Each round, participants were presented with the profile information of the other players and decided to vote to either accept (i.e., "Keep") or reject (i.e., "Kick out") each co-player, while led to believe that co-player was simultaneously voting to accept or reject the participant. Each profile was presented until the participant voted. To make the task more realistic, a statement appeared on the screen saying, "Waiting for [co-player name] to vote. . .," if participants voted faster than the simulated voting time assigned to the co-player for that round (based on actual voting speeds from pilot testing). Following the vote, a fixation cross was presented for 1000 ms, followed by feedback indicating how the co-player voted for the participant. A green thumbs up was shown on the screen indicating social reward/acceptance feedback, and a red thumbs down was presented indicating social rejection. Feedback was displayed for 2000 ms. This was followed by a screen that had two scales for participants to rate how much they liked the co-player and how much they thought other people would like the co-player, ranging from 1 (Not at all) to 9 (Extremely). Participants then saw a blank screen for 1500 ms before the next co-player profile within the round was presented. At the end of each round, participants were shown the picture of the co-player that was voted off during that round. All participants reached the final island at the end of the sixth and final round. Over the course of the 51 trials across the six rounds, participants were presented with roughly equal acceptance and rejection feedback, but ultimately "won" the game without being voted out by peers.

To increase believability, members of study staff acted as though they were in communication with other labs during the study setup and introduced pauses in the experiment to "wait" for other labs to be ready to begin. At the end of the task, prior to being debriefed, participants were asked to verbally indicate whether they believed that the task that they were playing was real in that they were playing against other live players. This was assessed with a 1-item question on a scale from 1 to 5, with higher scores indicating stronger belief in the task. On average, participants reported that they moderately believed that the task was real (M = 3.35, SD = 1.36), and belief ratings were not correlated with the residual RewP measure obtained from this task (p = 0.804).

## EEG Data Collection and Processing

EEG data were recorded with a 32-electrode cap BrainProducts actiCHamp system (Munich, Germany) based on a standard 10/20 layout. Facial electrodes were placed approximately 1 cm above and below the left eye and 1 cm from the outer corners of the eyes to measure electrooculogram (EOG) from eye movements. Bipolar electrodes were referenced to an electrode placed on the back of the neck of the participant. Mastoid references were electrodes TP9 and TP10. Impedances were reduced to approximately 10 k. A 24 bit resolution and sampling rate of 1000 Hz were used to digitize the recordings.

BrainVision Analyzer software (Brain Products, Munich, Germany) was used to process the EEG data. Data were rereferenced to an average of the two mastoids and band-pass filtered with 0.01 and 30 Hz as cutoffs with 24 db/oct slopes. Data were segmented 500 ms prior to and 1000 ms after acceptance/rejection feedback. Ocular correction was conducted using a modification of Gratton's algorithm (Gratton et al., 1983).

Automatic artifact rejection criteria were a voltage step greater than 50.0 µV between sample points, maximum voltage difference of 175.0 µV within trials, and minimum voltage difference of 0.5 µV within 100 ms intervals. Data were then inspected visually to reject any remaining artifacts. Following artifact rejection procedures, participants had on average 26.61 (SD = 1.44) trials for the accept condition and 24.06 (SD = 1.32) trials for the reject condition at Cz. The 200 ms prior to feedback was set as the baseline.

ERPs were averaged across participants for both acceptance/social reward and rejection/non-reward. ERP components were scored using the time window approach based on visual assessment. To examine RewP, data were extracted between 250 and 350 ms at Cz, consistent with RewP research using monetary reward tasks (Ethridge et al., 2017). We calculated unstandardized residual RewP to acceptance adjusting for RewP to rejection for analysis (Meyer et al., 2017). More positive values indicate greater responses to social reward. The RewP residual score has been shown to be reliably measured in this task (Ethridge and Weinberg, 2018).

## Data Analysis

To examine the associations between variables, bivariate correlation analyses were first conducted between residual RewP to social reward (i.e., RewP to acceptance adjusting for responses to rejection), clinical symptoms (depression, social anxiety), and social and non-social lifetime stress exposure. Next, both simple mediation and moderation analyses were conducted to examine the extent to which social reward responsiveness (residual RewP) mediated or moderated relationships between social and non-social stress exposure, and participants' depressive and anxiety symptoms. To conduct these analyses, the PROCESS v3.1 macro for SPSS was used (Hayes, 2017).

## RESULTS

## Preliminary Analyses

Participants' lifetime social stress severity scores ranged from 0 to 71 (M = 25.62, SD = 15.59). Lifetime non-social stress scores ranged from 0 to 46 (M = 9.81, SD = 8.54). Participants' depression scores (i.e., IDAS dysphoria symptoms) ranged from 10 to 42 out of a possible 50 (M = 21.86, SD = 7.51). Participants' social anxiety scores ranged from 6 to 30 out of a possible 30 (M = 13.18, SD = 5.55). The IDAS dysphoria and social anxiety subscales had high internal consistency (Cronbach's αs = 0.86 for each measure). With clinical cutoffs for IDAS identified by Stasik-O'Brien et al. (2018), 21.6% of participants were in the clinical range for symptoms of depression (clinical cut-offs for the social anxiety scale from the 99-item IDAS were not available).

ERP waveforms for acceptance and rejection conditions and corresponding scalp distribution for the difference of acceptance minus rejection conditions are presented in **Figure 1**. RewP to acceptance and rejection feedback had high split-half reliability at Cz (Spearman-Brown coefficients = 0.87 and 0.86, respectively). To examine associations between participants' symptoms, social and non-social lifetime stress exposure, and residual RewP, bivariate correlation analyses were first conducted (see **Table 1**). As expected, greater lifetime social stress exposure was positively associated with depression (r = 0.37, p < 0.001) and social anxiety (r = 0.26, p < 0.001). Non-social stress exposure was also positively correlated with symptoms of depression (r = 0.38, p < 0.001) and social anxiety (r = 0.20, p = 0.002). Social and non-social stress exposure were not significantly correlated with the RewP residual score. The RewP residual score was not significantly correlated with either social anxiety or depressive symptoms, suggesting that social reward responsiveness did not mediate the association between lifetime stress exposure and participants' symptom levels. Indeed, bootstrapped confidence intervals of tests of indirect effects of social and non-social lifetime stress exposure on internalizing symptoms through residual RewP all included 0 (see **Table 2**).

## Moderation Analyses

Four moderation analyses were conducted to investigate relationships between social and non-social lifetime stress exposure, RewP residual scores, and depressive and anxiety symptoms. Specifically, we examined residual RewP as a moderator of associations between social and non-social lifetime stress exposure and depressive and social anxiety symptoms. Main effects of social stress or non-social stress and residual RewP were entered into each model. Then the interaction between either social or non-social stress and residual RewP was entered (see **Table 3**).

The overall model for lifetime social stress exposure predicting depressive symptoms was significant, R 2 = 0.17, F(3, 227) = 15.01, p < 0.001. The significant main effect of social stress exposure in predicting symptoms of depression was qualified by an interaction between social stress exposure and RewP residual scores (see **Figure 2A**), t(227) = -2.28, p = 0.024. Decomposing this interaction using simple slopes revealed that greater lifetime social stress exposure predicted more depressive symptoms at low (-1 SD), mean, and high (+1 SD) levels of residual RewP. The magnitude of the relationship between social stress and depression was relatively stronger at low [simple slope = 0.24, SE = 0.04, t(227) = 5.96, p < 0.001] as compared to mean [simple slope = 0.18, SE = 0.03, t(227) = 6.10, p < 0.001], and high levels of residual RewP [simple slope = 0.11, SE = 0.04, t(227) = 2.85, p = 0.005]. To further understand this relationship, we also examined the effects of RewP at high and low levels of social stress. A reduced residual RewP predicted more depressive symptoms only at a high (+1 SD) level of social stress exposure [simple slope = -0.44, SE = 0.17, t(227) = -2.64, p = 0.009]. The simple slopes at low (-1 SD) and mean levels of social stress exposure were not significant (ps = 0.642 and 0.125, respectively; see **Figure 2B**).

For illustrative purposes, we divided the social stress variable into thirds. We then split these participants based on their depressive symptoms into high and low depressive symptom groups via a median split. As depicted in **Figure 3**, RewP was

TABLE 1 | Bivariate correlations between clinical symptoms, life stress variables, and social reward responsiveness.

∗p < 0.01, ∗∗p < 0.001; RewP, Reward positivity.

TABLE 2 | Model coefficients for simple mediation models testing effects of lifetime social and non-social stress severity and residual RewP on clinical symptoms.

∗p < 0.01, ∗∗p < 0.001; RewP, Reward positivity; b, unstandardized regression coefficients; SE, standard error.

TABLE 3 | Regression analyses testing the main and interaction effects of lifetime social and non-social stress severity and residual RewP on depressive symptoms (IDAS dysphoria subscale).

RewP = Reward positivity.

relatively reduced in the high lifetime social stress exposure/high depressive symptom group as compared to the high lifetime social stress exposure/low depression group.

The overall models for lifetime social stress exposure predicting social anxiety symptoms, non-social stress exposure predicting depressive symptoms, and non-social stress exposure predicting social anxiety symptoms were all significant (see **Table 3**). However, only social and non-social lifetime stress exposure were significant predictors of clinical symptoms. The interactions between social or non-social stress exposure and participants' residual RewP were not significant in these models, suggesting that residual RewP may be a relatively specific moderator of the impact of lifetime social stress exposure on symptoms of depression rather than social anxiety.

## DISCUSSION

The present study examined associations between social and nonsocial lifetime stress exposure, social reward responsiveness as measured by RewP using the Island Getaway task, and symptoms of depression and social anxiety in a sample of emerging adults. Both social and non-social stress exposure were related to depressive symptoms. Additionally, social and non-social stress exposure were associated with social anxiety symptoms. In contrast, we did not find significant bivariate associations between the RewP residual score and participants' symptoms, and results did not support social reward responsiveness (as measured by RewP) as a mediator of the effect of lifetime stress exposure on symptom levels. Instead, a significant interaction emerged between social stress and RewP to acceptance (adjusting for RewP to rejection via residual score) predicting depressive symptoms, such that the combination of greater lifetime social stress exposure and a reduced RewP to social reward was associated with greater depressive symptoms. Moreover, social reward responsiveness only predicted depressive symptoms at high levels of social stress. Finally, this moderation effect of RewP on symptom outcomes was unique to symptoms of depression and did not extend to symptoms of social anxiety.

Although preliminary and in need of replication, these results suggest that reduced social reward responsiveness may constitute a vulnerability for depressive symptoms following exposure to social stress, specifically. It is also possible that having greater reward responsiveness to social

to mean and high levels of residual RewP. Reduced RewP residual predicted more depressive symptoms only at a high level of social stress.

reward may help to protect against the impact of stress, particularly stress in interpersonal relationships. Individuals with blunted social reward responsiveness may be less likely to seek out and benefit from positive social interactions, which could inhibit their ability to cope with stress (Setterfield et al., 2016). As such, RewP to social feedback might predict more specific depressive symptom presentations, such as social withdrawal or social anhedonia. As RewP is relatively stable throughout development (Kujawa et al., 2018), identifying these specific symptom manifestations may improve understanding of depression onset and potential avenues for intervention before symptoms manifest (i.e., examining reduced social reward responsiveness and targeting these alterations early on).

More broadly, these findings emphasize the importance of examining social reward, in addition to monetary reward, in developmental trajectories of depression. Additional research should be conducted examining responses to multiple types of reward, including social reward, within the same sample to investigate whether particular types of reward responsiveness have unique predictive utility for depression. In addition, the current results emphasize the importance of considering specific dimensions of stressful experiences in clinical neuroscience research. That is, despite growing evidence that alterations in neural systems involved in positive emotions likely reflect a vulnerability that increase risk for later depression (for a review, see Kujawa and Burkhouse, 2017), little research has examined the possibility that a specific neural process might predict responses to specific types of stress. Despite the exploratory nature of the scoring of social and non-social stress scales used herein, the present study has taken a preliminary step to fill this gap.

Our results are broadly consistent with prior research showing that reduced activity in brain regions involved in reward processing may pose a potential increased risk for the development of depression in individuals exposed to stress (e.g., Nikolova et al., 2012; Corral-Frías et al., 2015). Despite a growing body of literature on the effects of stress and monetary reward responsiveness on depression, the present study is among the first to examine the effects of both life stress exposure and social reward responsiveness on depressive symptoms, and is the first to examine lifetime stress exposure. Our results suggest that, rather than directly explaining the relationship between life stress and depressive symptoms, reduced responsiveness to social reward may be a vulnerability factor specifically when people are exposed to social stress, a key risk factor for the development of depression. This suggests that individual differences in social reward responsiveness may be one factor that influences likelihood of developing depression following exposure to social stress, and, as such, individuals low in social reward responsiveness might benefit from targeted prevention.

Strengths of the current study include evaluation of competing hypotheses with regard to reward responsiveness as a mediator or moderator of the effects of stress on psychiatric symptoms, extension to the social reward domain, assessment of lifetime stress exposure, and tests of specificity of associations for depression or internalizing symptoms more broadly. A few limitations should be considered when interpreting these results. First, the study design was cross-sectional. For this reason, causality and the directionality cannot be determined.

In particular, although mediation analyses can be performed with cross-sectional data, results should be replicated with longitudinal data. Second, given prior work linking reduced RewP and activation of ventral striatum to monetary reward to the later emergence of depressive symptoms (Kujawa and Burkhouse, 2017; Keren et al., 2018), we interpreted RewP as an indicator of a potential vulnerability for depression in the context of lifetime social stress exposure. However, the study design did not enable us to examine whether reduced RewP to social reward emerges prior to exposure to social stress or to increases in symptoms of depression. Future longitudinal research must be conducted to examine associations between social reward responsiveness and stress exposure across time and development, and to assess social reward responsiveness across levels of analysis, including behavior and circuit measures (National Institute of Mental Health [NIMH], 2019). Third, although the items that comprised the STRAIN social and non-social stress subscales were binned based on whether they were related to social situations or interactions, the present study is limited in its ability to test the validity of this scoring approach. The analyses of social vs. non-social stress scales of the STRAIN are exploratory and should be interpreted as such. Further work examining the extent to which these subscales converge with other indicators of social and non-social strain is needed. Fourth, although subthreshold depressive symptoms are a strong predictor of subsequently developing MDD (e.g., Keenan et al., 2008), this was a non-clinical sample and future research is needed to examine whether the present results generalize to clinical populations. Likewise, future studies could sample adolescents and adults from the community who have greater lifetime stress exposure burdens to examine the associations described here in other, more generally representative, populations. Fifth, we employed a selfreport measure of current depression and anxiety symptoms in the present study, and it will be important for future studies to utilize interview-based assessments of participants' symptoms and current and past history. Finally, given the number of models tested and relatively modest effect sizes, the current results must be interpreted cautiously, and replication is needed.

It is also worth noting that we only measured responses to social acceptance and rejection feedback, as opposed to neutral feedback for a few reasons. First, measuring acceptance/social reward and rejection feedback is consistent with a commonly used monetary reward paradigm to elicit RewP (Proudfit, 2015). In this task, the relative response to reward vs. loss has consistently been linked cross-sectionally and prospectively with depressive symptoms (Bress et al., 2015; Nelson et al., 2016; Kujawa et al., 2019). Second, evidence suggests that RewP presents as a relative positivity to monetary reward or the best possible outcome in a task and is less sensitive to differences between neutral and loss feedback (e.g., Kujawa et al., 2013). Finally, given the nature of social interaction tasks, "neutral" feedback is difficult to manipulate, as there would likely be individual differences in how people process feedback that is more ambiguous. The inclusion of a third condition would lengthen the task considerably. Nonetheless, additional research is needed to examine neural responses to neutral feedback in social vs. monetary reward tasks.

Notwithstanding these limitations, the present study is the first to examine how social reward processing is associated with lifetime stress exposure and depression and anxiety symptoms in a large sample of emerging adults – a developmental period when rates of depression increase dramatically (Kessler et al., 2001). The results highlight the potential utility of ERP measures of social reward responsiveness for clarifying pathways to the emergence of depression. In addition, they elucidate a pathway that appears to be relatively specific for lifetime social (vs. non-social) stress exposure in predicting depressive (vs. anxiety) symptoms. These findings may thus have implications for designing preventions targeting those low in social reward responsiveness, with the possibility of buffering against the negative effects of social stress before symptoms emerge.

## DATA AVAILABILITY

All datasets generated for this study are included in the manuscript and/or the supplementary files (doi: 10.6084/m9. figshare.9033842).

## ETHICS STATEMENT

This study was carried out in accordance with the recommendations of Declaration of Helsinki with written informed consent from all participants. All participants gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved b the McGill University research ethics board.

## AUTHOR CONTRIBUTIONS

SP, PE, AW, and AK contributed to the design of this research study. PE and AW oversaw the conduct of the research study, data collection, and management. AK designed the original Island Getaway task. GMS created the STRAIN and oversaw the preparation of the stress data for inclusion, which was led by GSS. SP and AK analyzed and interpreted the data by consulting with all co-authors. SP and AK drafted the manuscript, which was subsequently revised by all co-authors. All authors read and approved the final version of the manuscript.

## FUNDING

Implementation of the STRAIN in this study was supported by a Society in Science—Branco Weiss Fellowship, NARSAD Young Investigator Grant #23958 from the Brain and Behavior Research Foundation, and National Institutes of Health grant K08 MH103443 to GMS.

## REFERENCES

fnbeh-13-00178 August 6, 2019 Time: 16:45 # 11

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer JK declared a past collaboration with one of the authors AW to the handling Editor.

Copyright © 2019 Pegg, Ethridge, Shields, Slavich, Weinberg and Kujawa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Social and Non-social Reward: A Preliminary Examination of Clinical Improvement and Neural Reactivity in Adolescents Treated With Behavioral Therapy for Anxiety and Depression

Karen T. G. Schwartz1 \*, Maria Kryza-Lacombe1 , Michael T. Liuzzi2 , V. Robin Weersing1,2 and Jillian Lee Wiggins1,2

1 San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States, 2 Department of Psychology, San Diego State University, San Diego, CA, United States

Background: Pediatric anxiety and depression are highly prevalent and debilitating disorders that often co-occur. Neural circuitry of reward processing has been shown to be implicated in both, and there is an emerging evidence base linking treatment response to brain patterns of reward processing. The current study aimed to add to this literature by investigating the association between clinical improvement and social and non-social reward in youth previously treated for anxiety and depression.

#### Edited by:

Amanda E. Guyer, University of California, Davis, United States

#### Reviewed by:

Autumn Kujawa, Vanderbilt University, United States Thomas M. Olino, Temple University, United States

> \*Correspondence: Karen T. G. Schwartz<EMAIL_ADDRESS>
Received: 31 March 2019 Accepted: 16 July 2019 Published: 23 August 2019

#### Citation:

Schwartz KTG, Kryza-Lacombe M, Liuzzi MT, Weersing VR and Wiggins JL (2019) Social and Non-social Reward: A Preliminary Examination of Clinical Improvement and Neural Reactivity in Adolescents Treated With Behavioral Therapy for Anxiety and Depression. Front. Behav. Neurosci. 13:177. doi: 10.3389/fnbeh.2019.00177 Methods: The current study leveraged clinical improvement data from a successful randomized controlled trial testing the efficacy of a transdiagnostic, brief behavioral treatment for youth diagnosed with anxiety or depression. Participants (N = 15) interested in engaging in a neuroimaging follow-up underwent an fMRI scan, during which they completed social (i.e., Face Task) and non-social (i.e., Piñata Task, a youth-friendly monetary incentive delay task) reward tasks. Whole-brain activation and functional connectivity analyses identified neural responses to the tasks separately; a third set of analyses directly compared clinical improvement-related findings to understand the impact of task context on neural reactivity to reward.

Results: Activation-based findings were sparse; however, connectivity as a function of degree of treatment response was apparent and robust. Within the context of social reward, significant clusters within frontal and temporal regions driven by happy face contrasts, the social reward stimulus, were observed. This supports connectivity between these regions and both amygdala and ventral striatum seeds as a function of degree of clinical improvement. Connectivity within the context of non-social reward also yielded significant clusters in temporal and parietal regions. Here too, the magnitude and direction of region coupling depended on the degree of clinical improvement and the task conditions. No differences in connectivity by task type as a function of clinical improvement were found.

Conclusion: Findings serve as preliminary evidence that neural regions found to be related to clinical improvement within the context of social and non-social reward are similar to regions that have been shown to support reward processing in normative samples. Implications for treatment and future work are discussed.

Keywords: reward, behavioral therapy, fMRI, adolescents, anxiety, depression

## INTRODUCTION

Pediatric anxiety and depression are highly prevalent, debilitating, and associated with a chronic course and longterm impairment (e.g., Merikangas et al., 2010). They frequently co-occur, both concurrently and sequentially (Garber and Weersing, 2010), and data suggest shared genetic risk (Thapar and McGuffin, 1997). Anxiety and depression also respond to the same classes of psychosocial (e.g., cognitive and behavioral therapies) and pharmacological (e.g., SSRIs) interventions (Compton et al., 2004), and treatment of one target disorder may lead to cross-over effects on the other disorder (e.g., interventions that target depression may reduce non-targeted anxiety symptoms; Garber et al., 2016).

This work has served as the rationale for the development of transdiagnostic interventions designed to target core processes across anxiety and depression and treat them as a unified problem area. In adults, unified protocols have demonstrated superior symptom improvement across clinical domains, both compared to control conditions and disorder-specific care (see McEvoy et al., 2009 for a review). The few studies that have tested transdiagnostic protocols in pediatric samples have also documented the efficacy in targeting internalizing disorders (Chu et al., 2016; Ehrenreich-May et al., 2017; Weersing et al., 2017), suggesting shared processes of disorder and recovery across anxiety and depression. However, such findings are in contrast to evidence that anxiety and depression differ in their responses to intervention. For instance, interventions targeting anxiety have the largest intervention effect sizes in the pediatric literature; in contrast, depression treatment effects are the smallest in the field (Weisz et al., 2017). Furthermore, pediatric unified protocols have evidenced better effects on anxiety, compared to depression outcomes, despite success overall (Queen et al., 2014; Weersing et al., 2017). Thus, additional work must be done to examine underlying dimensional factors that cross diagnostic boundaries and may more effectively account for observed differences in response to care in internalizing youth. Such efforts are aligned with the priorities of the National Institutes of Health (NIH) in an effort to improve the efficacy of treatments (e.g., Insel et al., 2010).

One neurobiological mechanism that has been implicated across anxiety and depression is reward processing, in both social and non-social contexts. Reward processing encompasses neural reactivity associated with anticipation and consumption of positive gains, such as monetary winnings or social approval, as well as behavioral learning that motivates future actions. Studies of reward processing in pediatric samples have included tasks based on monetary incentives or tasks focused on social appraisal. The former maps onto the adult literature, as money is an ecologically valid incentive that is easily manipulated and distributed by study personnel. Social reward is a relatively new area of interest relevant to pediatric samples, given the developmental alterations in social valuation during adolescence (Steinberg and Morris, 2001). Indeed, adolescence is a period of substantial neural maturation in areas associated with reward (e.g., Ernst et al., 2006; Galvan, 2010). Concurrent developmental changes include enhanced need for social inclusion and peer acceptance (e.g., Choudhury et al., 2006). Thus, happy faces may be particularly rewarding during this developmental period (Scherf et al., 2012). Furthermore, happy faces are utilized to signal success on achievement-oriented tasks, such as academic assignments; therefore, happy faces are emotionally salient and socially relevant cues that can be reasonably expected to probe reward processing neural circuitry in youth.

Anhedonia, or the motivation and ability to seek and experience rewarding activities, is a core diagnostic feature of depression, though not anxiety. Anhedonia has been shown to serve as a phenotype of aberrant integration of reward and arousal, above and beyond other symptoms of internalizing disorders in youth (Pornpattananangkul et al., 2019). Generally, youth with or at risk for depression evidence blunted patterns of response in areas associated with reward, and signals appear to be further diminished by intensified cognitive control (e.g., Forbes et al., 2006, 2009; Chantiluke et al., 2012; Wiggins et al., 2017). Four treatment trials targeting depression in youth included task-based neuroimaging components prior to treatment, only (i.e., baseline; Forbes et al., 2010), or at both pre- and post-treatment timepoints (i.e., baseline and follow-up; Straub et al., 2015; Chuang et al., 2016; Mori et al., 2016); three studies probed non-social (i.e., monetary) reward processing (Forbes et al., 2010; Straub et al., 2015; Mori et al., 2016). Pre- to post-treatment changes in brain patterns suggested that aberrant responses to reward conditions "normalized" in youth as a function of treatment (i.e., mirrored patterns observed in healthy controls; Mori et al., 2016). Pre-post signal reductions in areas associated with emotion regulation were observed as a function of treatment engagement and related significantly to larger depression symptom reductions at post and follow-up (Straub et al., 2015).

The conceptual connection between anhedonia, depression and reward processing is clear; however, reward processing has also be implicated in anxiety in a different fashion. Anxiety disorders are characterized by avoidance of anxiety-provoking stimuli, escape behaviors when exposed to anxiety triggers, and negative "reward" of avoidance and escape through the reduction of anxious distress. In adolescence, avoidance of social interactions becomes particularly prevalent due to intensified concerns regarding peer approval and acceptance. Though

smaller than the depression literature, neuroimaging findings on reward processing in pediatric anxiety have begun to accrue. fMRI studies enrolling anxious vs. non-anxious youth evidenced increased striatal, frontal, and limbic reactivity during social and non-social reward tasks (e.g., Guyer et al., 2012; Benson et al., 2015; Jarcho et al., 2015); of note, these studies did not include a treatment component. To date, five reports citing data from four independent trials serve as the current literature base on neural predictors of response to psychosocial interventions targeting pediatric anxiety (McClure et al., 2007; Maslowsky et al., 2010; Kujawa et al., 2016; Burkhouse et al., 2017; White et al., 2017). Three studies incorporated fMRI data from baseline and followup (McClure et al., 2007; Maslowsky et al., 2010; White et al., 2017), while two representing the same trial utilized baseline data only (Kujawa et al., 2016; Burkhouse et al., 2017). However, no investigations examined reward processing, specifically, as a neural predictor or mechanism of treatment response in this population.

It is notable that none of the published findings tested a transdiagnostic protocol, yet the majority of samples evidenced substantial diagnostic comorbidity. Furthermore, changes in striatal reactivity in response to a depression-focused intervention was associated with cross-over effects, such that changes of greater magnitude predicted a faster rate of decline in anxiety symptoms across time (Forbes et al., 2010). Thus, work within the transdiagnostic realm is warranted. Moreover, given the relevance of social processing in anxiety and depression, disorders frequently characterized by interpersonal difficulties, focusing on social in addition to non-social reward is necessary.

In sum, the literature base on neural mechanisms of treatment response in internalizing youth is in its infancy. Studies are few, segregated by diagnosis, and lack replication within treatment modality across independent teams. Additionally, treatment paradigms employed were diagnosis-specific, despite high rates of comorbidity within samples, and typically focused on only one aspect of reward (i.e., non-social reward). To address these gaps in the literature, the current study leveraged resources from a successful randomized controlled trial (RCT) to examine neural mechanisms within the context of a treatment trial in a comorbid sample of anxious-depressed youth. Specifically, we sought to examine both social and non-social reward processing as promising neural mechanisms of clinical improvement in a subsample of youth, ages 8−16 years, enrolled in a multi-site RCT investigating the effectiveness of a transdiagnostic brief behavioral therapy (BBT) for pediatric anxiety and depression (Weersing et al., 2017). BBT may be a particularly relevant treatment paradigm to evaluate the relationship between both social and non-social reward processing and treatment response due to the behavioral target of intervention. That is, both anxiety and depression are characterized by avoidance of negative affect and behavioral withdrawal, including from social situations. Behavioral interventions directly target avoidance by increasing reinforcement in response to engagement and decreasing reinforcement for avoidance behaviors. Furthermore, behavioral interventions are developmentally appropriate for youth, as behavioral tasks are active, concrete, and cognitively straightforward (Martin and Oliver, 2018). So, we re-contacted this sample to collect neuroimaging data post-treatment with the aim of relating imaging data to variables defined during the original RCT participation, such as baseline characteristics of youth and BBT treatment response.

The original BBT trial evidenced statistically significant positive effects across measures. Youth who received BBT were more likely to be categorized as treatment responders by the post-treatment assessment [i.e., Clinical Global Impressions, Improvement Scale (CGI-I; Guy, 1976) ≤2] and evidenced improved functioning compared to those receiving assisted referral to care (ARC; control condition). Furthermore, the rate of functional improvement among those who received BBT was significantly faster than improvements reported by those in the ARC condition (see Weersing et al., 2017 for full methods and CONSORT). The BBT intervention targeted avoidance across anxiety and depression by promoting graded engagement in important life tasks, providing participants with concrete, alternate experiences meant to be rewarding (Weersing et al., 2008). Some of the targeted tasks were social in nature while others were based on success experiences, as youth have a number of difficulties with achievementoriented activities. We thus took the opportunity to probe the distinction between social (i.e., happy face) and non-social (i.e., monetary) reward tasks, as a comparison like this in the same sample has yet to be published. Additionally, the developmental maturation of reward processing circuitry in adolescence suggests differences in salience and associated neural reactivity in response to social and monetary reward cues can be expected.

To our knowledge, this study is the first to (a) evaluate social reward in internalizing youth within the context of treatment (b) inform the relationship between reward processing and clinical improvement in response to a youth-focused transdiagnostic psychosocial intervention, and (c) analyze data from one sample of youth who each performed two tasks. Planned statistical analyses represent secondary analyses of data from a completed clinical trial (Weersing et al., 2017), combined with original neuroimaging data collection. Scans were performed posttreatment to generate hypotheses regarding the long-term role of treatment response in reward processing. These efforts are exploratory in nature to contribute to the establishment of a literature base of neural mechanisms of treatment response in internalizing youth and bolster future research.

## MATERIALS AND METHODS

The protocol for the neuroimaging follow-up was reviewed and approved by the University of California, San Diego Institutional Review Board. Secondary approval was obtained from San Diego State University's Institutional Review Board. Written informed consent was obtained from all participating caregivers and adolescents over 18 years of age, prior to the administration of any study materials, in accordance with the Declaration of Helsinki. Adolescents younger than 18 provided assent, in addition to their caregiver's written informed consent to participate. Consent forms included a specific clause allowing

data from initial BBT participation to be linked to current data collection.

## Participants

Neuroimaging and clinical improvement data from 15 youth were analyzed for the current study. All BBT families initially recruited from the San Diego site (October 2010 to December 2014) who consented to being contacted in the future regarding additional opportunities for research were considered for participation in this neuroimaging follow-up (N = 49). Recruitment targeted participants randomized to the BBT arm to allow for inferences to be made regarding the association between treatment response to BBT and reward processing circuitry, as well as to control for treatment type and dose received. BBT caregivers of record were contacted via phone between August 2016 and November 2017 to assess interest as well as youth contraindications for undergoing an fMRI scan.

Of the 49 participants randomized to receive BBT through the San Diego site, 44 were contacted to assess interest in the current investigation (i.e., consented to further contact, not lost to follow-up by the RCT final assessment). Of those 44, four were lost to follow-up (e.g., contact information was out of date) and one participant had died since RCT study completion. We connected by phone with the remaining 39 participants to assess eligibility; 10 declined to participate, while 29 completed the phone screen. Of those screened, 21 met eligibility requirements for the neuroimaging follow-up (e.g., expressed interest in completing study activities, denied contraindications for the fMRI environment). Post-screen, three eligible participants were lost to follow-up and one declined to participate, prior to consenting to the current study. Thus, the current study enrolled 17 youth. Of the 17 consented to participate in the neuroimaging follow-up, one individual refused to complete the fMRI scan and one individual yielded an unusable dataset due to technical error. **Table 1** reports sample characteristics of the 15 youth included in the current study's analyses.

Participants enrolled in the neuroimaging follow-up were initially randomized at a mean age of 11.43 years (SD = 1.61; range: 8−14 years); at the time of scan, participants had a mean age of 15.29 years (SD = 2.42; range: 9−19 years). Primary diagnostic complaints at initial RCT enrollment were predominantly within the anxiety spectrum [n = 15; 35% (n = 6) Generalized Anxiety Disorder, 35% (n = 6) Separation Anxiety Disorder, 18% (n = 3) Social Phobia]; 29% (n = 5) had clinically elevated depression in addition to anxiety. In terms of treatment response, 41% (n = 7) of the participants engaged in the neuroimaging follow-up were characterized as BBT treatment responders at post-treatment.

Those enrolled in the neuroimaging follow-up did not significantly differ from those recruited in San Diego who were randomized to BBT but ineligible for the follow-up on any demographic or clinical indicators at baseline or posttreatment, with the exception of baseline clinical severity of internalizing symptoms. Those who did not engage in the neuroimaging follow-up had higher average severity scores at baseline (M = 4.44, SD = 0.88) compared to those who participated [M = 4.00, SD = 0.61; t(43.34) = 2.04, p = 0.048]. TABLE 1 | Sample demographics.

Continuous variables are displayed as M (SD); categorical variables are displayed as N (%). CGI-I: Clinical Global Impressions, Improvement Scale assigned posttreatment (Treatment Response: CGI-I ≤2); SCARED, Screen for Child Anxiety and Related Disorders completed at time of scan; MFQ, Mood and Feelings Questionnaire completed at time of scan.

However, this difference, though statistically significant, does not reflect practical differences in clinical presentation. It is also notable that a smaller proportion of the neuroimaging sample was categorized as treatment responders at post (41%), versus rates of response observed in the BBT sample as a whole [57%; χ 2 (1) = 3.41, p = 0.065]. This difference was not statistically significant.

## Measures

#### Demographics

Caregivers provided updated demographic information at the time of scan (e.g., age, gender; see **Table 1**).

#### Clinical Characteristics

Clinical improvement was measured by the Clinical Global Impressions, Improvement Scale (CGI-I; Guy, 1976). The CGI-I is a 7-point, single item indicator of clinical change, such that lower scores represent improvement (1 = very much improved), while higher scores reflect clinical deterioration (7 = very much worse). Trained and reliable independent evaluators unaware of the participant's treatment condition assigned a CGI-I score at the post-treatment assessment, capturing change in symptoms of anxiety and/or depression since the baseline assessment. The original trial utilized the CGI-I as an indicator of treatment response; participants with a score of 1 (very much improved) or 2 (much improved) were categorized as treatment responders,

while those with a score of >3 (minimally improved) were categorized as non-responders (Weersing et al., 2017). The current study included the CGI-I dimensionally, as the main predictor of interest, to evaluate the association between clinical improvement and brain function.

The Schedule for Affective Disorders and Schizophrenia for School-Aged Children-Present and Lifetime Version (Kaufman et al., 1997) was used to determine eligibility at baseline (i.e., the presence of a primary anxiety or depression diagnosis). At the time of scan, caregivers completed the Screen for Child Anxiety and Related Disorders (SCARED; Birmaher et al., 1997) and the Mood and Feelings Questionnaire (MFQ; Angold et al., 1987) to inform severity of current anxiety and depression symptoms in youth, respectively, with higher scores indicating increased symptom severity (see **Table 1**). This study utilized the SCARED and MFQ in Additional Analyses intended to identify the impact of current symptoms on the observed pattern of fMRI findings.

#### Neuroimaging Paradigms

Youth completed two tasks that elicited neural activation within the context of social or non-social reward. Scans occurred 568−2317 days after the post-treatment assessment (see **Table 1** and "Additional Analyses"). Participants completed the social reward task prior to the non-social reward task. This determination was made due to the social reward task lasting longer with greater potential for cognitive fatigue than the nonsocial reward task. As two participants provided data collected in the inverted order, task order was included as a potential confound (see section "Additional Analyses").

#### **Social reward task (Face Task)**

Participants performed a jittered, event-related task with emotional face stimuli, including happy (i.e., social reward) faces, using a dot probe paradigm adapted from the Tel Aviv University/National Institute of Mental Health paradigm (Abend et al., 2014) during fMRI data acquisition. This task was modified to include four valence categories: happy, angry, sad, and neutral faces (see **Supplementary Figure S1**). Emotional faces were from the NimStim Set of Facial Expressions1 (Tottenham et al., 2009). Each trial began with a fixation cross for 500 ms. Next, neutral-neutral or neutral-emotional face pairs were presented on the screen for 500 ms, followed by a probe (< or >) presented for 1000 ms. The probe was positioned either in place of the emotional face (congruent condition) or the neutral face (incongruent condition). Participants were instructed to respond quickly and accurately by pressing the button that corresponded to the direction in which the probe was pointing (left or right). Inter-trial intervals were jittered (250−1180 ms, M = 715 ms).

The faces dot-probe paradigm was advantageous to control for potential differences in attention and to present social reward faces, in addition to faces reflecting other valences (angry, sad, neutral). These benefits allowed for the examination of specificity of response to the social reward faces. The inclusion of faces in dot-probe paradigms has been shown to probe areas implicated in reward processing in prior research in anxious (Shechner et al., 2012) and depressed (Forbes, 2011) youth.

Participants completed three runs of the Face Task (7 min. 27 s. per run). There were eight total conditions included in the task: happy-neutral/congruent, happy-neutral/incongruent, angry -neutral/congruent, angry-neutral/incongruent, sad-neutral/ congruent, sad-neutral/incongruent, neutral-neutral/congruent, and neutral-neutral/incongruent. Neutral-neutral pairs were randomly split into "congruent" and "incongruent" groups for analysis purposes. Due to lack of jitter between face and probe displays, presentation could not be separated for analysis. There were 48 trials per condition. All participants had >65% accuracy and were therefore included in analyses (see **Table 1**). Of the 15 participants included in the Face Task analyses, 14 participants had three usable runs and one had two usable runs (excessive motion in the third run; see fMRI Data Processing).

### **Non-social reward task (Piñata Task)**

A child-friendly monetary incentive delay task was utilized to assess neural functioning within the context of non-social reward (i.e., Piñata Task; see **Supplementary Figure S2**; Helfinstein et al., 2013; Wiggins et al., 2017; Dougherty et al., 2018). The Piñata Task is an event-related task (Helfinstein et al., 2013) previously used to reliably elicit reward-related brain activation in children (Wiggins et al., 2017). Each trial began with a variable length anticipation period consisting of a 2000 ms indicator of whether or not the participant had the opportunity to receive a reward during that round, followed by a 2500−5500 ms jittered delay period (together, referred to as the Cue Period). Then, the participant was presented with a target (i.e., turtle-shaped piñata) that they were instructed to "hit" (i.e., push a button to simulate striking the piñata). The fMRI operator explicitly instructed participants to attempt to hit the piñata during each trial, independent of reward condition. On reward trials, participants earned stars that translated into money earned (< \$15), if they struck the piñata within the time limit. The time to hit the piñata was automatically adjusted in real time (+/−50 ms), based on the participant's performance, to promote approximately 2/3 hit trials and 1/3 miss trials. If the participant pressed the button within the time allotted, the piñata broke, indicating a hit. Missed targets swung away (1500 ms). A basket was then shown displaying stars (reward/hit condition) or empty (reward/miss or no reward conditions; referred to as the Feedback Period, 1500 ms). Intertrial intervals were jittered.

Participants completed three runs with a total of 60 trials across all runs (30 reward, 30 no reward conditions). Task runs spanned 4 min and 52 s. Non-social analyses included all available data from 14 participants, all of whom completed three runs of the task. The fifteenth participant was excluded from analyses due to data acquisition error.

## ANALYTIC PLAN

Three sets of analyses were completed: (a) evaluation of the association between clinical improvement and neural reactivity within the context of social reward (i.e., in the Face Task); (b) evaluation of improvement and neural reactivity within the context of non-social reward (i.e., monetary; Piñata Task); (c) direct comparison of social and non-social reward findings.

1http://www.macbrain.org/resources.htm

Each set included group-level models that evaluated wholebrain activation, as well as seed-based functional connectivity. Although full factorial models were planned and executed, we focused on contrasts that included clinical improvement, given our interest in understanding that association between clinical improvement and neural processes. As such, direct comparisons of tasks were conducted if results by task yielded findings dependent on level of clinical improvement.

## Neuroimaging Acquisition

fnbeh-13-00177 August 22, 2019 Time: 18:18 # 6

Functional and anatomical brain images were acquired using a 3T General Electric MRI scanner with a 32-channel head coil, with multiband procedures to increase spatial and temporal resolution and thus better infer correlates of clinical improvement. Task stimuli were projected onto a screen at the foot of the fMRI bed and seen by the participant via a mirror attached to the head coil. Participants responded to displayed stimuli using their dominant hand to manipulate a 2-button response box. T2 blood oxygen level dependent (BOLD) images were acquired across 3 runs as 104 interleaved sagittal slices approximately parallel to the AC-PC line, with whole-brain coverage using a 3D multiband EPI pulse sequence [matrix size = 104 × 104 × 60 accelerated by a factor of 6, TR = 800 ms, TE = 29 ms, flip angle = 52◦ , FOV = 20.8 mm, voxel size = 2 mm × 2 mm × 2 mm, 556 (Face Task) or 370 (Piñata Task) image volumes per run]. Highresolution anatomical images with prospective motion correction (T2-weighted MPRAGE PROMO) were acquired for anatomical localization and spatial normalization (256 1.0 mm sagittal slices, flip angle = 8◦ , matrix size = 256 × 256, FOV = 25.6 mm, voxel size = 1 mm × 1 mm × 1 mm). The acquisition protocol was not optimized to capture cerebellar signal, so clusters identified within the cerebellum will not be discussed in detail.

## fMRI Data Preprocessing

Preprocessing protocols were implemented using Analysis of Functional NeuroImages (AFNI2 ). Preprocessing steps included functional image realignment, slice-time correction, spatial smoothing of 4 mm, and non-linear registration for spatial standardization to the Talairach template (Talairach and Tournoux, 1988). Image volume pairs with frame-wise displacement >1 mm were censored from individual level analysis. Task runs with censoring of ≥35% of image volumes were excluded from analyses (Face Task: 1 run of 1 participant). All participants evidenced mean frame-wise displacement (head motion) ≤0.30 mm.

## Data Analysis

### Activation

For the Face Task, probing social reward, regressors of interest in the individual-level general linear model included face emotion (happy, sad, angry, neutral) and probe location (congruent, incongruent), convolved with the BLOCK function. Beta images represented estimated activation during each of the conditions.

For the Piñata Task, probing non-social reward, two individual-level general linear models were run to generate

2https://afni.nimh.nih.gov/afni/

estimates of brain activation during anticipation and feedback periods, separately. The regressor of interest during the anticipation period included Reward Condition (no reward, reward). Reward Condition was convolved with AFNI's "dmBLOCK" basis function over the variable duration. Regressors of interest during the feedback period included Reward Condition and Performance (hit, miss). Both were convolved with the "BLOCK" function over 1500 ms. Analyses generated beta coefficients at each voxel for reward and no reward trials during the anticipation period, as well as for reward/hit, reward/miss, no reward/hit, and no reward/miss trials during the feedback period.

For both the social reward and non-social reward tasks, models included head motion in x, y, z, roll, pitch, yaw directions and third-degree polynomials to model low-frequency drift as nuisance regressors.

### Connectivity

Generalized psychophysiological interaction analysis (gPPI; McLaren et al., 2012) was utilized to calculate functional connectivity during the Face Task and the feedback period of the Piñata Task, given prior work that found connectivity results in the feedback period (Dougherty et al., 2018). gPPI calculates change in correlations between a seed region of interest and all other brain regions in each condition compared to implicit baseline. gPPI is advantageous as it allows for the evaluation of more than two task conditions in a single model. Given past work on reward tasks (Helfinstein et al., 2013; Dougherty et al., 2014; Wiggins et al., 2017) and prior fMRI work on anxiety (e.g., Blackford and Pine, 2012) and depression (e.g., Kerestes et al., 2014), bilateral amygdalae and ventral striatum (nucleus accumbens) were utilized as seeds for gPPI analyses. Seed regions were identified using the Talairach atlas in AFNI (Talairach and Tournoux, 1988; left amygdala = 1288 mm3 ; right amygdala = 1280 mm3 ; left ventral striatum = 136 mm3 ; right ventral striatum = 168 mm3 ). This analysis generated a set of voxel-wise images that represent connectivity between the seed region and the rest of the brain per condition for each task. Variance associated with head motion in x, y, z, roll, pitch, yaw directions was removed and third-degree polynomials were included to remove low-frequency drift.

#### Second Level Analyses

We conducted whole-brain, group-level ANCOVAs, by task, via AFNI's 3dMVM program to evaluate the association between clinical improvement (CGI-I) and reward-related brain function (activation, connectivity). Clinical improvement was included as a dimensional, between-subjects variable; task conditions were included as within-subjects categorical variables. Interactions between clinical improvement and task condition (Face Task: Clinical Improvement × Face Emotion, Clinical Improvement × Face Emotion × Probe Location; Piñata Task: Clinical Improvement × Reward Condition, Clinical Improvement × Performance, Clinical Improvement × Reward Condition × Performance) indicate the impact of task condition(s) on the relationship between clinical improvement and brain function.

Due to the small sample size, 3-way interactions were considered exploratory and interpreted with caution. All results were corrected for multiple comparisons, with a wholebrain corrected threshold of p < 0.05. The cluster threshold was calculated by 3dClustsim using the mixed-model spatial autocorrelation function (-acf) and the NN1 2-sided option, per the most recent recommendations on cluster correction (Cox et al., 2017). 3dClustsim used a group mask representing brain regions where 90% of participants had valid data. Spatial autocorrelation parameters were calculated by 3dFWHMx for each run separately, averaged over runs for each participant, and then averaged across participants. The cluster extent threshold across all models was k ≥ 56 voxels with a conservative height threshold of p < 0.005, which is appropriate for eventrelated designs (Cox et al., 2017). To decompose significant interactions, post hoc analyses were performed on values that were extracted and averaged from each cluster using SPSS; z-scores represented the test of the difference between two dependent correlations with one variable in common (Steiger, 1980; Lee and Preacher, 2013). Post hoc correlations were conducted for illustrative purposes to clearly depict the direction of effects.

#### Additional Analyses

Activation/connectivity values were extracted from clusters representing the main results and averaged for Additional Analyses. Regression analyses evaluated the effects of residual head motion, age, gender, concurrent anxiety, concurrent depression, length of time since post-treatment assessment, and task order on identified findings.

## RESULTS

## Behavioral Results

On the Face Task, overall mean accuracy (M = 93.58%, SD = 6.35) was well above chance (50%). Attention bias was calculated by subtracting reaction time between congruent and incongruent trials within face emotion condition. One-sample t-tests revealed no significant attention bias toward happy [t(14) = 0.84, p = 0.415], sad [t(14) = 0.51, p = 0.621], or threatening [t(14) = −1.35, p = 0.199] faces. No significant associations were found between clinical improvement and task accuracy (r = 0.29, p = 0.299) or bias scores (rhappy = −0.21, p = 0.453; rsad = 0.26, p = 0.354; rthreatening = −0.31, p = 0.257).

## fMRI Social Reward/Face Task

#### Activation

Within the context of the Face Task, there was a significant main effect of clinical improvement in the right middle frontal gyrus, such that across emotional faces (including social reward faces), decreased activation in this region related to a greater degree of clinical improvement (see **Figure 1A**). Additional significant activation clusters were evidenced in the cerebellum, as a function of degree of clinical improvement (see **Table 2**).

TABLE 2 | Significant clusters resulting from whole-brain analyses evaluating the association between clinical improvement and neural reactivity within the context of social reward (N = 15).

Contrasts that did not yield significant clusters are not listed in this table. ∗ indicates a contrast of interest; + indicates clusters from which values were extracted and presented in Figures 1, 2. BA, brodmann area; CGI-I, Clinical Global Impressions, Improvement Scale (Guy, 1976).

#### Connectivity

There was a main effect of clinical improvement on right and left ventral striatum connectivity with multiple posterior regions in the Face Task, including lingual gyrus, precuneus, angular gyrus, as well as middle frontal gyrus, inferior parietal lobule, and dorsal striatum (see **Figure 2**). Across all clusters, clinical improvement was associated with less connectivity between the ventral striatum and posterior regions, with the exception of the left ventral striatum to left dorsal striatum connectivity. Within that cluster, clinical improvement was associated with increased connectivity (see **Figure 2A**). Additionally, clusters representing right amygdala connectivity

with cerebellum were significant for Clinical Improvement × Face Emotion (see **Table 2**). Furthermore, the Clinical Improvement × Face Emotion interaction yielded significant left amygdala and post-central gyrus connectivity (see **Table 2**); however, post hoc analyses revealed that this cluster does not survive post hoc evaluation for potential confounding variables and may be outlier-driven.

#### **Exploratory connectivity analysis**

dataset.

An exploratory Clinical Improvement × Face Emotion × Probe Location interaction in the social reward task revealed significant connectivity between the left ventral striatum and medial frontal gyrus. Post hoc analyses indicated that the ventral striatum-medial frontal connectivity cluster was driven by differences in response to happy faces (i.e., the social reward stimulus). Heightened clinical improvement was associated with greater connectivity during happy/congruent trials; however, less connectivity was observed during happy/incongruent trials (see **Table 3** and **Figure 3A**). Similarly, using the right amygdala as the seed-region of interest, the 3-way interaction evidenced significant connectivity between the right amygdala and multiple temporal and frontal clusters, including bilateral temporo-parietal junction, insula, and prefrontal cortex. Specifically, the magnitude of right amygdala connectivity with these temporal and frontal regions differed as a function of degree of clinical improvement and depended on face

TABLE 3 | Significant clusters resulting from exploratory 3-way interactions evaluating the association between clinical improvement and neural reactivity within the context of social reward (N = 15).

Contrasts that did not yield significant clusters are not listed in this table. ∗∗ indicates findings not primarily driven by brain responses to happy faces; + indicates clusters from which values were extracted and presented in Figure 3. BA, brodmann area; CGI-I, Clinical Global Impressions, Improvement Scale (Guy, 1976).

emotion type, as well as congruency. Like findings in the ventral striatum, these interactions were driven by social reward. Participants evidencing less clinical improvement in response to BBT exhibited greater connectivity during happy/congruent trials compared to happy/incongruent trials. In contrast, participants with the most clinical improvement evidenced the opposite pattern: greater connectivity during happy/incongruent trials compared to happy/congruent trials. Post hoc analyses supported that correlations between clinical improvement and amygdala connectivity for happy/congruent vs. happy/incongruent trials differed significantly in all clusters (see **Figure 3B**).

Of note, there were additional significant clusters for the Clinical Improvement × Probe Location (see **Table 2**) and the Clinical Improvement × Face Emotion × Probe Location (see **Table 3**) contrasts, but these interactions were not driven by social reward faces.

## fMRI Non-social Reward/Piñata Task Activation

Within the context of the Piñata Task, during the anticipation period, increased clinical improvement related to decreased activation in the superior parietal lobule across reward and no reward conditions (see **Figure 1B**). During the feedback period of the Piñata Task, activation significantly varied as a function of task conditions; however, contrasts that included clinical improvement were not statistically significant. Additional significant activation clusters are reported in **Table 4**.

### Connectivity

Differences in connectivity as a function of degree of clinical improvement were evident during the Feedback period of the Piñata task. When the ventral striatum was used as the seed region of interest, left and right ventral striatum connectivity analyses yielded significant clusters in multiple medial prefrontal and parietal regions for the Clinical Improvement × Reward Condition interaction during the Feedback period (see **Table 4** and **Figures 4A,B**). Across all of these clusters, greater clinical improvement was associated with less connectivity during reward conditions yet greater connectivity during the no reward conditions. In addition, greater connectivity between the right ventral striatum and right middle frontal gyrus was observed across reward conditions (see **Figure 4B**). The Clinical Improvement x Reward Condition interaction also yielded significant clusters reflecting connectivity between the left amygdala and right middle occipital gyrus and between the right amygdala and the left precentral gyrus (see **Figures 4C,D**). Post hoc analyses within these clusters supported that greater clinical improvement was associated with less connectivity between the left amygdala and right middle occipital gyrus during reward conditions yet greater connectivity between these regions during the no reward conditions across clusters. However, the connectivity patterns between the right amygdala and left precentral gyrus evidenced the opposite pattern of findings.

## **Exploratory connectivity analysis**

An exploratory Clinical Improvement x Reward Condition x Performance interaction during the feedback period of the Piñata Task revealed significant left and right amygdala connectivity with multiple temporal and parietal regions, including temporalparietal junction (see **Table 5**). In these regions, increased clinical improvement was associated with greater amygdala connectivity when participants either received a reward for hitting the target (i.e., reward/hit condition) or missed the target when no reward was expected (i.e., no reward/miss condition). In contrast, increased clinical improvement was associated with lower levels of amygdala connectivity when participants either hit the target but did not receive a reward (i.e., no reward/hit condition) or missed the target when a reward was expected (i.e., reward/miss condition). Post hoc analyses indicated that the relationship between clinical improvement and brain activation in reward vs. no reward conditions differed significantly for both hit and miss trials, across all clusters (see **Figure 5**).

FIGURE 3 | Exploratory higher level interactive effects of clinical improvement on ventral striatum and amygdala connectivity within the context of social reward. (A) Left ventral striatum (VS) connectivity; (B) Right amygdala connectivity. N = 15. Brain images represent axial sections (left = left) with threshold set at whole-brain FDR-corrected p < 0.05. Scatterplots of the significant connectivity effects for the indicated clusters are displayed for illustrative purposes to depict the direction of association; patterns are similar for other clusters within each contrast. CGI-I (i.e., clinical improvement; Guy, 1976) anchors are displayed inversely to improve readability and restricted to reflect the range observed within this dataset. L, left; MFG, middle frontal gyrus; R, right; TPJ, temporo-parietal junction.

TABLE 4 | Significant clusters resulting from whole-brain analyses evaluating the association between clinical improvement and neural reactivity within the context of non-social reward (N = 14).

Contrasts that did not yield significant clusters are not listed in this table. ∗ indicates a contrast of interest; + indicates clusters from which values were extracted and presented in Figures 1, 4. BA, brodmann area; CGI-I, Clinical Global Impressions, Improvement Scale (Guy, 1976).

FIGURE 4 | Effects of clinical improvement on ventral striatum and amygdala connectivity when receiving feedback within the context of non-social reward. (A) Left ventral striatum (VS) connectivity; (B) Right ventral striatum connectivity; (C) Left amygdala connectivity; (D) Right amygdala connectivity. N = 14. Brain images represent axial sections (left = left) with threshold set at whole-brain FDR-corrected p < 0.05. Scatterplots of the significant connectivity effects for the indicated clusters are displayed for illustrative purposes to depict the direction of association; patterns are similar for other clusters within each contrast. CGI-I (i.e., clinical improvement; Guy, 1976) anchors are displayed inversely to improve readability and restricted to reflect the range observed within this dataset. ACC, anterior cingulate cortex; L, left; MFG, middle frontal gyrus; MOG, middle occipital gyrus, R, right; SPL, superior parietal lobule; TPJ, temporo-parietal junction.

TABLE 5 | Significant clusters resulting from exploratory 3-way interactions evaluating the association between clinical improvement and neural reactivity within the context of non-social reward (N = 14).

Contrasts that did not yield significant clusters are not listed in this table. a indicates a cluster that failed to maintain significance when controlling for parent-rated youth anxiety, irritability, or depression. + indicates clusters from which values were extracted and presented in Figure 5. BA, brodmann area; CGI-I, Clinical Global Impressions, Improvement Scale (Guy, 1976).

## Comparison of Findings by Task Type

Our findings implicated connectivity differences during happyneutral/congruent vs. happy-neutral/incongruent conditions in the Face Task and during reward/hit vs. reward/miss conditions in the Piñata Task. As such, we conducted an exploratory whole-brain ANCOVA to directly compare connectivity within the context of social vs. non-social reward, in relation to clinical improvement as a function of treatment. Contrast images, representing happy/congruent vs. happy/incongruent and reward/hit vs. reward/miss connectivity for left and right amygdalae as well as left and right ventral striata, were calculated and entered into separate 3dMVM models. Task type (social reward, non-social reward) was included as a categorical, withinsubjects variable and clinical improvement was maintained as a dimensional, between-subjects variable.

#### Activation

We were unable to statistically evaluate differences in wholebrain activation across tasks due to a lack of findings dependent on clinical improvement scores during the feedback period of the Piñata Task.

#### Connectivity

Clinical Improvement × Task Type (i.e., social reward, nonsocial reward) did not yield significant clusters indicative of

non-social reward. N = 14. Brain images represent axial sections (left = left) with threshold set at whole-brain FDR-corrected p < 0.05. Scatterplots of the significant connectivity effects for the indicated clusters are displayed for illustrative purposes to depict the direction of association; patterns are similar for other clusters within each contrast. CGI-I (i.e., clinical improvement; Guy, 1976) anchors are displayed inversely to improve readability and restricted to reflect the range observed within this dataset. R, right; TPJ, temporo-parietal junction.

connectivity between bilateral amygdalae or ventral striata and other regions in the brain.

## Additional Analyses

Additional analyses were run to evaluate the impact of potential confounding factors on the reported results. Hypothesized confounds utilized in these analyses included residual head motion, age, gender, concurrent anxiety, concurrent depression, length of time since post-treatment assessment, and task order. All main results maintained significance after the inclusion of these factors in statistical models.

## DISCUSSION

The current study aimed to add to the sparse literature on reward processing in pediatric anxiety and depression. Of note, the focus of this work was to evaluate neural correlates of a successful unified protocol, rather than to determine exclusive and common correlates of depression and anxiety in youth. Future research can build on the results of our work to distinguish neural correlates in these highly comorbid conditions. This investigation is unique in its focus on social as well as nonsocial reward tasks, use of a clinically impaired service-seeking sample, and effort to probe associations between reward and clinical improvement in a treated sample. Indeed, this study is the only study to investigate reward processing in a treatment context within a sample of anxious youth and one of only four to do so in pediatric depression. Moreover, this was the only study to investigate multiple tasks (i.e., social and non-social) within the same individuals, enabling the evaluation of task context effects. The study design was exploratory and hypothesis-generating in nature; indeed, findings should be interpreted cautiously due to the small sample size. Perhaps the most important message

from this work is that evaluating the relationship between clinical improvement and reward processing neural circuitry, particularly within the context of transdiagnostic samples and treatment paradigms, appears promising for further investigation. Our findings indicate that integrating behavioral neuroscience tools into clinical science can provide a complementary way to "look under the hood" of treatments, and thus inform the generation of more targeted, mechanistically based treatments. Broadly, we found that social and non-social reward paradigms triggered patterns of connectivity in frontal and striatal regions associated with social cognition and reward, and these patterns reliably differed by level of clinical improvement obtained over the course of treatment. Patterns were consistent with the literature at large and suggest value in further efforts to probe shifts in reward processing as a mechanism of treatment-related changes in internalizing pathology in youth.

Findings further illustrate how data from multiple withinsubject tasks can be incorporated in a single study and underscore the importance of task context for interpreting differences in brain function. Although both reward and emotion regulation networks were implicated in both the social and non-social reward tasks, the specific regions as well as the direction of differences (e.g., greater vs. less activation/connectivity) depended on task context and conditions. Thus, when summarizing the literature, it may not be enough to state that observed differences in activation or connectivity of a particular region is associated with a disease state, or that reducing or increasing brain function in those regions is the mechanism by which clinical improvement occurs. Rather, our study which compares and contrasts results from two tasks within the same participants suggests that differences in activation or connectivity appear to be highly dependent on task context. More direct comparisons of neural responses to social and non-social feedback, particularly in larger samples, are needed in future work, as findings have implications for the future of clinical care and evaluation of treatment mechanisms.

Although the specific patterns of results differed by task, clinical improvement was associated with alterations in regions involved in emotion regulation, in addition to reward, across both tasks. Observed differences as a function of degree of clinical improvement could reflect "normalization" of circuitry or compensatory mechanisms. This finding has intriguing implications for understanding the integration of emotion regulation and reward processing in treatment. As stated above, both anxious and depressed samples of youth have evidenced aberrant patterns of reward processing and emotion regulation, compared to healthy peers. The guided behavioral activation in concert with targeted skill building included in BBT may have interacted more effectively with reward circuitry compared to the more varied care received by those in ARC. Thus, successful treatment of internalizing pathology may require integration of emotion regulation and reward processing regions, such that youth are able to learn to modulate their emotions – which may in itself be rewarding. Improved emotion regulation skills and exposure in this treatment may have been sufficient to alter both reward and emotion regulation circuitry without an overt cognitive component.

Of note, significant contrasts in the Face Task were driven by the social reward stimulus (i.e., happy faces) in relation to clinical improvement. This is particularly remarkable given that prior work documented that across the lifespan, faces as social reward have been shown to be less impactful in motivating behavior change than monetary incentives (Kohls et al., 2009). Furthermore, the employed tasks were not perfectly parallel in terms of their assessment of social vs. non-social reward. The Face Task paradigm involved the passive viewing of rewarding faces, in contrast to the Piñata Task's dependence on participant action to trigger the reward. Nevertheless, stronger connectivity between the amygdala and multiple frontal and temporal areas implicated in rewardbased learning, social prediction error, and reappraisal of emotions triggered by social situations (e.g., Grecucci et al., 2013); but, weaker coupling between the ventral striatum and medial frontal gyrus, another area implicated in social cognition (Amodio and Frith, 2006) was observed when the Face Task probe was located under the neutral rather than happy face (i.e., incongruent trial). BBT's focus on exposure may improve anxiety/depression symptoms by "incentivizing" engagement in adaptive activities, including social interactions; that is, repeated exposure and associated habituation may help participants re-define activities deemed dangerous as inherently rewarding, through the practice of approach behaviors and emotion regulation skills (i.e., a behavioral activation model). In addition, increased clinical improvement was associated with decreased activation in areas associated with re-orienting attention (Japee et al., 2015) and emotion reappraisal (Grecucci et al., 2013). This may moreover suggest that those who improved as a function of BBT may be less prone to distraction by internal processes (e.g., rumination, worry) and more able to maintain focus on their environment, perhaps supporting increased approach behaviors. Whereas we have taken this initial step of comparing tasks, future research can add to the literature base describing these relations using more parallel social vs. non-social task paradigms.

Within pediatric anxiety (e.g., Guyer et al., 2008) and depression (e.g., Silk et al., 2012), social reward processing has been a research target due to the characteristic fears of social evaluation by peers in internalizing youth. Evidence has suggested that youth exhibit a negative bias during social interactions, interpreting peer behavior as overly critical and expecting interactions to be negative in support of their misappraisal. Our findings with social reward stimuli (i.e., happy faces) are consistent with the idea that clinical improvement may occur through amelioration of this negative bias and greater valuation of social reward. Nevertheless, faces are relatively passive stimuli to assess reward processing, and the faces utilized by this task reflect adults, rather than same-aged peers. Moving forward, studies that build on our findings to further investigate social reward processing as a mechanism of treatment response may wish to consider more interactive tasks (e.g., Chatroom Task, Guyer et al., 2008; Virtual School, Jarcho et al., 2013) that include age-appropriate faces and peer evaluation as feedback, as these may be more ecologically valid for adolescents.

Within the context of non-social reward, differential coupling of the affective and cognitive control networks by condition was apparent. Increased clinical improvement was associated with increased connectivity between the amygdala and areas associated with cognitive control and decision making after positive or neutral experiences (e.g., obtaining a reward, missing when no reward was promised). In contrast, the same regions evidenced decreased connectivity in response to frustrating experiences (e.g., hitting the target when no reward was promised, missing the target when a reward was available). This may suggest more effective recruitment of emotion regulation strategies (e.g., decreased influence of emotional responses in decision making after aversive events) so as to maintain adaptive behavior. Interestingly, the areas implicated in nonsocial reward connectivity analyses also have been shown to be part of social cognition circuitry. As similar regions emerged across tasks, this serves as support that both tasks probe reward-related processes, which may in turn underlie clinical improvement.

Several limitations warrant consideration. First, although our sample size (N = 15) is comparable to that of the few other pediatric studies probing associations between neural mechanisms of reward processing and treatment response (e.g., n = 10, Straub et al., 2015; n = 13, Forbes et al., 2010; n = 15, Mori et al., 2016), our sample size was modest and represents a fraction of the participants who originally participated in the clinical trial (similar to comparable studies as well). Thus, our power was limited and our findings may not generalize to the broader population of youth. Replicating findings within a larger sample could strengthen interpretations and power more complex statistical models. Furthermore, the lack of interest in re-engaging in research suggests potential for sampling bias, as there may be differences between those who completed their scans and those who refused on dimensions not measured by the current battery. Nevertheless, given the paucity of studies characterizing neural treatment mechanisms, and as the only study to include multiple tasks within youth, this study serves as a proof-of-concept for future work.

Second, scanning occurred post-treatment, which limits our ability to determine whether the neural profiles identified are present prior to or as a consequence of clinical improvement. It is possible that our findings reflect that clinical improvement promoted the observed patterns of brain reactivity, suggesting that brain patterns were in fact outcomes of response. However, it is also possible that the observed brain patterns were pre-existing and therefore predictors of treatment outcome. A third option is that clinical improvement and observed brain patterns were both related to a third, unmeasured variable. Additionally, substantial time passed between treatment completion and the scan. Although the additional analyses suggested that our findings primarily reflected neither the amount of time passed nor concurrent symptoms, brain patterns may have nonetheless been influenced by individual, unmeasured characteristics. Moreover, the lack of a comparison group complicates the interpretation of observed patterns of findings, particularly as they relate to what we might expect from healthy youth. Thus, our findings are correlational and speak to neural reactivity in response to intervention. Replication in a study designed to include pre-treatment, post-treatment and follow-up fMRI scans could allow for causal inferences to be made. Such a design would also allow for the maintenance of randomization, to better understand the impact of specific treatment paradigms on changes in reward-related circuitry, and vice versa. Future trials that incorporate imaging at multiple time points across multiple treatment arms, including a control condition can use these findings to generate hypotheses to establish directionality of change.

Change in clinical presentation after the receipt of an intervention has implications for participants' abilities to learn skills within a therapeutic context, and suggests support for treatment match. Findings may also suggest that BBT (and potentially behavioral interventions more broadly) may capitalize on the integration of emotion regulation and social/non-social reward processing networks to promote behavioral activation. Taken together, conclusions should be viewed as preliminary data aimed at hypothesis generation for future work. Ultimately, incorporating behavioral neuroscience tools into clinical science will improve treatment outcomes, as identifying predictors and mechanisms of treatment response is crucial groundwork to move toward a precision medicine approach, including mechanism-based treatment to the individuals whose neural profiles indicate they would benefit the most. This work offers evidence of value in comparing complex data from multiple task contexts and contributes to the establishment of a literature base of neural mechanisms of treatment response in internalizing youth.

## DATA AVAILABILITY

The datasets for this study will not be made publicly available because the data are undergoing secondary analyses in preparation for additional publications.

## AUTHOR CONTRIBUTIONS

KS, VW, and JW were jointly involved in the study conceptualization and protocol development. KS spearheaded the recruitment for the neuroimaging follow-up, data collection, data preparation, and writing of the manuscript. MK-L led the analytical efforts with ML's assistance. VW contributed the data from the original RCT and approved the use of the sample for the neuroimaging follow-up. JW provided training to facilitate the study completion, supervised the data collection and analytical efforts, and contributed to the writing of the manuscript. All authors critically reviewed the manuscript and approved it prior to submission.

## FUNDING

The original randomized controlled trial described was supported by the National Institute of Mental Health (Grant Numbers 5R01MH084935 to VW and 5R01MH084916 to Dr. David A. Brent). The neuroimaging follow-up component was supported by the National Institutes of Health UL1TR001442 Clinical and Translational Research Institute Pilot Grant and NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation, both awarded to JW.

## ACKNOWLEDGMENTS

fnbeh-13-00177 August 22, 2019 Time: 18:18 # 17

We would like to thank the families who participated in this study, without whom this project would not have been possible. We would also like to thank Jill Weisberg, Ph.D., for her guidance on analytical techniques and general support of data management and processing. Many thanks to the personnel at UC San Diego's Center for Magnetic Resonance Imaging, particularly Aaron Jacobson, M.A., for their assistance in fMRI training and

## REFERENCES

efforts to ensure high-quality data collection. We are grateful to Rick Reynolds, M.S., and Gang Chen, Ph.D., of the Scientific and Statistical Computing Core of the National Institute of Mental Health for their assistance with the data analyses in AFNI. Finally, we would like to thank the research assistants in the Translational Emotion Neuroscience and Development Laboratory (TEND Lab), particularly Carly Farrington, Ysabella Panggat, and Geneva Toledo, for their assistance in the data collection and management, and David A. Brent, M.D., for his role as the BBT principal investigator for the Pittsburgh site.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnbeh. 2019.00177/full#supplementary-material

pediatric anxiety disorders. Am. J. Psychiatry 169, 205–212. doi: 10.1176/appi. ajp.2011.11010006

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Schwartz, Kryza-Lacombe, Liuzzi, Weersing and Wiggins. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Time-Frequency Delta Activity to Social Feedback Demonstrates Differential Associations With Depression and Social Anxiety Symptoms

Jingwen Jin1 \*, Amri Sabharwal 1 , Zachary P. Infantolino1 , Johanna M. Jarcho2 and Brady D. Nelson1

1Department of Psychology, Stony Brook University, Stony Brook, NY, United States, 2Department of Psychology, Temple University, Philadelphia, PA, United States

#### Edited by:

Bahar Güntekin, Istanbul Medipol University, Turkey

#### Reviewed by:

Birgit Mathes, University of Bremen, Germany Dan Foti, Purdue University, United States

> \*Correspondence: Jingwen Jin<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Motivation and Reward, a section of the journal Frontiers in Behavioral Neuroscience

> Received: 11 April 2019 Accepted: 06 August 2019 Published: 27 August 2019

#### Citation:

Jin J, Sabharwal A, Infantolino ZP, Jarcho JM and Nelson BD (2019) Time-Frequency Delta Activity to Social Feedback Demonstrates Differential Associations With Depression and Social Anxiety Symptoms. Front. Behav. Neurosci. 13:189. doi: 10.3389/fnbeh.2019.00189 Social feedback is highly salient and particularly relevant when investigating the pathophysiology of depression and social anxiety. A bourgeoning body of research has demonstrated an association between reward-related delta activity and psychopathology. However, a critical limitation is that these findings are derived from neural responses to monetary feedback, and time-frequency representation of social feedback remains unexplored. In addition, no study has isolated the differential/unique associations of positive valence and the intrinsic rewarding experience of being correct with reward-related neural activity. In the present study, 204 participants underwent electroencephalography (EEG) while they completed a novel paradigm comprised of monetary and social feedback tasks that were matched in trial structure, timing, and feedback stimuli. For each task, participants were instructed to correctly identify one of two doors that would provide positive feedback (monetary win behind the door) or one of two peers who would provide positive feedback (social like); or to correctly identify the door or peer that would provide negative feedback (money loss behind the door/social dislike). A principal component analysis (PCA) was conducted on the time-frequency data and revealed two factors in the delta and one factor in the theta frequency ranges. Results indicated that the lower-frequency delta factor (delta-low) was greater to correct vs. incorrect feedback, more so for social vs. monetary tasks, while the higher-frequency delta factor (delta-high) was greater to correct vs. incorrect feedback for social like, social dislike, and monetary win tasks, but not the monetary loss task. In contrast, the theta factor was greater to incorrect relative to correct feedback in negative valence (lose money/social dislike) but not positive valence (win money/social like) tasks. Furthermore, greater delta-high activity for social feedback was associated with greater social anxiety symptoms, whereas lesser delta-high activity for social feedback was associated with greater depressive symptoms. Finally, greater theta activity to monetary feedback was associated with greater depressive symptoms. The present study provides novel evidence demonstrating unique social vs. monetary feedbackrelated delta and theta activity, and differential associations between delta activity with depression and social anxiety symptoms. These findings highlight the importance of investigating feedback-related neural responses in the social domain.

Keywords: time-frequency, delta, theta, social feedback processing, social anxiety, depression

## INTRODUCTION

Understanding how humans process salient feedback is central not only to economic theories of monetary decision-making (Bernoulli, 1954; Tversky and Kahneman, 1992; Von Neumann and Morgenstern, 2007) but also to theories of social decisionmaking (Homans, 1958; Sanfey, 2007). Adaptive behavioral changes often rely on successful processing of outcome feedback (Everitt and Robbins, 2005; Wrase et al., 2007). Failure to use feedback to flexibly update decision-making strategies has been linked to various mental disorders, such as depression (Cella et al., 2010) and anxiety disorders (Hartley and Phelps, 2012; Phelps et al., 2014). While responses to positive and negative feedback in monetary (win and loss) and social (being accepted/liked and rejected/disliked) domains are both important in daily life, it remains unclear whether the two domains share the same or have unique neural mechanisms. It is also unknown whether neural responses to monetary and social feedback demonstrate common or differential relationships with depression and anxiety symptoms.

Neuroimaging studies comparing monetary and social feedback have demonstrated that the two domains share overlapping neural circuitry, including the striatum and prefrontal regions (Izuma et al., 2008; Lin et al., 2012; Hausler et al., 2015). On the other hand, accumulating evidence also suggests that monetary and social feedback elicit unique neural responses (Rademacher et al., 2010; Chan et al., 2016). For example, using the monetary incentive delay task, one study found that while monetary reward was associated with thalamic activity, social reward was associated with amygdala activity (Rademacher et al., 2010).

Parallel to these neuroimaging studies, event-related potential (ERP) research has identified the reward positivity (RewP), a positive-going component that peaks approximately 250–350 ms following monetary win feedback that is absent or reduced to monetary loss feedback (Holroyd et al., 2008; Foti et al., 2011; Novak and Foti, 2015; Proudfit, 2015). The RewP has been primarily examined in the context of monetary feedback, but more recent studies have shown that it can also be elicited by positive social feedback (e.g., social acceptance; Kujawa et al., 2014; van der Veen et al., 2016). When directly compared within the same participants, one study found a higher RewP to monetary vs. social reward in emerging adults but not in early adolescents, and the monetary and social RewPs were not correlated across the two groups (Ethridge et al., 2017). However, the social paradigm used in this study had important differences compared with the monetary task. For instance, the paradigm required the participants to decide on accepting or rejecting simulated co-players before receiving acceptance/rejection feedback from the same co-players. Additionally, there were timing differences in trial structure (e.g., the social task contained an additional variable delay between making a choice and receiving feedback). A more recent study that matched the designs of the monetary and social tasks found that the RewP to monetary and social feedback was of comparable magnitude and positively correlated, although only the RewP to social feedback was associated with depressive symptoms (Distefano et al., 2018). Taken together, the current literature suggests that when the paradigms are closely matched, monetary and social feedback may elicit overlapping neural responses. On the other hand, feedback from the two domains may exhibit unique and potentially dissociable relationships with particular forms of psychopathology, such as depression and social anxiety.

In addition to monetary and social paradigm differences, the task designs of previous studies often confounded the positive valence of outcome (i.e., monetary win and being socially accepted) with the intrinsic reward of being correct (i.e., the chosen option yielding win/acceptance feedback). One exception was a recent investigation that examined time-frequency indices in response to monetary win vs. loss and correct vs. incorrect feedback (Bernat et al., 2015). In this study, delta activity was higher both for positive valence (i.e., win) compared to negative valence (i.e., loss) and being correct compared to incorrect, and theta activity was higher for negative valence compared to positive valence, but not incorrect vs. correct outcomes (Bernat et al., 2015). However, the correct/incorrect outcome was dependent on valence such that correct (in contrast to incorrect) indicated a larger win or smaller loss, and therefore was secondary to valence. In sum, no study has investigated time-frequency activity to these two dimensions simultaneously as primary feedback attributes.

Previous studies examining electrocortical responses to win and loss feedback have largely focused on time-domain ERPs. While this line of research has yielded largely consistent findings (Bernat et al., 2015; Proudfit, 2015), there are some notable limitations to this analytic approach. Time-domain ERPs consist of multiple temporally-overlapping components that are often characterized by different frequency profiles (Bernat et al., 2005; Dien, 2010b; Foti et al., 2015). Time-frequency based representation of the signal can help elucidate distinct neural processes that occur at different frequency bands (e.g., delta vs. theta activity) that are otherwise embedded in the time-domain data (Spencer et al., 2001). Furthermore, time-frequency analysis of single-trial data allows researchers to identify non-phase locked aspects of the neural response that might be attenuated or absent in the time-domain signal due to the common practice of trial averaging (Bernat et al., 2005; Cohen et al., 2007; Cohen, 2014). Multiple investigations that conducted time-frequency analysis have found that the neural response in the time range of the RewP shows greater delta activity to monetary win feedback and greater theta activity to monetary loss feedback (Bernat et al., 2011, 2015; Foti et al., 2015). However, no study has investigated the time-frequency activity in the context of social feedback and compared that activity to monetary feedback. Given the unique information time-frequency based representation can provide, it is important to examine whether the delta and theta activities are also present for social feedback.

Examining distinct time-frequency indices in response to monetary and social feedback may also help reveal any differential neural correlates of depression and anxiety. Theoretical and empirical research has suggested that depression is associated with a blunted neural response to monetary win and an enhanced neural response to monetary loss (Henriques and Davidson, 2000; Eshel and Roiser, 2010; Kujawa et al., 2014; Luking et al., 2016). In addition, a blunted RewP to monetary win (compared to loss) has been shown to prospectively predict depressive symptoms and syndromes (Bress et al., 2013; Nelson et al., 2016). Two recent studies using social reward tasks also demonstrated a blunted RewP in association with depression (Kujawa et al., 2014; Distefano et al., 2018). To date, only a small number of studies have examined time-frequency neural activity to monetary feedback in relation to depression. One study found that blunted delta activity to monetary reward was associated with greater depression, anxiety, and stress (Foti et al., 2015). Conversely, a separate study of adolescent girls found that depression was associated with higher loss-related theta activity, but there were no group differences in reward-related delta activity (Webb et al., 2017). Finally, a recent investigation of adolescent girls found that blunted delta activity to monetary reward prospectively predicted first-onset depression, independent of the time-domain RewP (Nelson et al., 2018). Together, this nascent literature suggests that depression might be associated with an aberrant neural response in particular frequency bands. However, no study has examined time-frequency activity to social feedback in relation to depressive symptoms.

Even less is known about the relationship between the neural response to monetary and social feedback and social anxiety. One study using a child sample found that a greater RewP was associated with higher social anxiety symptoms even after controlling for depressive symptoms (Kessel et al., 2015). An important limitation of the current literature is that the neural response to feedback is often examined using monetary tasks, and there is a lack of research examining the ERP response to social feedback in relation to social anxiety. As the hallmark of social anxiety is the fear of social evaluation (American Psychiatric Association, 2013), it is possible that the association between the neural response to feedback and social anxiety is more sensitive to social compared to non-social information. However, no study has examined the time-frequency indices of neural response to social compared to monetary feedback in relation to social anxiety symptoms.

To address these issues, the current study utilized a novel paradigm that carefully matched the trial structure, timing, and visual presentation of feedback stimuli. This design permitted the comparison of participants' neural response to feedback indicating monetary win, monetary loss, social acceptance (i.e., being liked), and social rejection (i.e., being dislike). Furthermore, the tasks were designed to tease apart the effects of feedback domain (monetary vs. social), valence (positive vs. negative), and outcome (correct vs. incorrect). In a large sample of young adults, we employed time-frequency analysis to examine delta and theta activity to feedback across both monetary and social domains. In addition, we investigated relations between these time-frequency indices and individual differences in depression and social anxiety symptoms. We hypothesized that: (1) for both the monetary and social domains, there would be higher delta activity to positive and/or correct feedback and higher theta activity to negative and/or incorrect feedback; and (2) blunted delta activity to positive feedback (across both monetary and social domains) would be associated with more severe depressive symptoms. Due to the exploratory nature of the remaining analyses, we did not have other specific hypotheses for time-frequency activity to social feedback or social anxiety symptoms.

## MATERIALS AND METHODS

## Participants

Two hundred and five participants were recruited, with one excluded due to not completing the experiment. The final sample included 204 participants (M = 19.92 years old, SD = 2.50), who were 63.7% female, racially/ethnically diverse (45.1% Asians, 5.9% Black, 26.5% Caucasian, 10.8% Latino, and 11.8% ''Other''), and participated for course credit. All participants gave informed consent and the study was approved by the Stony Brook University Institutional Review Board.

## Measures

Participants completed the Inventory of Depression and Anxiety Symptoms—Expanded Version (IDAS-II; Watson et al., 2007, 2012). IDAS-II is a factor analytically-derived self-administered questionnaire that assesses symptomatology of mood and anxiety disorders in the past 2 weeks using a Likert scale ranging from 1 (not at all) to 5 (extremely). IDAS-II has demonstrated excellent psychometric properties across various populations, including college students, community, and patient samples (Watson et al., 2012). The current study focused on the 10-item dysphoria scale (M = 19.47, SD = 7.52, Cronbach's α = 0.88), which is the most discriminant symptom dimension of major depressive disorder, and the 6-item social anxiety scale (M = 10.95, SD = 5.22, Cronbach's α = 0.86).

## Stimuli

The social feedback task stimuli were identical to a previous investigation (Distefano et al., 2018) and consisted of 120 images of age-matched peers (60 females) compiled from multiple sources [e.g., National Institute of Mental Health's Child Emotional Faces picture set (Egger et al., 2011), internet databases of non-copyrighted images, and photographs of college-aged individuals]. Variability in the appearance of the social stimuli was necessary in order to corroborate task deception, which suggested participants were being evaluated by actual peers. All images were cropped to a standardized size (3.5" width × 4.5" height), and occupied approximately 8◦ of visual space horizontally and 10◦ vertically for participants seated approximately 24" from the monitor. Each trial slide contained a pair of either male peers or female peers (60 pairs of male faces and 60 pairs of female faces), pictured from their shoulders up, with a positive facial expression and a solid background.

## Procedure

At the beginning of the experimental session, participants were told that they would complete a social evaluation study with peers at different universities across the United States. Participants were asked to provide a digital photo of themselves that was purportedly uploaded to a study database. Participants believed that once this photograph was uploaded, peers would receive a text message on their cell phone asking them to view the photo and indicate whether they thought they would ''like'' or ''dislike'' the participant. Participants were told that later in the experimental session, after enough time had elapsed for the purported peers to have rated their photo, they would be asked to guess which peers ''liked'' and ''disliked'' them. Participants were also told that they would be completing monetary guessing tasks. Next, participants completed self-report questionnaires while an electroencephalography (EEG) cap was applied to their head. Finally, participants completed the monetary and social feedback tasks in a counterbalanced order.

### Monetary and Social Feedback Tasks

The monetary and social feedback tasks were administered using Presentation software (Neurobehavioral Systems, Inc., Albany, CA, USA) and were modified variants of previously established tasks (Proudfit, 2015; Distefano et al., 2018). Overall, there were four total tasks (monetary win, monetary loss, social like, and social dislike) that were presented in a counterbalanced order.

**Figure 1** displays the overall task schematic. In the monetary win task, each trial began with the presentation of two identical doors. Participants were told there were three possible scenarios for each trial: (1) both doors contained a \$0.25 monetary win; (2) one door contained a \$0.25 monetary win while the other door resulted in a break-even outcome (i.e., neither win nor lose); or (3) both doors resulted in a break-even outcome. These instructions ensured that the feedback the participant received would only be informative about the door they chose and not the door they did not choose. For example, if a participant chose a door and received feedback indicating a break-even result, the other door could have been a win door [consistent with trial scenario (2) above] or it could have been a break-even door [consistent with trial scenario (3) above]. Conversely, if a participant chose a door and received feedback indicating a win result, the other door could have been a win door [consistent with trial scenario (1) above] or it could have been a breakeven door [consistent with trial scenario (2) above]. Participants were told that the goal of these trials was to try and guess which door contained the monetary win. The image of the doors was presented until the participant made a selection. After stimulus offset, a fixation cross (+) was presented for 1,000 ms, and then feedback was presented on the screen for 2,000 ms. Correct selection of the monetary win door resulted in a \$0.25 monetary win, indicated by a green arrow pointing upward (↑). Incorrect selection of the break-even door resulted in no monetary win, indicated by a white horizontal dash (–). In actuality, feedback was pre-programmed to generate an equal number of win and break-even trials. The feedback stimulus was followed by a fixation cross presented for 1,500 ms, immediately followed by the message ''Click for next round.'' This prompt remained on the screen until the participant responded with a button press to initiate the next trial. The task consisted of 30 total trials (15 of each outcome).

In the monetary loss trials, trial structure and timing was identical to the monetary win trials, but participants were told there were three possible situations for each trial: (1) both doors contained a \$0.25 monetary loss; (2) one door contained a \$0.25 monetary loss while the other door resulted in a breakeven outcome (i.e., neither win nor lose); or (3) both doors resulted in a break-even outcome. Participants were told that the goal of these trials was to try and guess which door contained the monetary loss. Correct selection of the monetary loss door resulted in a \$0.25 monetary loss, indicated by a red arrow pointing downward (↓). Incorrect selection of the break-even door resulted in no monetary loss, indicated by a white horizontal dash (–). All participants were told that they would start with a pot of \$5. Given that there were equal number of wins and losses, they were paid \$5 at the end of the experiment.

The social like and dislike tasks were identical to the monetary win and loss tasks, respectively, except pictures of gendermatched peers (i.e., two male faces or two female faces) were presented instead of doors. There was an equal number of trials with male and female peers across the social like and social dislike tasks (30 each, 60 total). In the social like trials, participants were told that there were three possible situations for each trial: (1) both people said they would like the participant; (2) one person said they would like the participant while the other person never rated the participant; or (3) neither person rated the participant. Participants were told that the goal of these trials was to try and guess which person said they would like the participant. Correct selection of the person who said they would like the participant was indicated by a green arrow pointing upward (↑). Incorrect selection of the person who never rated the participant was indicated by a white horizontal dash (–).

In the social dislike trials, participants were told there were three possible situations for each trial: (1) both people said they would dislike the participant; (2) one person said they would dislike the participant while the other person never rated the participant; or (3) neither person rated the participant. Participants were told that the goal of these trials was to try and guess which person said they would dislike the participant. Correct selection of the person who said they would dislike the participant was indicated by a red arrow pointing downward (↓). Incorrect selection of the person who never rated the participant was indicated by a white horizontal dash (–). Participants took about 5–7 min for each task.

#### EEG Recording and Processing

Continuous EEG was recorded using an elastic cap with 34 electrode sites placed according to the 10/20 system. Electrooculogram (EOG) was recorded using four additional facial electrodes: two placed approximately 1 cm outside of the right and left eyes and two placed approximately 1 cm above and below the right eye. All electrodes were sintered Ag/AgCl electrodes. Data were recorded using the ActiveTwo system (BioSemi, Amsterdam, Netherlands). The EEG was digitized with a sampling rate of 1,024 Hz using a low-pass fifth-order sinc filter with a half-power cut-off of 204.8 Hz. A common mode sense active electrode producing a monopolar (non-differential) channel was used as recording reference.

Offline data processing was conducted using EEGLAB toolbox version 13.6.5b (Delorme and Makeig, 2004) and customized MATLAB scripts (The MathWorks, Inc., Natick, MA, USA). EEG data were first re-referenced to the average of the left and right mastoids, high-pass filtered (0.01 Hz) to remove baseline drift, and segmented into single-trial epochs (−3,000, +3,000 ms) around the feedback onset. Epochs containing artifacts were identified and rejected using Fully Automated Statistical Thresholding for EEG Artifact Rejection (Nolan et al., 2010). Consistent with published guidelines (Nolan et al., 2010), the decision to reject epochs was based on three parameters: the amplitude range of the epoch, the deviation between the epoch and the channel average, and the variance within the epoch. The parameters were converted to z-scores and epochs with an absolute z-score greater than three were identified and rejected. Eye blinks artifacts were then removed using independent component analyses. The number of trials went into the time-frequency analyses for each condition were: M = 14.77 (SD = 0.77) for monetary loss correct, M = 14.78 (SD = 0.92) for monetary loss incorrect, M = 14.82 (SD = 0.77) for monetary win correct, M = 14.86 (SD = 0.52) for monetary win incorrect, M = 14.80 (SD = 0.88) for social dislike correct, M = 14.72 (SD = 1.05) for social dislike incorrect, M = 14.73 (SD = 1.09) for social like correct, M = 14.79 (SD = 0.97) for social like incorrect.

In order to retain phase and non-phase locked neural responses (Cohen, 2014; Luck, 2014), single-trial epochs for each electrode were then decomposed into their time-frequency representation using Morlet wavelets. Specifically, the power spectrum of the epochs was multiplied by the power spectrum of a set of complex Morlet wavelets that increased by 33 logarithmic steps from 1 to 13 Hz. The frequency band-specific power at each time point was calculated by squaring the absolute value of the complex signal. A decibel transformation was used to normalize the power. Specifically, we took the logarithm of the ratio of post-feedback power divided by the average baseline (−200 to 0 ms) power for each frequency.

**Figure 2** displays the time-frequency plots for all four tasks. Following established guidelines (Bernat et al., 2005), a two-step principal component analysis (PCA) was conducted to

better isolate distinct neural responses. Time-frequency surfaces for the 0–500 ms post-feedback segment were vectorized and entered into PCA Toolkit version 2.52 (Dien, 2010a) to conduct a PCA using the time-frequency vectors as variables and the participants, outcomes (correct and incorrect), and tasks (monetary win, monetary loss, social like, social dislike) as observations. Varimax rotation was applied and 55 factors were extracted based on the resulting Scree plot (Cattell, 1966). A spatial PCA was then conducted using an Infomax rotation and four factors were extracted based on the resulting Scree plot (Cattell, 1966). The two-step PCA resulted in 220 temporalfrequency spatial factors in total. With a cut-off of at least 0.5% of the variance explained, 33 factors emerged, accounting for 65.4% variance altogether. Next, we identified PCA factors for data analysis based on a two-step visual inspection approach. First, we organized the factors in order from the most to the least variance accounted for, and we ignored all factors that accounted for <1% of the variance. Second, we only examined factors that overlapped with the delta or theta frequency ranges and contained spatial distributions that centered around frontal and parietal regions. As shown in **Figure 3**, this visual inspection procedure revealed three factors that accounted for the most variance and resembled the expected delta (two delta factors TF1SF1 and TF2SF1) and theta (one theta factor TF4SF1) activity. The delta factor TF1SF1 centered on the lower frequency range (delta-low) and accounted for 10.38% variance, while delta factor TF2SF1 centered on higher frequency range (delta-high) and accounted for 7.51% variance. The theta factor accounted for 4.97% variance.

#### Data Analyses

We conducted a 2 (Domain: monetary vs. social) × 2 (Valence: positive vs. negative) × 2 (Outcome: correct vs. incorrect) repeated-measures analysis of variance (rmANOVA). Separate analyses were conducted for each PCA factor. We also conducted a 2 (Domain: monetary vs. social) × 2 (Valence: positive vs. negative) × 2 (Outcome: correct vs. incorrect) mixed-measures analysis of covariance (ANCOVA), with dysphoria and social anxiety symptoms entered as simultaneous covariates. When dysphoria and/or social anxiety symptoms were associated with PCA factors, linear regression was conducted to compute the residual scores for one symptom dimension independent of the

the time-frequency power spectrum of each factor, with yellow indicating higher power. The right column depicts the scalp distribution of the corresponding factor.

other symptom dimension. These residual scores were then used to further investigate the relationships.

We also conducted a series of analyses examining the potential effects of domain order. For each of the Domain × Valence × Outcome rmANOVAs, we entered Domain Order (monetary first vs. social first) as a betweensubjects factor. If significant interactions were identified for the Domain Order variable, we would follow up with additional ANCOVA analyses for depression and social anxiety symptoms, including the Domain Order variable as an additional betweensubject covariate. All statistical analyses were conducted in IBM SPSS Statistics, Version 25.0 (Armonk, NY, USA).

## RESULTS

## Monetary and Social Tasks

Descriptive statistics for the PCA factors and symptom measures were reported in **Table 1**. For the delta-low factor (**Figure 4A**), results indicated a main effect of domain with greater delta activity for monetary vs. social feedback, F(1,203) = 15.73, p < 0.001, η 2 p = 0.07, a main effect of valence with greater delta activity for positive vs. negative feedback, F(1,203) = 17.71, p < 0.001, η 2 p = 0.08, and a main effect of outcome with greater delta activity for correct vs. incorrect feedback, F(1,203) = 51.48, p < 0.001, η 2 p = 0.20. There was also a Domain × Outcome interaction F(1,203) = 23.95, p < 0.001, η 2 p = 0.11. Simpleeffect analyses indicated that delta activity was greater for correct compared to incorrect feedback for both monetary (mean difference = 0.29, p < 0.01) and social (mean difference = 0.95, p < 0.001) tasks, but this increase was greater for the social compared to monetary task.

For the delta-high factor (**Figure 4B**), results indicated a main effect of domain with greater delta activity for monetary vs. social feedback, F(1,203) = 18.67, p < 0.001, η 2 p = 0.08, a main effect of valence with greater delta activity for positive vs. negative feedback, F(1,203) = 23.30, p < 0.001, η 2 p = 0.10, and a main effect of outcome with greater delta activity for correct vs. incorrect feedback, F(1,203) = 28.90, p < 0.001,

#### TABLE 1 | Descriptive statistics.

η 2 p = 0.13. There were also Domain × Valence, F(1,203) = 7.22, p < 0.01, η 2 p = 0.03, Domain × Outcome, F(1,203) = 29.27, p < 0.001, η 2 p = 0.13, and Valence × Outcome interactions, F(1,203) = 6.84, p = 0.05, η 2 p = 0.03, which were qualified by a Domain × Valence × Outcome interaction, F(1,203) = 6.36, p < 0.05, η 2 p = 0.03. To follow-up the three-way interaction, we conducted separate Valence × Outcome rmANOVAs for monetary and social tasks. Simple-effect analyses indicated that for the monetary tasks, delta activity was greater for correct feedback compared to incorrect feedback for positive valence (i.e., win) trials (mean difference = 0.45, p < 0.01), but not for negative valence (i.e., loss) trials (mean difference = −0.27, ns). For the social tasks, delta activity was greater for correct compared to incorrect feedback for both positive valence (i.e., like) trials (mean difference = 0.76, p < 0.001) and negative valence (i.e., dislike) trials (mean difference = 0.72, p < 0.001).

For the theta factor (**Figure 4C**), results indicated a main effect of outcome with greater theta activity for incorrect vs. correct feedback, F(1,203) = 26.72, p < 0.001, η 2 p = 0.12, and a main effect of valence with greater theta activity for positive vs. negative valence trials, F(1,203) = 30.76, p < 0.001, η 2 p = 0.13. There was also a Valence × Outcome interaction, F(1,203) = 16.37, p < 0.001, η 2 p = 0.08). Simple-effect analyses indicated that theta activity was greater for incorrect vs. correct feedback for negative valence trials (mean difference = 0.77, p < 0.001), but not positive valence trials (mean difference = 0.13, ns).

Panels (A–C) represent the results for each factor: delta-low (TF1SF1; A), delta-high (TF2SF2; B), and theta (TF4SF1; C). Mean activity for each factor in each condition is shown for monetary (left column) and social (right column) tasks. Error bars represents ± 95% confidence interval. Negative valence is represented in red, and positive valence is represented blue. Correct and incorrect outcomes are differentiated by presence and absence of the line pattern.

## Dysphoria and Social Anxiety Symptoms

For the delta-low factor, results indicated a Domain × Social Anxiety interaction, F(1,201) = 4.02, p < 0.05, η 2 p = 0.02. For the follow-up analyses, delta-low activity was averaged across valence and outcome for the monetary and social tasks. As shown in **Figure 5**, follow-up Pearson's correlations indicated that more severe social anxiety symptoms were associated with greater delta activity for social feedback (r = 0.15, p < 0.05), but not for monetary feedback (r = 0.05, ns).

For the delta-high factor, results indicated Domain × Outcome × Dysphoria, F(1,201) = 4.24, p < 0.05, η 2 p = 0.02, and Domain × Outcome × Social Anxiety interactions, F(1,201) = 6.59, p < 0.05, η 2 p = 0.03. In order to examine these associations, we first averaged delta-high activity values across

the positive and negative valence for each domain and outcome combination. Next, we created separate difference scores for correct and incorrect outcomes (i.e., correct-incorrect) for the monetary and social tasks. Finally, in order to examine the variance explained by domain-specific responses, we computed two residual scores to quantify delta-high activity for the monetary (independent of the social difference score) and social (independent of the monetary difference score) tasks. We also calculated residuals for dysphoria (independent of social anxiety) and social anxiety (independent of dysphoria), and we conducted Pearson's correlations between the two delta-high activity residuals and the two symptom residuals. As shown in **Figure 6**, results indicated that more severe dysphoria symptoms were associated with a lower delta activity to social feedback (r = −0.16, p < 0.05), but more severe social anxiety symptoms were associated with greater delta activity to social feedback (r = 0.16, p < 0.05). In contrast, neither dysphoria (r = 0.07, ns) nor social anxiety symptoms (r = −0.12, ns) were associated with delta activity for the monetary tasks.

Finally, for the theta factor, results indicated a Domain × Dysphoria interaction, F(1,201) = 5.29, p < 0.05, η 2 p = 0.03. For follow-up analyses, theta activity was averaged across valence and outcome for monetary and social tasks. As shown in **Figure 7**, dysphoria was not significantly correlated with theta activity for monetary feedback (r = 0.01, ns) or social feedback (r = −0.10, ns) individually, but was correlated with monetary feedback minus social feedback (r = 0.16, p < 0.05).

## Domain Order

There was no effect of domain order on any of the three PCA factors (ps > 0.077). Therefore, no further ANCOVA analyses were conducted for the relationships with dysphoria and social anxiety.

## DISCUSSION

The current study is the first to examine the time-frequency representation of electrocortical responses to monetary relative to social feedback. PCA of the time-frequency data in response to feedback revealed two delta factors and one theta factor. The delta factors were both modulated by combinations of task domain (monetary vs. social), valence (positive vs. negative), and outcome (correct vs. incorrect), and showed a general tendency of greater activity to rewarding feedback vs. non-rewarding feedback. In contrast, the theta factor was sensitive to outcome and valence, but not task domain. In addition, for the social domain, delta activity was greater to correct relative to incorrect feedback among those with more severe symptoms of anxiety, but smaller in those with more severe symptoms of depression. Overall, the current study demonstrates the importance of examining neural response to feedback processing via time-frequency analysis, especially in the context of the social domain.

PCA of the time-frequency data yielded two distinct delta factors, with one factor capturing lower frequency delta activity and the other factor capturing higher frequency delta activity. These two delta factors were similarly modulated by task effects such that both the intrinsic reward of being correct and positive valence feedback elicited higher delta activity overall across monetary and social domains. These results are consistent with previous findings of reward-related delta activity using monetary tasks (Bernat et al., 2011, 2015; Foti et al., 2011; Webb et al., 2017) and extend that research to the social domain, while

activity for social feedback (difference between correct and incorrect trials) residual with dysphoria (left) and social anxiety (right) residuals.

theta activity for monetary feedback (averaged across valence and outcome) subtracting social feedback (averaged across valence and outcome) and dysphoria symptoms.