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. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: J Clin Child Adolesc Psychol. 2019 Jul 22;50(1):131–140. doi: 10.1080/15374416.2019.1630834

Neural Response to Rewards, Stress and Sleep Interact to Prospectively Predict Depressive Symptoms in Adolescent Girls

Kreshnik Burani 1, Julia Klawohn 1,3, Amanda R Levinson 2, Daniel N Klein 2, Brady D Nelson 2, Greg Hajcak 1,3
PMCID: PMC6980457  NIHMSID: NIHMS1532218  PMID: 31328972

Abstract

Blunted reward processing both characterizes major depressive disorder and predicts increases in depressive symptoms. However, little is known about the interaction between blunted reward processing and other risk factors in relation to increases in depressive symptoms. Stressful life events and sleep problems are prominent risk factors that contribute to the etiopathogenesis of depression and have been linked to reward dysfunction; these factors may interact with reward dysfunction to predict increased depressive symptoms. In a large sample of 8 to 14 year-old adolescent girls, the current study examined how blunted reward processing, stressful life events, and sleep problems at baseline interacted to predict increases in depressive symptoms one year later. Reward processing was indexed by the reward positivity (RewP), an event-related potential elicited during a simple monetary reward paradigm (i.e. Doors task). Two-way interactions confirmed that a blunted RewP predicted increased depressive symptoms at: a) high levels of stress but not average or low levels of stress; and b) high and average levels of sleep problems but not low levels of sleep problems. Finally, a three-way interaction confirmed that a blunted RewP predicted increased depressive symptoms at high levels of stress and sleep problems but not average or low levels of stress and sleep problems. Thus, adolescents characterized by low reward response (i.e., blunted RewP) were at an increased risk of developing depressive symptoms if they experienced increased stressful life events or sleep problems; moreover, risk was greatest among adolescents characterized by all three.

Keywords: Reward Positivity, Depression, Adolescence, Stress, Sleep problems

Depression is increasingly linked to reward system dysfunction (Whitton, Treadway, & Pizzagalli, 2015). Neuroimaging research examining deficits in reward processing in depression has focused mainly on the mesolimbic pathway and on the striatum in particular (Nestler & Carlezon Jr, 2005). Adults and adolescents with major depressive disorder (MDD) show reduced ventral striatum activation to rewards compared to their healthy peers (Pizzagalli et al., 2009; Forbes et al., 2009). Blunted striatal response to rewards is also evident in healthy young adults and adolescents who report elevated depressive symptoms (Forbes et al., 2010; McCabe et al., 2012), as well as in adolescents who are at increased risk for depression based on maternal history of depression (Gotlib et al., 2010). In fact, blunted ventral striatum activation prospectively predicts increases in depressive symptoms in adolescence (Telzer et al., 2014; Keren et al., 2018; Stringaris et al., 2015). Collectively, these data suggest that reduced activation in reward-related circuitry is part of the etiopathogenesis of depression.

In addition to functional magnetic resonance imaging (fMRI) studies, electroencephalogram (EEG) research has focused on an event-related potential (ERP) referred to as the reward positivity (RewP). The RewP is evident 250 ms to 350 ms at frontocentral electrode sites following the presentation of rewards compared to non-rewards. Although a relative negativity was originally interpreted in the ERP following non-rewards (i.e., the feedback negativity), more recent research supports the possibility that the difference between gains and losses is driven by variability in the response to rewards (Holroyd, Pakzad-Vaezi and Krigolson, 2008). Using principal component analysis (PCA), Foti, Weinberg, Dien and Hajcak (2011) found that the differentiation between reward and loss ERPs was driven by a reward-related positivity that was absent or reduced on non-reward trials. In support, an increased RewP is related to heightened ventral striatal activation to reward measured using fMRI (Carlson, Foti, Mujica-Parodi, Harmon-Jones, & Hajcak, 2011). Collectively, these data suggest that ERP variability in the time-range of the RewP is related to reward rather than loss.

The RewP has good psychometric properties (Levinson et al., 2017; Luking et al., 2017) and is related to personality traits linked to depression, such as low positive emotionality (Kujawa et al., 2015; Speed et al., 2018). In addition, a blunted RewP is evident among depressed children and adults (Belden et al., 2016; Foti & Hajcak, 2009; Brush et al., 2018; Liu et al., 2014), and is observable prior to the onset of depression and can predict increases in depressive symptoms and first-onset MDD (Bress, Foti, Kotov, Klein, & Hajcak, 2013; Nelson et al., 2016). Recent evidence also indicates that the RewP may interact with other risk factors in predicting the development of depression. For instance, one investigation found that a maternal history of depression predicted increased levels of depressive symptoms, but only in the context of a blunted RewP (Kujawa, Hajcak, & Klein, 2018). Overall, reward system function indexed by the RewP appears robustly related to risk for developmental increases in depressive symptoms and disorders (Proudfit, 2015).

In addition to blunted neural reward measures, stressful life events are associated with increases in depressive symptoms and prospectively predict the onset of depression in adolescence (Monroe, Rohde, Seeley & Lewinsohn, 1999; Abela & Skitch, 2007; Allgood-Merten, Lewinsohn, & Hops, 1990). Other studies have similarly found that stressful life events predict increases in depressive symptoms, even after controlling for baseline depressive symptoms (Auerbach et al., 2010)—results that may be particularly true for females (Hankin, Mermelstein, & Roesch, 2007; Ge et al., 1994; Rudolph & Hammen, 1999). Collectively, these studies suggest that both a blunted neural response to reward and stressful life events are prominent risk factors for the development of depressive symptoms.

Increasingly, theory and research suggest that stress may interact with reward-related neural systems to impact depression, such that those at the greatest risk for depression may be individuals characterized by reduced reward activation and increased stressful life events (Pizzagalli, 2014). To our knowledge, only one study has examined how neural measures of reward processing and stressful life events relate to depressive symptoms within an adolescent sample. Specifically, Luking and colleagues (2018) found that ventral striatum activity interacted with negative life events to explain increased depressive symptoms cross-sectionally (Luking, Nelson, Infantolino, Sauder, & Hajcak, 2018). One aim of the current study is to investigate how reduced reward processing, as measured by the RewP, and stressful life events interact to predict longitudinal increases in depressive symptoms in an adolescent sample.

Similar to stressful life events, sleep disturbances have depressogenic effects (Roberts & Duong, 2013; Roberts & Duong, 2014). Nearly three quarters of adolescents with MDD or elevated depressive symptoms suffer from sleep disturbances (Liu et al., 2007). One study found that sleep disturbance during childhood predicted adolescent-onset depression (Ong, Wickramaratne, Tang, & Weissman, 2006); other studies have similarly found that sleep problems predicted increased depressive symptoms (Gregory et al., 2009; Roberts & Duong, 2014). Indeed, a recent meta-analysis confirmed a directional relationship between sleep problems and depressive symptoms such that sleep problems precede the onset of MDD and increases in depressive symptoms in adolescents (Lovato & Gradisar, 2014).

In addition to directly contributing to the onset of MDD and increased depressive symptoms, sleep problems have also been linked to reduced reward processing in adolescence: poor sleep quality has been related to reduced ventral striatum activation to reward feedback (Holm et al., 2009; Telzer et al., 2013; Hasler et al., 2012). Collectively, these studies suggest that sleep disturbances are related to reduced reward processing and significantly enhance the risk for developing depression. A second goal of the current study is to examine whether increased sleep problems moderate the relationship between blunted reward processing and prospective increases in depressive symptoms. Moreover, the current study examines whether the experience of stress and sleep disturbances are unique or overlapping moderators of the association between the RewP and subsequent increases in depressive symptoms.

The current study seeks to examine whether a blunted RewP, stressful life events, and sleep problems interact to predict increases in depressive symptoms during late childhood and early adolescence. We conceptualize neural response to reward (i.e., the RewP) as a diathesis, whereas both stressful life events and sleep problems act as stressors. In support of this view, there is evidence that a blunted RewP precedes the onset of MDD (Nelson et al., 2016). Moreover, RewP appears to aggregate in families, as it is reduced in individuals with a maternal history of depression (Kujawa, Proudfit, & Klein, 2014). In addition, an individual’s level of positive affect is associated with their sibling’s RewP, such that decreased positive affect in probands is linked with decreased RewP in siblings—even after controlling for the sibling’s own level of positive affect (Weinberg, Liu, Hajcak and Shankman, 2015). Finally, there is evidence from a twin study that reward system measures apart from the RewP may be heritable (Macoveanu, Miskowiak, Vedel Kessing, Vinberg, Siebner, 2015). However, we note that sleep problems may be conceptualized as a diathesis since they may precede the onset of depression.

We focused on late childhood and early adolescence as this developmental period is characterized by increases in depressive symptoms. A large sample of adolescent girls completed measures of reward processing, depressive symptoms, stressful life events, and sleep problems at baseline. One year later, they completed the same measure of depressive symptoms. First, we examine whether baseline RewP interacts with exposure to stressful life events to predict increases in depressive symptoms one year following the baseline visit. We also test whether baseline RewP similarly interacts with sleep problems at baseline to predict a prospective increase in depression. We hypothesize that a blunted RewP will predict increased depressive symptoms, but only in the context of increased stress or sleep disturbances. Finally, we conducted exploratory analysis to investigate the outcome of a three-way interaction between RewP, stressful life events and sleep problems to determine their combined effect on prospective increases in depression.

Methods

Participants

Participants were part of a community sample of 317 adolescent girls between the ages 8 to 14 years old (M = 12.4; SD = 1.8) from Long Island, New York and were predominantly Caucasian (86 %), with the remaining participants identifying as 5% African American, 5% Hispanic, and 4% Other. Participants were recruited from the community through a commercial mailing list, word of mouth and use of study-related flyers. Most parents reported having obtained a bachelor’s degree (31.3 %) or a Master’s degree (27.9). Depressive symptoms were assessed one year later in 252 participants. Of the 252 participants with data available at both assessments, 9 participants were excluded due to poor quality EEG data. In total, data from 243 participants were available for analysis. Of the 243 participants, 187 participants (baseline age: M = 12.6; SD =1.7) completed the Adolescent Life Events Questionnaire (ALEQ) at the initial assessment since the stress questionnaire was introduced after data collection had begun; 238 participants (baseline age: M = 12.4; SD = 1.8) completed the Pittsburgh Sleep Quality Index (PSQI) at the initial assessment. For the post-hoc moderation analysis testing the three-way interaction between the reward positivity, stressful life events, and sleep problems, the sample included 183 participants (baseline age: M = 12.6; SD = 1.7). Participants without follow-up data reported significantly more stressful life events at baseline (t = −2.02, p = .045). However, participants with and without follow-up data did not differ on baseline measures of RewP (t = 1.34; p = .18), age (t = .270, p = .79), depressive symptoms (t = 1.44, p = .15) or PSQI total score (t = −.096, p = .92). Along the same lines, participants with (n =187) and without (n = 56) the ALEQ at baseline did not differ on the RewP (t = 0.73, p = .47), depressive symptoms (t = 1.31, p = .19), or PSQI total score (t = 0.62, p = .53). All participants and their parents provided informed assent and consent, respectively, as approved by the Institutional Review Board at [REMOVED FOR MASKED REVIEW].

Measures

Adolescent Life Events Questionnaire (ALEQ)

The ALEQ measures a range of stressful life events including school and family problems, and friendship and romantic relationship difficulties, and has good psychometric properties (Hankin & Abramson, 2002). Participants completed the ALEQ at the baseline visit. Participants rated the frequency with which each of 57 stressful life events had occurred in the past 3 months. A 5-point rating scale from 0 (never) to 4 (always) was used to rate each event. The total score was calculated by summing the frequency scores of each of the events.

Children’s Depression Inventory (CDI)

The CDI is a measure of depressive symptoms in children (Kovacs, 1992) and has good internal consistency and test-retest reliability (Masip et al., 2010). Participants completed the CDI at both baseline and 1-year follow-up visits. This measure consists of 27 items rated on a 3-point scale (0–2). The internal consistency of the CDI total score at baseline (α = .89) as well as at follow-up (α = .89) was good.

Pittsburgh Sleep Quality Index (PSQI)

The PSQI was administered at the baseline visit to assess sleep disturbances in the past month. It has good psychometric properties (Buysse et al., 1989). To be more age appropriate, the current study slightly modified the PSQI by removing the item that asks whether or not the respondent has a roommate. Overall, the measure consists of 18 items rated on a 4-point scale (0–3). The internal consistency of the total PSQI sleep score in our sample was moderate, α = .67. Although the PSQI is commonly used in adults, some research studies have utilized the PSQI in children and adolescents and report similar internal consistency as the current study (de la Vega et al., 2015; Lund, Reider, Whiting & Prichard, 2010; Siu, Chan, Wong & Wong, 2012).

Doors Task

The Doors task is a simple monetary reward task in which gains and losses are equiprobable on each trial (Proudfit, 2015). Using the left and right mouse buttons, participants were instructed to select between two identical doors displayed on a computer screen. After making a selection, a fixation cross was presented for 1500 ms, followed by feedback stimuli that indicated whether they won (i.e., a green arrow pointing upward signified +$0.50) or lost (a red arrow pointing downward signified -$0.25) on that trial. The feedback stimuli remained on the screen for 2000 ms. Text appeared on the screen that instructed participants to “Click for next round”, followed by a fixation cross presented for 1000 ms. The task contained 30 gain trials and 30 loss trials that were presented in pseudo-random order, using Presentation, version 17.2 (Neurobehavioral Systems, Albany, Calif.). Participants were told that they could receive between $0 to $15 dollars at the end of the task based on their cumulative earnings; all participants received $8 for completing the task.

Psychophysiological Data Recording and Processing

Continuous electroencephalography (EEG) was recorded while participants completed the doors task. EEG signal was recorded using the ActiveTwo BioSemi system (BioSemi, Amsterdam, Netherlands) with an elastic cap containing 34-electrode sites placed according to the 10/20 system (i.e., 32 channels plus FCz and Iz). Additional electrodes were placed above and below the left eye, and near the outer canthi of the left and right eyes to monitor vertical electrooculographic (VEOG) activity and horizontal electrooculographic (HEOG) activity. Two electrodes were placed on the left and right mastoids. The EEG signal was preamplified at the electrode to improve signal-to-noise ratio, and data were digitized at a 24-bit resolution with a sampling rate of 1024 Hz using a low-pass fifth-order sinc filter with a half-power cutoff of 204 Hz. Active electrodes were measured online with reference to a common mode sense active electrode constructing a monopolar channel.

EEG data were analyzed using BrainVision Analyzer, version 2.1 (Brain Products, Gilching, Germany). The raw EEG data were re-referenced offline to the average of the left and right mastoids and band-pass filtered from 0.1 to 30 Hz. Eyeblink and ocular-movement corrections were performed using established standards described by Gratton, Coles, and Donchin (1983). Feedback-locked epochs were extracted with a duration of 1000 ms, starting 200 ms before feedback presentation. The 200 ms segment prior to feedback onset served as the baseline. Epochs containing a voltage greater than 50 μV between consecutive sample points, a voltage difference of 300 μV within a segment, or a maximum voltage difference of less than 0.5 μV within 100 ms intervals were automatically rejected. The reward positivity was scored by utilizing the mean activity in a 100 ms window around the peak of the gain minus loss difference waveform within 200 to 400 ms timeframe at electrode site FCz for each subject.

Data Analysis

Analyses were conducted with the statistical software package SPSS, Version 23 (SPSS Inc, Armonk, NY). Bivariate correlations were assessed using Pearson’s r correlations between the baseline RewP, ALEQ total stress score, PSQI total sleep score, CDI at baseline and follow-up as well as the Pubertal Development Scale (PDS; Peterson, Crockett, Richards and Boxer, 1988). Moderation analyses were conducted using the PROCESS macro (Version 3.1; Hayes, 2017) model 1 for the first two moderation models and model 3 to test the three-way interaction (i.e., double moderation model). The a priori analyses included two separate moderation models. The first moderation model included the reward positivity as the independent variable, CDI total score at follow-up as the dependent variable, CDI baseline total score as a covariate, and ALEQ total score as the moderator. The second moderation model included the same independent and dependent variables and covariate as the first model; however, PSQI total sleep score was used as the moderator. A follow-up exploratory analysis was conducted using both ALEQ total stress and PSQI sleep scores as moderators to examine whether a three-way interaction effect existed between the reward positivity, stress, and sleep problems in predicting changes in depressive symptoms. All moderation models used a bootstrapping approach with 5,000 bootstrapped samples, and significance was determined using a 95% confidence interval.

Results

Table 1 displays descriptive statistics and bivariate correlations among the baseline RewP, ALEQ stress score, PSQI sleep score, PDS and CDI depressive symptoms as well as CDI at follow-up. Figure 1 displays the waveform and scalp topography of the RewP at baseline.

Table 1.

Bivariate correlations of the RewP, PDS, ALEQ total score and PSQI total score, CDI score at baseline and at follow-up as well as the descriptive statistics of the sample.

RewP PDS ALEQ Total Score PSQI Total Score Baseline CDI Follow-up CDI
RewP - .028 .06 .12* .062 −.013
PDS - .34** .36** .027 .30**
ALEQ Total Score - .41** .70** .55**
PSQI Total Score - .55** .44**
Baseline CDI - .63**
Mean 5.5 (N= 305) 13.3 (N=302) 31.3 (N= 187) 4.9 (N=238) 6.7 (N=305) 7.0 (N=243)
Standard Deviation 6.0 4.2 23.8 2.9 6.5 7.1
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Note.

*

p < .05.

**

p < .01.

Figure 1:

Figure 1:

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Waveform and headmap of the RewP (dashed) as well as the neural response to gains (black) and losses (gray) at electrode site FCz. The x-axis represents time in milliseconds and the y-axis represents the amplitude in microvolts (uV). The headmap represents the scalp distribution of the RewP during 100 ms interval from 250 to 350 ms following feedback (N = 183).

The first moderation model involved the RewP and stressful life events. This model yielded no main effects of RewP or stress on depressive symptoms; however, there was a significant interaction between the RewP and stress (see Table 2). The simple slopes analyses (see Figure 2) revealed that a more blunted RewP predicted increased depressive symptoms among adolescents who had higher levels of stress (+ 1 SD; b = − 0.26, t = −2.73, p < .01) compared to adolescents who experienced average levels (b = −0.11, t = −1.63, p = .11) or low levels of stress (− 1 SD; b = 0.04, t = 0.43, p = .67)1.

Table 2.

The table displays the main effects of ALEQ stress (M = 31.3; SD = 23.8), RewP (M = 5.92; SD = 5.91) and their interaction predicting depressive symptoms at follow-up while covarying for depressive symptoms at baseline (N = 187).

Predictor B SE Lower CI Upper CI
RewP 0.09 .11 −.13 .30
ALEQ Total** 0.10 .03 .04 .16
RewP × ALEQ* −0.006 .003 −.012 −.0007
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Note.

*

p < .05.

**

p < .01.

Figure 2:

Figure 2:

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The figure displays the predicted CDI depression scores at follow-up from the interaction between baseline RewP and ALEQ stress. Note that CDI depression scores at baseline were included as a covariate.

The second moderation model involved the RewP and sleep disturbance. This model yielded a significant main effect of the RewP and sleep on depressive symptoms, as well as a significant interaction between the RewP and total sleep score (see Table 3). The simple slopes analyses (see Figure 3) revealed that a more blunted RewP predicted increased depressive symptoms when sleep problems were at a high (+ 1 SD; b = − 0.38, t = −3.7, p < .005) and average (b = − 0.14, t = −2.31, p < .05), but not a low level (−1 SD; b = 0.10; t = 1.22, p = .23).

Table 3.

The table displays the main effects of sleep problems (M = 4.7; SD = 2.9), the RewP (M = 5.8; SD = 6.0) and their interaction predicting depressive symptoms at follow-up while covarying for depressive symptoms at baseline (N = 238).

Predictor B SE Lower CI Upper CI
Reward Positivity* 0.24 .11 .02 .46
PSQI Total** 0.83 .20 .44 1.2
RewP × PSQI** −0.08 .02 −.13 −.04
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Note.

*

p < .05.

**

p < .01.

Figure 3:

Figure 3:

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The figure displays the predicted CDI depression scores at follow-up from the interaction between baseline RewP and PSQI sleep. Note that CDI scores at baseline were included as a covariate.

The third, exploratory, moderation model examined whether stress and sleep score together moderated the relationship between the RewP and depressive symptoms. This model yielded no main effect of the RewP, stress and sleep on depressive symptoms. In addition, there was no two-way interaction between the RewP and stress or the RewP and sleep; however, there was a significant two-way interaction between stress and sleep. Finally, there was a significant three-way interaction between the RewP, stress and sleep score (see Table 4). The simple slopes analyses (see Figure 4) revealed that the interaction between the RewP, ALEQ stress score, and sleep problems significantly predicted depressive symptoms at a high level of stressful life events (+ 1 SD) and a high level of sleep problems only (+ 1 SD; b = −0.44; t = −3.74, p < .005)2. In contrast, the interaction between the RewP, stress, and sleep problems was not significant at high stress (+1 SD) and low sleep (−1 SD; b = 0.15, t = 0.93, p = .35), low stress (−1 SD) and high sleep (+ 1 SD; b = 0.09, t = 0.39, p = .70), or low stress (−1 SD) and low sleep (+1 SD; b = −0.07, t = −0.56, p = .57).

Table 4.

The table displays the main effect of the RewP (M = 6.0; SD = 5.9), stress (M = 31.1; SD = 23.8), sleep problems (M = 4.7; SD = 2.9) at baseline on depressive symptoms at follow-up while covarying for CDI depressive symptoms at baseline. In addition, it displays the RewP and stress interaction, the RewP and sleep interaction, the stress and sleep interaction, and the RewP, sleep and stress interaction predicting depressive symptoms at 1-year follow-up (N = 183).

Predictor B SE Lower CI Upper CI
Reward Positivity −0.20 .23 −.65 .27
ALEQ Total Score −0.04 .06 −.15 .07
RewP × ALEQ 0.01 .006 −.003 .02
PSQI Total Sleep −0.28 .40 −1.1 .51
RewP × PSQI 0.05 .06 −.07 .16
ALEQ × PSQI* 0.02 .009 .006 .04
RewP × PSQI × ALEQ* −0.003 .001 −.005 −.0004
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Note.

*

p < .05.

**

p < .01.

Figure 4:

Figure 4:

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The figure displays the predicted CDI depression scores at follow-up from the three-way interaction between baseline RewP, ALEQ stress score and PSQI sleep score. Note that CDI scores at baseline were included as a covariate.

Discussion

The goal of the current study was to examine potential moderators of the relationship between blunted neural reward system activation and subsequent increases in depressive symptoms in a community sample of adolescent girls during late childhood and early adolescence. We found that a blunted RewP predicted increased depressive symptoms in individuals exposed to high levels of stress; however, a blunted RewP did not predict increased depressive symptoms in individuals exposed to average or low stress levels. In addition, a blunted RewP predicted increased depressive symptoms among individuals with elevated and average sleep problems, but not in individuals with low sleep problems. Finally, we observed a significant three-way interaction between the RewP, stressful life events, and sleep problems, such that participants at baseline who were characterized by a blunted RewP, more stressful life events, and sleep problems had the greatest subsequent increase in depressive symptoms.

Given the potential utility of leveraging reward system activation to predict risk for depression, it is important to consider moderators that may affect this relationship. The current study suggests that exposure to stressful life events and sleep problems may be particularly potent moderators of the relationship between reduced reward system activation and subsequent increases in depression during adolescence. Previous research has consistently found that increased sleep problems and stressful life events have depressogenic effects; however, the current study is the first to indicate that these factors interact to impact the relationship between reduced reward processing and prospective increases in depression. The results of the current study are in line with Luking et al. (2017) who showed that stressful life events interacted with fMRI reward processing components to explain concurrent depressive symptoms. The current study extends those results by examining prospective increases in depressive symptoms and integrating the effects of sleep problems on the relationship between a blunted RewP and increases in depressive symptoms.

Insofar as the moderator and independent variable are interchangeable statistically, the current results could also be interpreted as evidence that a large neural response to reward is a protective factor—such that stressful life events and sleep disturbance did not predict increases in depression among participants characterized by a large and robust RewP. Regardless, variation in reward sensitivity could serve as a pre-existing neurobehavioral trait such that a blunted RewP characterizes at-risk individuals, and increased response to rewarding stimuli may reflect resilience against the development of depression in the face of stressors and poor sleep. Although increased reward processing may serve as a resilience factor for depression, it may relate to other negative outcomes. For instance, among vulnerable individuals, potentiated responses to rewards could lead to excessive approach-related affect characteristic of hypomania and mania symptoms (Boland et al., 2016; Alloy & Nusslock 2019). A possible mechanism explaining our findings is that stressful life events and sleep problems might both inhibit dopaminergic activity of the reward circuit, and thereby have negative consequences on reward circuit activation over time (Pizzagalli, 2014). In this way, stressful life events and sleep problems might be harbingers of later reductions of the RewP. Along these lines, it is important to note that the RewP was measured concurrently with sleep problems and stressful life events. Future studies should examine the sequential or bi-directional relationships between these variables to determine the time-scale on which stressful life events and sleep disturbance influence one another, and their potential impact on reward-related neural activity. In the current study, we are unable to draw inferences about casual pathways among reward processing, sleep problems and stressful events, and further research is needed to evaluate the temporal associations between these variables.

The present study provides novel evidence for the moderating effects of stress and sleep problems on the relationship between reduced reward processing and subsequent increases in depression; however, there are several limitations worth noting. First, although the sample for each analysis was relatively large, only adolescent girls were included. Future studies are needed to understand whether these results generalize to different populations such as males, older adolescents, and adults. Given that female adolescents are at increased risk for depression, it is conceivable that associations between reward processing, stress, sleep and subsequent development of depression are stronger in girls compared to boys. Second, it will be important for future studies to determine whether the effects of stress, sleep problems and reduced reward processing predict depression diagnoses, as opposed to symptoms, which were generally in the milder range. Third, depressive symptoms were assessed more than a year following the exposure to stressful life events. Research suggests that the effects of stressful life events are weaker after six months (Kenneth, Karkowski, & Prescott, 1998; Brown and Harris, 2012)—thus, the extended time between the stressful events and the assessment of depressive symptoms may underestimate the magnitude of the association. Fourth, the current study utilized a between-subjects approach to analyze the effects of reward processing, stressful life events, and sleep problems on change in depressive symptoms. Future studies might utilize a within-subjects analytic approach by obtaining repeated measures of reward processing, stressful life events, sleep problems, and depression – this would provide information about the circumstances in which youth are likely to experience increases in depression at the individual level.

Fifth, the current study only examined how negative events affect the relationship between reward responding and depression. The vantage sensitivity model provides a theoretical framework to investigate how positive experiences can mitigate risk factors for developing depression (Pluess and Belsky, 2013). Future studies are needed to examine whether positive events might interact with reward processing to impact depressive symptoms. Finally, self-report measures of stressful life events and sleep problems have important limitations. Self-reports of life events may not accurately distinguish between stress exposure and stress response, and most questionnaire measures do not consider the context in which life events occur (Harkness & Monroe, 2016). For example, one of the items in the ALEQ asks participants how often they failed a test or class project, but does not account for the percentage of the final class grade or whether the class was important to the individual or not. Self-report measures of stressful life events can also be biased by depressogenic personality traits that may affect subjective reporting of stressful events (Liu, 2013). Similarly, the PSQI is not an objective measure of sleeping habits, and is not commonly utilized in children and adolescents. It would behoove future researchers to utilize more objective measures of sleep, such as actigraphs.

In conclusion, the results of the current study provide novel and valuable insights into the pathogenesis of depression during adolescence and could be most useful for developing targeted intervention and prevention efforts that focus on increasing reward sensitivity (i.e., potentiating RewP), decreasing sleep problems, and/or aiding in stress management. Indeed, due to the heterogeneity of depression, the most effective treatments may be those that concurrently target multiple factors, include sleep problems, external stressors, and reward responsiveness.

Acknowledgments

Funding

This work was supported by the National Institute of Mental Health (R01 MH097767) to GH. The authors have no conflicts of interest to report.

Footnotes

1

The ALEQ was also completed by participants at the follow-up assessment. When ALEQ total score at follow-up was used as the moderator, the model yielded no main effect of the RewP or stress on depressive symptoms. In addition, the interaction between the RewP and stress did not significantly predict changes in depressive symptoms (b = −0.004, SE = .003, p = .10), though the effect was at a trend level and in the same direction as the moderation model using baseline ALEQ.

2

When pubertal status was included as a covariate in all of the moderation models tested, the interaction between the RewP and stressful life events trended towards significance (b = −0.0053, SE = .0028, p = .06), whereas the interaction between the RewP and sleep problems (b = −0.08, SE = 0.02, p < .001) and the three-way interaction between the RewP, stressful life events and sleep problems (b = −0.003, SE = .001, p < .05) continued to be significant.

References

  1. Abela JR, & Skitch SA (2007). Dysfunctional attitudes, self-esteem, and hassles: Cognitive vulnerability to depression in children of affectively ill parents. Behaviour Research and Therapy, 45(6), 1127–1140. [DOI] [PubMed] [Google Scholar]
  2. Alloy LB, & Nusslock R (2019). Future Directions for Understanding Adolescent Bipolar Spectrum Disorders: A Reward Hypersensitivity Perspective. Journal of Clinical Child & Adolescent Psychology, 1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Allgood-Merten B, Lewinsohn PM, & Hops H (1990). Sex differences and adolescent depression. Journal of abnormal psychology, 99(1), 55. [DOI] [PubMed] [Google Scholar]
  4. Auerbach RP, Bigda-Peyton JS, Eberhart NK, Webb CA, & Ho MHR (2011). Conceptualizing the prospective relationship between social support, stress, and depressive symptoms among adolescents. Journal of abnormal child psychology, 39(4), 475–487. [DOI] [PubMed] [Google Scholar]
  5. Belden AC, Irvin K, Hajcak G, Kappenman ES, Kelly D, Karlow S, ... & Barch DM (2016). Neural correlates of reward processing in depressed and healthy preschool-age children. Journal of the American Academy of Child & Adolescent Psychiatry, 55(12), 1081–1089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Boland EM, Stange JP, LaBelle DR, Shapero BG, Weiss RB, Abramson LY, & Alloy LB (2016). Affective disruption from social rhythm and Behavioral Approach System (BAS) sensitivities: A test of the integration of the social zeitgeber and BAS theories of bipolar disorder. Clinical Psychological Science, 4(3), 418–432. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bress JN, Foti D, Kotov R, Klein DN, & Hajcak G (2013). Blunted neural response to rewards prospectively predicts depression in adolescent girls. Psychophysiology, 50(1), 74–81. [DOI] [PubMed] [Google Scholar]
  8. Brown GW, & Harris T (2012). Social origins of depression: A study of psychiatric disorder in women. Routledge. [Google Scholar]
  9. Brush CJ, Ehmann PJ, Hajcak G, Selby EA, & Alderman BL (2018). Using multilevel modeling to examine blunted neural responses to reward in major depression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. [DOI] [PubMed] [Google Scholar]
  10. Buysse DJ, Reynolds CF III, Monk TH, Berman SR, & Kupfer DJ (1989). The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry research, 28(2), 193–213. [DOI] [PubMed] [Google Scholar]
  11. Carlson JM, Foti D, Mujica-Parodi LR, Harmon-Jones E, & Hajcak G (2011). Ventral striatal and medial prefrontal BOLD activation is correlated with reward-related electrocortical activity: a combined ERP and fMRI study. Neuroimage, 57(4), 1608–1616. [DOI] [PubMed] [Google Scholar]
  12. Foti D, & Hajcak G (2009). Depression and reduced sensitivity to non-rewards versus rewards: Evidence from event-related potentials. Biological psychology, 81(1), 1–8. [DOI] [PubMed] [Google Scholar]
  13. Foti D, Weinberg A, Dien J, & Hajcak G (2011). Event-related potential activity in the basal ganglia differentiates rewards from nonrewards: Temporospatial principal components analysis and source localization of the feedback negativity. Human brain mapping, 32(12), 2207–2216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Forbes EE, Hariri AR, Martin SL, Silk JS, Moyles DL, Fisher PM, ... & Dahl RE (2009). Altered striatal activation predicting real-world positive affect in adolescent major depressive disorder. American Journal of Psychiatry, 166(1), 64–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Forbes EE, Ryan ND, Phillips ML, Manuck SB, Worthman CM, Moyles DL, ... & Dahl RE (2010). Healthy adolescents’ neural response to reward: associations with puberty, positive affect, and depressive symptoms. Journal of the American Academy of Child & Adolescent Psychiatry, 49(2), 162–172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. de la Vega R, Tomé-Pires C, Solé E, Racine M, Castarlenas E, Jensen MP, & Miró J (2015). The Pittsburgh Sleep Quality Index: Validity and factor structure in young people. Psychological assessment, 27(4), e22. [DOI] [PubMed] [Google Scholar]
  17. Ge X, Lorenz FO, Conger RD, Elder GH, & Simons RL (1994). Trajectories of stressful life events and depressive symptoms during adolescence. Developmental psychology, 30(4), 467. [Google Scholar]
  18. Gotlib IH, Hamilton JP, Cooney RE, Singh MK, Henry ML, & Joormann J (2010). Neural processing of reward and loss in girls at risk for major depression. Archives of general psychiatry, 67(4), 380–387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gregory AM, Rijsdijk FV, Lau JY, Dahl RE, & Eley TC (2009). The direction of longitudinal associations between sleep problems and depression symptoms: a study of twins aged 8 and 10 years. Sleep, 32(2), 189–199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hankin BL, & Abramson LY (2002). Measuring cognitive vulnerability to depression in adolescence: Reliability, validity, and gender differences. Journal of clinical child and adolescent psychology, 31(4), 491–504. [DOI] [PubMed] [Google Scholar]
  21. Hankin BL, Mermelstein R, & Roesch L (2007). Sex differences in adolescent depression: Stress exposure and reactivity models. Child development, 78(1), 279–295. [DOI] [PubMed] [Google Scholar]
  22. Harkness KL, & Monroe SM (2016). The assessment and measurement of adult life stress: Basic premises, operational principles, and design requirements. Journal of Abnormal Psychology, 125(5), 727. [DOI] [PubMed] [Google Scholar]
  23. Hasler BP, Dahl RE, Holm SM, Jakubcak JL, Ryan ND, Silk JS, ... & Forbes EE (2012). Weekend–weekday advances in sleep timing are associated with altered reward-related brain function in healthy adolescents. Biological psychology, 91(3), 334–341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hayes AF (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press. [Google Scholar]
  25. Holm SM, Forbes EE, Ryan ND, Phillips ML, Tarr JA, & Dahl RE (2009). Reward-related brain function and sleep in pre/early pubertal and mid/late pubertal adolescents. Journal of Adolescent Health, 45(4), 326–334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Holroyd CB, Pakzad-Vaezi KL, & Krigolson OE (2008). The feedback correct-related positivity: Sensitivity of the event-related brain potential to unexpected positive feedback. Psychophysiology, 45(5), 688–697. [DOI] [PubMed] [Google Scholar]
  27. Kendler KS, Karkowski LM, & Prescott CA (1998). Stressful life events and major depression: risk period, long-term contextual threat, and diagnostic specificity. The Journal of nervous and mental disease, 186(11), 661–669. [DOI] [PubMed] [Google Scholar]
  28. Keren H, O’Callaghan G, Vidal-Ribas P, Buzzell GA, Brotman MA, Leibenluft E, ... & Pine DS (2018). Reward Processing in Depression: A Conceptual and Meta-Analytic Review Across fMRI and EEG Studies. American Journal of Psychiatry, appi-ajp. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kovacs M (1992). Children’s depression inventory: Manual. Multi-Health Systems. [Google Scholar]
  30. Kujawa A, Hajcak G, & Klein DN (2018). Reduced reward responsiveness moderates the effect of maternal depression on depressive symptoms in offspring: evidence across levels of analysis. Journal of Child Psychology and Psychiatry. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kujawa A, Proudfit GH, Kessel EM, Dyson M, Olino T, & Klein DN (2015). Neural reactivity to monetary rewards and losses in childhood: Longitudinal and concurrent associations with observed and self-reported positive emotionality. Biological psychology, 104, 41–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kujawa A, Proudfit GH, & Klein DN (2014). Neural reactivity to rewards and losses in offspring of mothers and fathers with histories of depressive and anxiety disorders. Journal of abnormal psychology, 123(2), 287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Levinson AR, Speed BC, Infantolino ZP, & Hajcak G (2017). Reliability of the electrocortical response to gains and losses in the doors task. Psychophysiology, 54(4), 601–607. [DOI] [PubMed] [Google Scholar]
  34. Liu RT (2013). Stress generation: Future directions and clinical implications. Clinical Psychology Review, 33(3), 406–416. [DOI] [PubMed] [Google Scholar]
  35. Liu WH, Wang LZ, Shang HR, Shen Y, Li Z, Cheung EF, & Chan RC (2014). The influence of anhedonia on feedback negativity in major depressive disorder. Neuropsychologia, 53, 213–220. [DOI] [PubMed] [Google Scholar]
  36. Liu X, Buysse DJ, Gentzler AL, Kiss E, Mayer L, Kapornai K, ... & Kovacs M (2007). Insomnia and hypersomnia associated with depressive phenomenology and comorbidity in childhood depression. Sleep, 30(1), 83–90. [DOI] [PubMed] [Google Scholar]
  37. Lovato N, & Gradisar M (2014). A meta-analysis and model of the relationship between sleep and depression in adolescents: recommendations for future research and clinical practice. Sleep medicine reviews, 18(6), 521–529. [DOI] [PubMed] [Google Scholar]
  38. Luking KR, Nelson BD, Infantolino ZP, Sauder CL, & Hajcak G (2017). Internal consistency of functional magnetic resonance imaging and electroencephalography measures of reward in late childhood and early adolescence. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(3), 289–297. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Luking KR, Nelson BD, Infantolino ZP, Sauder CL, & Hajcak G (2018). Ventral Striatal Function Interacts with Positive and Negative Life Events to Predict Concurrent Youth Depressive Symptoms. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. [DOI] [PubMed] [Google Scholar]
  40. Lund HG, Reider BD, Whiting AB, & Prichard JR (2010). Sleep patterns and predictors of disturbed sleep in a large population of college students. Journal of adolescent health, 46(2), 124–132. [DOI] [PubMed] [Google Scholar]
  41. Macoveanu J, Miskowiak K, Kessing LV, Vinberg M, & Siebner HR (2016). Healthy co-twins of patients with affective disorders show reduced risk-related activation of the insula during a monetary gambling task. Journal of psychiatry & neuroscience: JPN, 41(1), 38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Masip AF, Amador-Campos JA, Gómez-Benito J, & del Barrio Gándara V (2010). Psychometric properties of the Children’s Depression Inventory in community and clinical sample. The Spanish journal of psychology, 13(2), 990–999. [DOI] [PubMed] [Google Scholar]
  43. McCabe C, Woffindale C, Harmer CJ, & Cowen PJ (2012). Neural processing of reward and punishment in young people at increased familial risk of depression. Biological psychiatry, 72(7), 588–594. [DOI] [PubMed] [Google Scholar]
  44. Monroe SM, Rohde P, Seeley JR, & Lewinsohn PM (1999). Life events and depression in adolescence: Relationship loss as a prospective risk factor for first onset of major depressive disorder. Journal of abnormal psychology, 108(4), 606. [DOI] [PubMed] [Google Scholar]
  45. Monroe SM, & Simons AD (1991). Diathesis-stress theories in the context of life stress research: implications for the depressive disorders. Psychological bulletin, 110(3), 406. [DOI] [PubMed] [Google Scholar]
  46. Nelson BD, Perlman G, Klein DN, Kotov R, & Hajcak G (2016). Blunted neural response to rewards as a prospective predictor of the development of depression in adolescent girls. American Journal of Psychiatry, 173(12), 1223–1230. [DOI] [PubMed] [Google Scholar]
  47. Nestler EJ, & Carlezon WA Jr (2006). The mesolimbic dopamine reward circuit in depression. Biological psychiatry, 59(12), 1151–1159. [DOI] [PubMed] [Google Scholar]
  48. Ong SH, Wickramaratne P, Tang M, & Weissman MM (2006). Early childhood sleep and eating problems as predictors of adolescent and adult mood and anxiety disorders. Journal of affective disorders, 96(1–2), 1–8. [DOI] [PubMed] [Google Scholar]
  49. Petersen AC, Crockett L, Richards M, & Boxer A (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of youth and adolescence, 17(2), 117–133. [DOI] [PubMed] [Google Scholar]
  50. Pizzagalli DA (2014). Depression, stress, and anhedonia: toward a synthesis and integrated model. Annual review of clinical psychology, 10, 393–423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Pizzagalli DA, Holmes AJ, Dillon DG, Goetz EL, Birk JL, Bogdan R, ... & Fava M (2009). Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. American Journal of Psychiatry, 166(6), 702–710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Pluess M, & Belsky J (2013). Vantage sensitivity: Individual differences in response to positive experiences. Psychological bulletin, 139(4), 901. [DOI] [PubMed] [Google Scholar]
  53. Proudfit GH (2015). The reward positivity: From basic research on reward to a biomarker for depression. Psychophysiology, 52(4), 449–459. [DOI] [PubMed] [Google Scholar]
  54. Roberts RE, & Duong HT (2013). Depression and insomnia among adolescents: a prospective perspective. Journal of affective disorders, 148(1), 66–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Roberts RE, & Duong HT (2014). The prospective association between sleep deprivation and depression among adolescents. Sleep, 37(2), 239–244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Rudolph KD, & Hammen C (1999). Age and gender as determinants of stress exposure, generation, and reactions in youngsters: A transactional perspective. Child development, 70(3), 660–677. [DOI] [PubMed] [Google Scholar]
  57. Siu YF, Chan S, Wong KM, & Wong WS (2012). The comorbidity of chronic pain and sleep disturbances in a community adolescent sample: prevalence and association with sociodemographic and psychosocial factors. Pain Medicine, 13(10), 1292–1303. [DOI] [PubMed] [Google Scholar]
  58. Speed BC, Nelson BD, Levinson AR, Perlman G, Klein DN, Kotov R, & Hajcak G (2018). Extraversion, neuroticism, and the electrocortical response to monetary rewards in adolescent girls. Biological psychology. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Stringaris A, Vidal-Ribas Belil P, Artiges E, Lemaitre H, Gollier-Briant F, Wolke S, ... & Fadai T (2015). The brain’s response to reward anticipation and depression in adolescence: Dimensionality, specificity, and longitudinal predictions in a community-based sample. American Journal of Psychiatry, 172(12), 1215–1223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Telzer EH, Fuligni AJ, Lieberman MD, & Galván A (2013). The effects of poor quality sleep on brain function and risk taking in adolescence. Neuroimage, 71, 275–283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Weinberg A, Liu H, Hajcak G, & Shankman SA (2015). Blunted neural response to rewards as a vulnerability factor for depression: Results from a family study. Journal of abnormal psychology, 124(4), 878. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Whitton AE, Treadway MT, & Pizzagalli DA (2015). Reward processing dysfunction in major depression, bipolar disorder and schizophrenia. Current opinion in psychiatry, 28(1), 7. [DOI] [PMC free article] [PubMed] [Google Scholar]

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