The Role of Emotional Regulation in Anxiety and Depression Symptom Interplay and Expression among Adolescent Females

Depression and anxiety symptomology were measured using the Patient Health Questionnaire (PHQ-9; Kroenke et al., 2001) and the Generalised Anxiety Disorder Scale (GAD-7; Spitzer et al., 2006). Both scales are self-report measures of symptom severity in line with DSM-IV criteria and the more recent DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 4th edition; 5th edition). Items in both scales were scored on a 4-point likert scale, ranging from 0 to 4. The response categories were, not at all (0), several days (1), more than half the days (2) and nearly every day (3) and related to the past two weeks. Both the GAD-7 and PHQ-9 have demonstrated excellent internal reliability (Kroenke et al., 2001; Spitzer et al., 2006). In the context of the current study, each scale demonstrated excellent psychometric properties, with an internal consistency of .89 for the PHQ-9 and .91 for the GAD-7. Both scales have been utilised across both clinical and community based adolescent research, from age 11 to 18 years (Burdzovic Andreas & Brunborg, 2017; Mossman et al., 2017; Richardson et al., 2010).

The Difficulties in ER scale- short form (DERS-SF) was used to measure deficit’s in ER ability, across six domains (Kaufman et al., 2016). The DERS-SF contains 18 items rated on a 5-point likert scale ranging from almost never (0) to almost always (5), with higher scores indicating greater ER difficulties. The measure yields a total score as well as scores on six sub-scales which are ‘non-acceptance’, ‘difficulties with goal directed behaviour’, ‘impulse control’, ‘lack of emotional awareness’, ‘clarity’ and ‘limited access to ER strategies’. DERS-SF has demonstrated excellent psychometric properties within adolescent samples, with internal consistency for both the DERS-SF total scale and six subscales ranging from .78 to .91 (Kaufman et al., 2016). In the context of the current study, the internal consistency of each of the sub scales was high (average α for the DERS-SF subscales was .82). The DERS-SF has been demonstrated as appropriate for adolescents aged 11–17 years (Kaufman et al., 2016).

Networks were estimated, using the statistical program ‘R version 3.3.2’. Each network structure was estimated using a Graphical Gaussian Model (GGM). GGM was deemed appropriate due to the continuous nature of the data in the current study. Edges, in the context of GGM can be understood as partial associations, which represent the relationship between two nodes whilst controlling for all other relationships in the network (Epskamp et al., 2018). As GGMs typically estimate a large number of parameters, it is common practice to regularise GGM to avoid the possibility of false positive edges (Epskamp et al., 2018). The network structure in the current study was regularised by running the graphical LASSO (Least Absolute Shrinkage and Selection Operator; Friedman et al., 2008) via the R package qgraph (Epskamp et al., 2012). The graphical LASSO aims to reduce the edges within a given network by reducing the smallest edges within the network to zero (Beard et al., 2016; Epskamp et al., 2012), this creates a more parsimonious network. The Fruchterman and Reingold algorithm via the R-package qgraph was used to visually depict each of the networks estimated (Epskamp et al., 2012; Fruchterman & Reingold, 1991). This layout aids visual interpretation of the network structure by positioning the most strongly correlated nodes together and by placing the most central nodes towards the centre of the network (Epskamp et al., 2012). The qgraph package also aids how the nature of edges are visually interpreted, edges are coloured either red (negative relationship) or blue (positive relationship). Edge thickness depicts the strength of connection i.e. thicker lines represent stronger connections between nodes (Epskamp et al., 2012).

Centrality indicates the importance of each node within a given network. In the context of the current study expected influence (EI) was calculated (Borsboom & Cramer, 2013; Robinaugh et al., 2016). EI refers to a given nodes/symptoms influence with its neighboring symptoms/nodes (Robinaugh et al., 2016). This metric addresses issues around the more traditionally used centrality metric of ‘node strength’ as EI is calculated by summing the raw edge weights (+ and -), as opposed to node strength which calculates node centrality based on the absolute value of a given edge. Previous research has suggested that EI may be a more reliable indicator of centrality in the case where a given network has many negative edges (Robinaugh et al., 2016). In the present study, we estimated expected influence using the R package qgraph (Epskamp et al., 2012).

Clustering of symptoms within each of the estimated networks was explored using the ‘Walktrap’ community detection algorithm’ (Pons & Latapy, 2005), available via the Exploratory Graph Analysis (EGAnet) package (Golino & Epskamp, 2017). However as ‘Walktrap’ is likely to find clusters of nodes even within a random network structure, it was also necessary to calculate the modularity index Q (Newman & Girvan, 2004). Q is calculated to determine how well-defined a given clustering structure is within a network (McElroy & Patalay, 2019). Q values closer to 0.3 reflect weakly defined communities, and values approx. 0.7 reflect strong community structures (Newman & Girvan, 2004).

The R package ‘bootnet’ was used to investigate the accuracy and stability of each network estimated (Epskamp et al., 2018). Network stability estimation is a relatively new tool, it has not yet been refined to jointly estimated networks. Therefore, the stability of each network estimated in this study was investigated individually. Network stability was estimated in three ways; (1) bootstrapping 95% confidence intervals (CI) around edge weights, (2) estimating the correlation-stability co-efficient for centrality indices (values below 0.25 imply inadequate stability and values over 0.5 imply strong stability), (3) computing an edge-weights difference test for each network estimated (see Epskamp et al., 2018).

Latent profile analysis (LPA) is a mixture modelling technique used to identify homogenous groups/classes from continuous data (Weiss et al., 2018). LPA was used to identify whether different groups/classes of adolescents who varied in their ability to understand and regulate their emotions existed and what the nature of these classes/groups was.

A series of LPAs was estimated to identify the fewest latent emotional dysregulation profiles/classes. Seven models were specified and tested (a 2-class through to an 8-class solution) using MPLUS v7.3 (Muthén & Muthén, 2008). The models were estimated using robust maximum likelihood (Yuan & Bentler, 2000). To avoid solutions based on local maxima, 100 random sets of starting values were used. Several statistical model fit indices were used to identify the optimal number of latent classes. Specifically, by using three information theory-based fit statistics: the Akaike information criterion (AIC) (Akaike, 1987), the Bayesian information criterion (BIC) (Schwarz, 1978) and sample size-adjusted Bayesian information criterion (ssa-BIC; Sclove, 1987). The model that produced the lowest values was judged to be the best fitting model. The Lo-Mendell-Rubin (Lo et al., 2001) adjusted likelihood ratio test (LRT) was also used to compare models with increasing numbers of latent classes. When a non-significant value (p > 0.05) occurred, this indicated that the solution with one less class should be accepted.

The primary aim of this study was to use NA to examine the anxiety-depression symptom network structures of adolescents with varying degrees of ER ability. This was achieved by partitioning the data into three sub data sets based on class membership derived from LPA and estimating the networks for each. These three networks based on ER ability (high, intermediate and low) were compared using a ‘Network Comparison Test’ (NCT; van Borkulo et al., 2017). NCT allows for the comparison of specific edges across networks, and tests invariance in overall connectivity (i.e. global strength) and structure using non-parametric permutation tests (see van Borkulo et al., 2017).

Standardised expected influence centrality estimates for the overall network structure is presented in Fig. 2. Stability analyses indicated a stable order of expected influence with a CS coefficient of 0.6 (Fig. OS-4). The symptom with the highest EI was ‘control worry’ (2.04), followed by ‘hopelessness’ (1.10).

Finally, the presence of meaningful clusters of symptoms within the network was explored using the Walktrap community detection algorithm (see Fig. OS-1). Based on the Walktrap analysis, a community structure of three clusters was detected. However, the Q-index of modularity value was below acceptable (Q = 0.29), indicating that the clusters were most likely random in nature. Q values lower than 0.3 suggest random clustering (Newman & Girvan, 2004).

The fit indices for the LPA are displayed in Table 1. As aforementioned, a 2-class to an 8-class solution were specified and tested. The AIC, BIC and ssaBIC continued to decrease from a 2-class solution through to an 8-class solution. Despite this, the LRT value became non-significant at the 4-class solution. This suggested that the model with one fewer class should therefore be accepted; in this case this was the 3-class solution. Furthermore, the AIC, BIC and ssBIC values for the 3-class solution were lower than the corresponding values from the 2-class solution. Moreover, the AIC, BIC and ssBIC values for the subsequent class solutions (4-class to 8-class solution) indicated “flattening” i.e. that the subsequent decreases were smaller than those observed between the 2 and 3-class solutions (Weiss et al., 2018). The entropy value for the 3-class solution was 0.96, indicating acceptable classification of participants in this particular model. Average latent class probabilities for most likely class membership were 0.98 for class 1, 0.97 for class 2 and 0.94 for class 3, indicating good class discrimination. The 3-class solution was accepted as the most parsimonious.

The three profiles demonstrated varying degrees of severity in emotion dysregulation (see Fig. 3). Class 1 contained 15% (N = 92) of the sample and was characterised by having the greatest difficulties in regulating their emotions, overall demonstrating the highest scores across all six dimensions of the DERS-SF. This class was labelled the ‘low ER ability’ class. In comparison to the other two classes, class 1, when experiencing intense negative emotions, had the greatest difficulty engaging in goal directed behaviour, had limited access to strategies to help to regulate their emotions, were unable to accept their distress, had no clarity on which emotions they were experiencing and often acted on impulse. This group, although overall scoring the highest across all DER-SF scales, had the lowest score for the ‘lack of emotional awareness’ subscale compared with the other five subscales.

Class 2 contained 51.2% (N = 315) of the sample and was characterised by an above average ability to regulate their emotions well, compared to classes 1 and 3. Of the three classes, class 2, when experiencing intense negative emotions, were the group of adolescents most capable of engaging in goal directed behaviour, having good access to strategies to regulate their emotions, accepting that they were distressed, being clear on which emotions they were experiencing and not acting on impulse. This group demonstrated a greater awareness of their own emotions but had the lowest score on this subscale than the other five subscales. This class was labelled as the ‘high ER ability’ class.

Finally, class 3 represented 33.8% (N = 208) of the sample, demonstrating relatively low scores across all six DERS-SF subscales. This class demonstrated relatively intermediate scores across all six DERS-SF subscales. This class is characterised by an ability to adequately regulate their emotions, showing no greater than average difficulty across the six DERS-SF subscales. Class 3 was labelled as the ‘intermediate ER ability’ class. Overall, there was little variation in the probability estimates for the ‘lack of awareness’ subscale between classes, indicating that for this sample, lack of emotional awareness was not a prominent or distinguishing feature of emotional dysregulation.

Networks were estimated separately for both ER ability classes (low ER and intermediate ER ability; see supplementary S5–6). For ease of visual comparison, both ER ability networks were restricted to a consistent ‘average layout’ and are presented in Fig. 4. A description of the node labels and item descriptions can be seen in Table OS-1.

Firstly, in terms of the ‘low ER ability’ network, of a possible 120 edges (16*15/2), 58 (48.3%) were above zero. Generally positive edges were more commonly occurring and stronger than negative edges. The edge weights ranged between −0.14 to 0.49, with positive edges being more common occurring than negative edges. The strongest edges identified in were between ‘control worry’ and ‘worrying often’ (0.49), ‘sleep problems’ and ‘tiredness’ (0.37), ‘trouble relaxing’ and ‘restlessness’ (0.31), ‘sleep problems’ and ‘concentration’ (0.28), ‘hopelessness’ and ‘risk’ (0.26) and ‘nervous’ and ‘worrying often’ (0.24). Of the strongest edges identified, none linked depression and anxiety symptoms. Therefore, connections between symptoms within each construct were stronger than the connections between constructs. The edge weights bootstrap (Fig. OS-9) showed that the 95% confidence intervals for many of the edges were overlapping. Furthermore, there were few significant differences between the strongest edges; this therefore indicates that the ranking of edge weights should be interpreted with care (Fig. OS-10).

Looking towards the ‘intermediate ER ability’ network, of a possible 120 edges (16*15/2), 73 (60.83%) were non-zero. The strongest edges identified in the network were between ‘control worry’ and ‘worrying often’ (0.47), ‘nervous’ and ‘control worry’ (0.44), ‘psychomotor retardation’ and ‘restlessness’ (0.33), ‘worrying often’ and ‘feeling afraid’ (0.29), ‘trouble relaxing’ and ‘restlessness’ (0.27) and ‘sleep problems’ and ‘trouble relaxing’ (0.26). Of the strongest edges identified, two linked depression and anxiety symptoms, ‘psychomotor retardation’ and ‘restlessness’, and also, ‘sleep problems’ and ‘trouble relaxing’. Overall, connections between symptoms within each construct were stronger than the connections between constructs. The edge weights bootstrap (Fig. OS-12) showed that the 95% confidence intervals for many of the edges were overlapping. Furthermore, there were few significant differences between the strongest edges; this therefore indicates that the ranking of edge weights should be interpreted with care (Fig. OS-13). The most consistently strong edge across both estimated ER ability networks was between anxiety symptoms ‘control worrying’ and ‘worrying often’. This connection was stronger in the low ER network.

Standardised expected influence (EI) centrality estimates for both the low ER and intermediate ER network structures are presented in Figs. 5 and 6, respectively. Firstly, in the context of the intermediate ER network the symptoms with the highest EI centrality were, ‘psychomotor retardation/agitation’ (2.10), ‘worrying often’ (1.11), ‘trouble relaxing’ (0.99) and ‘control worrying’ (0.95). ‘Anhedonia’ and ‘risk’ had the lowest EI. In relation to the ‘low ER’ network, the symptom with the highest EI was ‘worrying often’ (1.60), followed by ‘restlessness’ (0.91) and ‘feeling nervous’ (0.82). The symptoms with the lowest expected influence in the ‘low ER’ network, were ‘psychomotor retardation’, followed by ‘appetite/weight loss’. Stability analyses revealed a CS coefficient of 0.44 for the intermediate ER network (Fig. 14-OS) and 0.21 for the low ER network (Fig. 11-OS). This indicates the EI centrality estimates should be interpreted with caution, particularly in relation to the low ER network.

The presence of meaningful clusters of symptoms within both ER ability networks was explored using the Walktrap community detection algorithm. This was important to explore as it was hypothesised that in the context of poor ER ability, symptoms of anxiety and depression may have reflected what is typically recognised in clinical practice and diagnoses as anxiety and depression i.e. grouped into meaningful within disorder clusters. Additionally as both ER ability groups contained unequal samples, a random selection of 92 observations from the intermediate groups was computed in R in order to create two equal samples to match the 92 observations within the ‘low ER ability’ group. The intermediate ER network was then re-estimated and the Walktrap community detection algorithm carried out again to explore meaningful clustering when all samples for each network were of equal size (see Fig. OS-8).

A community structure of three clusters of nodes was detected for the ‘intermediate ER ability’ network. Again, this was also the case for the ‘matched sample intermediate ER ability’ network. The Q-index of modularity value was below acceptable (Q = 0.26), indicating that the clusters were most likely random in nature. This was also the case for the ‘matched sample intermediate ER ability’ network, were the modularity value was 0.31. Additionally, a community structure of four clusters of nodes was detected for the ‘low ER ability’ network. However, the Q-index of modularity value was not acceptable (Q = 0.22), indicating that the clusters were most likely random in nature.

Network comparison permutation tests were used to empirically compare whether both` ER ability networks significantly differed from one another. The results of the NCT found no significant difference in global strength between the ‘low ER’ and the ‘intermediate ER’ ability network. As a robustness check a random selection of 92 observations from the intermediate group was computed in R to create an equal sample to match the 92 observations within the ‘low ER ability’ group. The intermediate ER network was then re-estimated and the NCT permutation tests were carried out again. In the case of the matched sample sizes the results of the NCT permutation tests remained the same.

The current study aimed to estimate and compare networks of depression and anxiety symptoms at varying levels of ER within a non-clinical adolescent sample. Given the well-established evidence base demonstrating the transdiagnostic quality of ER in adolescence, it was hypothesised that ER may be a plausible mechanism for explaining the context in which a network of anxiety and depression symptoms might begin to connect, perpetuate, and ultimately diverge. At the lowest levels of ER, it was hypothesised that this anxiety-depression network would be most reflective of what is recognised clinically as depression and anxiety, demonstrating greater overall global connectivity and clear evidence of within disorder clustering (i.e. separation of symptoms into their relevant disorder) in comparison to both the intermediate and high ER anxiety-depression networks. However, this path was met with methodological challenges, which meant that an anxiety-depression network for the ‘High ER’ group could not be reliably estimated, due to a non-positive definite matrix, likely the result of a lack of statistical power or variance. Terluin et al. (2016) highlight that issues surrounding variance are particularly prevalent when studying psychological networks within healthy populations or samples (see Terluin et al., 2016). Therefore, the dissemination of findings will focus on the anxiety-depression networks estimated for the ‘low ER’ and ‘intermediate ER’ ability groups only.

In the context of both the low ER network and intermediate ER network structures, the current study found no evidence of distinct within symptom clustering in relation to GAD and MDD. Therefore, it appears even in the context of low ER ability (i.e. emotion dysregulation), clear separation of symptoms into their relevant construct is not evident. This therefore highlights the complexity of internalising symptomology in adolescence (McElroy et al., 2018), as the current findings may suggest that even in the case of a network structure based on individuals with low ER ability, that there is still little that separates the constructs of anxiety and depression in adolescence. The authors acknowledge this study utilised data from a non-clinical sample, however previous similar general population and clinical adolescent based studies using network analytic techniques have yielded similar results (McElroy et al., 2018; McElroy & Patalay, 2019).

Regarding symptom importance, the GAD symptom ‘worrying often’ had the highest expected influence followed closely by GAD symptoms, ‘restlessness’ and ‘feeling nervous’ for the ‘low ER ability’ network. This tentatively suggests that the presence of these symptoms may increase the likelihood that more serious symptoms in the network may be activated. This makes intuitive sense in the context of emotion dysregulation, as aforementioned, symptoms such as intense worry or nervousness can be representing ‘negative affect states’ (McElroy et al., 2018). Therefore, one possible interpretation (in the context of this emotionally dysregulated group) is that these adolescents are unable to implement adaptive ER regulation strategies to manage the distress experienced by this intense worry (Klemanski et al., 2017; McLaughlin et al., 2011; Schäfer et al., 2017). The findings yielded from the LPA demonstrated that this group of adolescents had the greatest difficulty engaging in goal directed behaviour, have limited access to strategies to help to regulate their emotions, are unable to accept their distress, have no clarity on which emotions they are experiencing and often act on impulse. Therefore, it stands to reason when experiencing intense worry or nervousness, this group of adolescents are more likely to implement maladaptive ER strategies e.g. avoidance or rumination (Schäfer et al., 2017). In theory, when a person utilises maladaptive ER strategies, this could in turn lead to other internalising symptoms becoming ‘activated’ e.g. sleep or appetite problems and overtime more serious symptoms such as low mood, anhedonia, hopelessness, suicidal ideation; therefore allowing for the dynamic interactions and mutual reinforcement between the other internalising symptoms in the network. Therefore these highly central symptoms are particularly relevant and applicable to a number of transdiagnostic therapeutic approaches where ER is a core component of case conceptualisation and treatment (Neacsiu et al., 2014), and is also consistent with prior empirical research in the context of adolescence (McElroy et al., 2018). However, given the low stability of the low ER network, these findings can only be considered exploratory, and therefore should be interpreted with caution.

Additionally, it was expected that core symptoms required for a diagnosis of MDD would be highly influential within the low ER network, however this was not the case. Interestingly MDD symptom ‘anhedonia’ which is one of the essential criteria necessary for a diagnosis of MDD according to DSM-5 criteria (American Psychiatric Association, 2013), had among the lowest expected influence. This may be reflective of the sample. Previous research has shown males are more likely to exhibit higher rates of anhedonia than females, whereas females are more likely to experience MDD symptoms such as sleep, appetite, and concentration problems as well as fatigue, which may shed light on the above findings (Fried et al., 2014). As expected, in the context of the intermediate ER ability network, the MDD symptoms which pertain to ‘anhedonia’ and ‘hopelessness, which are essential for a diagnosis of MDD according to DSM-5, had the lowest expected influence. ‘Risk’ was also low on expected influence, therefore having little influence over other symptoms within the network. This could tentatively suggest that an ability to effectively manage and regulate one’s emotions prevents these symptoms from being able to manifest.

Finally, network comparison permutation tests were used to empirically compare whether both ER ability networks significantly differed from one another. The findings indicated that there were no significant differences in the overall connectivity of the ER ability networks, even in the case of the matched sample sizes. While this was a surprising finding, this may be reflection of the limitations of the data used to test our hypothesis. Specifically, given the small sample size, the current study may not be adequately powered to detect meaningful differences between these networks. At the time of writing, the authors were not able to source an available large scale secondary data set which adequately captured ER ability among the age range of interest. The authors intention was to use this exploratory study as a first step to explore ER as a plausible mechanism for explaining the context in which a network of anxiety and depression symptoms might begin to connect, perpetuate and ultimately diverge. More specifically to propose a conceptual framework to explain symptom connectivity among networks of internalising symptoms in adolescent samples. Therefore, the authors stress the value of the conceptual framework put forward in the current study, alongside the study design but assert caution in the interpretation of the data given the low stability of the low ER network. Consequently, it is imperative that the hypotheses proposed in the current study are further examined using more adequately powered data. Further, ER should be considered a fruitful avenue of exploration in future NA studies examining internalising symptomatology in adolescence, given the existing empirical evidence base.