12 -.02 -.50 .48 .60 .55 .50 .35 .44 .50 .52 .46 Note. Significant ( p <.05) correlations are bolded . Abbreviations: RSA: Respiratory Sinus Arrhythmia; NE: Negative Emotionality; DET: Detachment; PSY: Psychoticism; ANT: Antagonism; DIS: Disconstraint; MDE: Major Depressive Episode; PDD: Persistent Depressive Disorder; GAD: Generalized Anxiety Disorder; OCD: Obsessive-Compulsive Disorder; PTSD: Post-Traumatic Stress Disorder; PDA: Panic Disorder and/or Agoraphobia; SAD: Social Anxiety Disorder; SP: Specific Phobia; EAT: Eating Disorder; AUD: Alcohol Use Disorder; DUD: Drug Use Disorder; CUD: Cannabis Use Disorder; INT: ADHD-Inattentive Type; HI: ADHD-Hyperactive/Impulsive Type; DEL: Delusions; HAL: Hallucinations; MANIA: Hypomania/Mania. Table 3 Total, Direct, and Indirect Effects of RSA on SCID-5 and CAT-PD Dimensions Estimate (SE) Estimate (SE) Estimate (SE) % accounted for by p-factor SCID-5 Diagnoses P-Factor - .18 (.08) Internalizing -.09 (.10) -.09 (.07) - .18 (.08) 50% Externalizing -.03 (.10) -.14 (.08) -.17 (.10) 82% Thought Disorder .16 (.13) -.20 (.09) -.04 (.10) 100% CAT-PD Dimensions P-Factor -.20 (.07) Internalizing - .13 (.05) -.12 (.07) -.25 (.07) 48% Externalizing .07 (.06) -.20 (.07) -.13 (.07) 100% Thought Disorder .08 (.05) -.16 (.05) -.09 (.06) 100% Figure 1 Measurement Models Note. This figure displays the measurement models of the general p-factor and internalizing, externalizing and thought disorder spectra based on either SCID-5 diagnoses (left) or CAT-PD dimensions (right). Rectangles represent measured variables and ovals represent unmeasured, latent variables. Single-headed arrows from indicators to their respective latent factor represent their standardized factor loadings. Double-headed arrows represent correlated residuals. Abbreviations: MDE: Major Depressive Episode; PDD: Persistent Depressive Disorder; GAD: Generalized Anxiety Disorder; OCD: Obsessive-Compulsive Disorder; PTSD: Post-Traumatic Stress Disorder; PDA: Panic Disorder and/or Agoraphobia; SAD: Social Anxiety Disorder; SP: Specific Phobia; EAT: Eating Disorder; AUD: Alcohol Use Disorder; DUD: Drug Use Disorder; CUD: Cannabis Use Disorder; ADHD-INT: ADHD-Inattentive Type; ADHD-HI: ADHD-Hyperactive/Impulsive Type; DEL: Delusions; HAL: Hallucinations; MANIA: Hypomania/Mania. Figure 2 Higher-Order Mediation Models of RSA on SCID-5 and CAT-PD Dimensions Note. Diagram of the regression of the general (p) and internalizing factors on RSA. The effect of RSA on the p-factor is represented by path a. The indirect effect of RSA on internalizing is represented by path a > b. The direct effect of RSA on internalizing is represented by path c. 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Authors Affiliations Elise Adams 0000-0001-8586-6193 [email protected] Stony Brook University Department of Psychology View all articles by this author Clare Beatty Stony Brook University Department of Psychology View all articles by this author Brady D. Nelson 0000-0003-3214-8977 Stony Brook University Department of Psychology View all articles by this author Metrics & Citations Metrics Article Usage 209 views 147 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Elise Adams, Clare Beatty, Brady D. Nelson. Respiratory Sinus Arrhythmia and Hierarchical Dimensions of Psychopathology. Authorea . 20 November 2025. DOI: https://doi.org/10.22541/au.176362227.70765825/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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