Blunted Reward Processing in Remitted Melancholic Depression

Abstract

Blunted reward response appears to be a trait-like marker of vulnerability for major depressive disorder (MDD). As such, it should be present in remitted individuals; however, depression is a heterogeneous syndrome. Reward-related impairments may be more pronounced in individuals with melancholic depression. The present study examined neural responses to rewards in remitted melancholic depression (rMD; n = 29), remitted nonmelancholic depression (rNMD; n = 56), and healthy controls (HC; n = 81). Event-related potentials to monetary gain and loss were recorded during a simple gambling paradigm. Relative to both the HC and the rNMD groups, who did not differ from one another, rMD was characterized by a blunted response to rewards. Moreover, the rMD and rNMD groups did not differ in course or severity of their past illnesses, or current depressive symptoms or functioning. Results suggest that blunted response to rewards may be a viable vulnerability marker for melancholic depression.

Keywords

reward processing melancholia vulnerability depression

Despite the prevalence and public health impact of major depressive disorder (MDD; Greenberg, Stiglin, Finkelstein, & Berndt, 1993; Mathers, Fat, & Boerma, 2008; Murray et al., 2013), few reliable mechanisms of vulnerability for the disorder have been identified, due, in part, to the heterogeneity of the syndrome (Cuthbert, 2014; Cuthbert & Insel, 2013; Helzer, Kraemer, & Krueger, 2006; Klein, 2008). Different symptoms and symptom clusters subsumed under the MDD diagnosis appear to have distinct etiologies (Day et al., 2015), courses (Angst, Gamma, Benazzi, Ajdacic, & Rössler, 2007; Lux & Kendler, 2010), and biological correlates (Pizzagalli et al., 2004; Shankman, Sarapas, & Klein, 2011). Examining more specific processes and symptom clusters within the category of depression may be useful in refining psychological and biological phenotypes and identifying vulnerability markers (Insel et al., 2010; Shankman & Gorka, 2015).

Dysfunctions in reward responding, a process that relies heavily on dopaminergic (DA) activity in the frontostriatal system (Berridge, 2007; Schultz et al., 1995), appear to play a central role in the pathophysiology of at least some expressions of depression (Hanson, Hariri, & Williamson, 2015; Romens et al., 2015). Blunted striatal response to rewards has been observed in currently depressed individuals (Forbes et al., 2009; Keedwell, Andrew, Williams, Brammer, & Phillips, 2005; Olino et al., 2011; Pizzagalli et al., 2009), and hypoactivation of this system appears to relate specifically to reduced hedonic capacity, and not other symptoms of depression or anxiety (Keedwell et al., 2005; Wacker, Dillon, & Pizzagalli, 2009). Furthermore, there is evidence that both adults (Henriques & Davidson, 2000; Pizzagalli, Jahn, & O’Shea, 2005; Steele, Kumar, & Ebmeier, 2007) and youth (Forbes et al., 2006; Forbes et al., 2009) with depression are less sensitive to the effect of rewards in shaping behavior.

Moreover, reward-related impairments appear to be relatively stable over time (Oquendo et al., 2004; Shankman et al., 2013), suggesting trait-like qualities. Consistent with this, depression-linked abnormalities in reward processing appear to be evident even in the absence of observable psychopathology: Children of depressed parents, who are at elevated risk for depression (Gotlib, Joormann, & Foland-Ross, 2014; Weissman et al., 2006), have shown blunted reward responding in the absence of observable symptoms (Gotlib et al., 2010; Kujawa, Proudfit, & Klein, 2014; Nelson et al., 2013). Moreover, reward-processing dysfunction can prospectively predict the onset of depression (Bress, Foti, Kotov, Klein, & Hajcak, 2013; Forbes, Shaw, & Dahl, 2007; Hanson et al., 2015; Rawal, Collishaw, Thapar, & Rice, 2013) and may mediate the association between early stressors and later depressive symptomatology (Hanson et al., 2015; Romens et al., 2015), suggesting a mechanistic role.

However, not all individuals who meet criteria for depression, or who are at risk for depression, show evidence of blunted reward responding (Chen, Eaton, Gallo, & Nestadt, 2000; Foti, Carlson, Sauder, & Proudfit, 2014; Shankman et al., 2011; Shankman, Klein, Tenke, & Bruder, 2007), suggesting the necessity of examining more specific subgroups and symptom clusters. In particular, although depression in general has been associated with DA-linked reward processing impairments, DA functioning may be most impaired in melancholic depression (Baumeister & Parker, 2012; Parker, 2007; Parker et al., 1995), a specifier within the MDD category. Indeed, the essential symptom of melancholia is pervasive anhedonia, or the inability to respond to positive events in the environment (Parker, 2007; Parker et al., 1995), and there is evidence that blunted response to rewards might be most pronounced in this subtype (Foti et al., 2014; Haslam & Beck, 1994; Klein, 1974; Leventhal & Rehm, 2005; Shankman et al., 2011). Biomarkers of reward dysfunction might therefore be present in individuals at risk for melancholic depression.

Remitted depressed individuals are helpful targets of research concerned with vulnerability markers, in that, although they are currently well, they remain vulnerable to relapse of illness (Horwath, Johnson, Klerman, & Weissman, 1992). Use of remitted depressed individuals thereby permits investigation of neural correlates of vulnerability, but mitigates potential confounding effects of symptom severity and mood states in currently depressed individuals. Neural response to reward observed during periods of remission may therefore provide useful information about trait-like vulnerabilities and the diathesis of the disorder (Jacobs et al., 2014). In fact, although there is evidence that some neural abnormalities normalize in remission (Bench, Frackowiak, & Dolan, 1995; Brody et al., 1999; Mayberg et al., 1999), persistent hypoactivation of reward networks has been observed in remitted depressed individuals (Dichter, Kozink, McClernon, & Smoski, 2012; McCabe, Cowen, & Harmer, 2009; Whitton et al., 2016), as have impairments in reinforcement learning (Pechtel, Dutra, Goetz, & Pizzagalli, 2013). On the other hand, other studies have reported normal hedonic capacity in remitted depression (Dichter et al., 2009; McFarland & Klein, 2009), or even enhanced reward anticipation (Ubl et al., 2015) suggesting state-linked abnormalities. Diagnostic heterogeneity may explain these mixed results.

The present study therefore sought to measure neural response to rewards in a sample of individuals with remitted melancholic and remitted nonmelancholic depression. The index of neural response to rewards used in the present study was the feedback negativity (FN), an event-related potential (ERP) component that is increasingly used in research on reward processing (Carlson, Foti, Mujica-Parodi, Harmon-Jones, & Hajcak, 2011; Foti et al., 2014; Weinberg, Luhmann, Bress, & Hajcak, 2012). The FN peaks approximately 250 to 300 ms at frontocentral recording sites following the presentation of feedback (Holroyd & Coles, 2002; Miltner, Braun, & Coles, 1997) and has typically been conceptualized as a negativity elicited by loss feedback that is absent following gain feedback. However, recent evidence suggests that the apparent differentiation in the ERP between gain and loss trials is driven by variation in a reward-related positivity (called the RewP; Proudfit, 2015) that is larger and more positive for rewards than nonrewards (Carlson et al., 2011; Foti et al., 2014; Foti, Weinberg, Dien, & Hajcak, 2011; Holroyd, Pakzad-Vaezi, & Krigolson, 2008). The magnitude of the RewP is also moderated by variation in genes governing the synaptic degradation of dopamine (Foti & Hajcak, 2012), and a larger RewP is associated with increased striatal response to rewards (Becker, Nitsch, Miltner, & Straube, 2014; Carlson et al., 2011; Foti et al., 2014).

A blunted RewP has also been observed repeatedly in individuals with current depression (Bress, Smith, Foti, Klein, & Hajcak, 2012; Foti et al., 2014; Foti & Hajcak, 2009; Liu et al., 2014), as well as in individuals vulnerable to depression (Foti, Kotov, Klein, & Hajcak, 2011; Kujawa et al., 2014; Kujawa et al., 2015; Weinberg, Liu, Hajcak & Shankman, 2015). A smaller RewP also appears to prospectively predict the onset of depression, even accounting for other known risk factors (Bress et al., 2013), suggesting that the blunted RewP not only may be a correlate of depression but also may represent a neural marker of vulnerability for depressive disorders.

To date, only one study has examined ERPs to loss and gain feedback in remitted depression—and found that individuals with remitted depression had a larger FN elicited by loss trials in a reinforcement learning task compared with controls (Santesso et al., 2008). However, this study did not examine ERPs to reward trials, nor did it account for diagnostic heterogeneity. Examination of the association between specific symptom clusters and the RewP may be particularly informative in light of a recent study in currently depressed individuals, which found that the blunted RewP related specifically to symptoms of melancholia, and was not associated with other depressive symptoms and specifiers (Foti et al., 2014).

The present study therefore sought to examine whether the blunted RewP might represent a state-independent vulnerability marker for melancholic, but not other forms of depression. We examined the magnitude of the RewP elicited in a simple gambling task in a sample of individuals with remitted MDD (melancholic type), remitted MDD (nonmelancholic type), and healthy controls (HC). We predicted that the magnitude of the RewP would be blunted in remitted melancholic depression relative to both controls and remitted nonmelancholic depression. To explore whether the blunted RewP was a state or trait characteristic, we also compared current symptoms and functioning between the two remitted depressed groups.

Methods

Participants

The participants in the current sample were selected from a larger sample of siblings (for other reports on this sample, see, e.g., Gorka, Liu, Klein, Daughters, & Shankman, 2015; Weinberg, Liu, Hajcak, & Shankman, 2015). We recruited participants between the ages of 18 and 30 from the community and area mental health clinics (via fliers, Internet postings, etc.) on the basis of symptoms of anxiety and depression. We used minimal symptom-based inclusion and exclusion criteria, and aimed to recruit a sample with a broad range of internalizing symptomatology. However, to ensure the clinical relevance of the sample, we also oversampled from individuals with severe psychopathology. Thus, the goal was recruit a sample with normally distributed internalizing symptoms but with a mean more severe than the mean of the general population. Prior to their involvement in the study, participants were screened via telephone using the Depression, Anxiety, and Stress Scale (Lovibond & Lovibond, 1995), a brief (21 items) measure of broad internalizing psychopathology (the measure was used to ensure that the sample had the earlier-mentioned distribution on internalizing symptoms). As manic and psychotic symptoms have been shown to be separable from internalizing disorders (Watson, 2005), subjects were excluded during screening if they had a personal or first-degree family history of a manic/hypomanic episode or psychotic symptoms, assessed via items from the Structured Clinical Interview for DSM–IV (SCID; First, Gibbon, Spitzer, & Williams, 1996). Participants were also excluded if they were unable to read or write English, had a history of head trauma with loss of consciousness, or were left-handed.

Participants were included in the current analyses if (a) they did not meet criteria for any current or lifetime anxiety, depressive, alcohol, substance, posttraumatic stress disorder (PTSD), and eating disorder assessed in the SCID (healthy controls; HC; n = 71) or (b) they met lifetime—but not current—criteria for MDD (n = 85). The remitted depressed group was then divided into individuals who met lifetime criteria for Diagnostic and Statistical Manual of Mental Disorders (DSM–IV) melancholic depression (rMD, n = 29), and individuals who did not meet criteria for melancholic depression (rNMD, n = 56). Of those with remitted nonmelancholic depression, 10 met criteria for atypical depression, and the remaining 46 met criteria for unspecified major depressive episodes. The final sample was 64.8% female, and was racially diverse (42.1% Caucasian American, 22.6% Hispanic, 12.6% African American, 15.1% Asian, 3.1% Middle Eastern, 0.6% Other, and 2.5% Mixed Race), well-educated (47.8% had completed at least some college education; 18.2% had completed 4 years of college), and relatively young (age M = 22.53, SD = 3.17). All procedures were approved by the University of Illinois at Chicago Institutional Review Board, and all research was carried out in accordance with the provisions of the World Medical Association Declaration of Helsinki.

Measures

All clinical diagnoses of current and lifetime anxiety, depressive, alcohol and substance, PTSD, and eating disorders were assessed via the SCID (First et al., 1996), which was administered to every participant. The SCID included items that assessed melancholic MDD using DSM–5 criteria. Current clinician-rated global assessment of functioning (GAF) was also made in the course of the SCID. Diagnosticians were trained to criterion on the SCID and supervised by a licensed clinical psychologist (S.A.S.). To assess prior episodes of depression, participants were asked to recall the worst past episode they had ever experienced. Current and lifetime diagnoses of substance use disorders (SUD; including alcohol use disorders) and anxiety disorders were also collected in the course of the SCID. In addition to diagnoses, interviewers dimensionally assessed functional impairment and subjective distress during the past depressive episode that the remitted depressed participants identified as their most severe. Interviewers made ratings for impairment in the domains of social, occupational, and daily life, as well as perceived distress, along a 9-point scale ranging from 0 (none) to 8 (severe). The anchors for the scale were adopted from the Anxiety Disorders Interview Schedule (Brown, DiNardo, Barlow, & DiNardo, 1994) in which a 2 or higher was clinically significant distress or impairment. Severity of worst past episode was calculated as the mean of these impairment ratings.

Current depression and anxiety symptoms (past two weeks) were also assessed in all participants using the expanded Inventory of Depression and Anxiety Symptoms (IDAS-II; Watson et al., 2007; Watson et al., 2012). We report here on the following 11 subscales associated with unipolar depression and DSM–5 anxiety disorders: General Depression, Dysphoria, Lassitude, Insomnia, Suicidality, Appetite Loss, Appetite Gain, Ill-Temper, Well-Being, Social Anxiety, and Panic.

Procedure

Participants completed a battery of tasks and the order was counterbalanced across subjects. Results from other tasks will be reported elsewhere (e.g., Gorka et al., 2015; Weinberg, Liu, & Shankman, 2015). The present task was administered on a Pentium class computer, using the stimulus presentation software Presentation (Neurobehavioral Systems, Inc.).

Task and materials

The reward task was a simple guessing task, which has been used in other studies concerned with reward processing (Foti, Weinberg, et al., 2011). The task consisted of 60 trials, presented in three blocks of 20. At the beginning of each trial, participants were presented with an image of two doors and were instructed to choose one door by clicking the left or right mouse button. The doors remained on the screen until the participant responded. Next, a fixation mark (+) appeared for 1,000 ms, and feedback was presented on the screen for 2,000 ms. Participants were told that they could either win $0.50 or lose $0.25 on each trial. A win was indicated by a green “↑,” and a loss was indicated by a red “↓.” Next, a fixation mark appeared for 1,500 ms and was followed by the message “Click for the next round,” which remained on the screen until the participant responded and the next trial began. Across the task, 30 win and 30 loss trials were presented in a random order.

Psychophysiological recording, data reduction, and analysis

Continuous EEG recordings were collected using an elastic cap and the ActiveTwo BioSemi system (BioSemi, Amsterdam, Netherlands). Sixty-four electrodes were used, based on the 10/20 system, as well as two electrodes on the right and left mastoids. Electrooculogram (EOG) generated from eye movements and eyeblinks was recorded using four facial electrodes: Horizontal eye movements (HEM) were measured via two electrodes located approximately 1 cm outside the outer edge of the right and left eyes. Vertical eye movements (VEM) and blinks were measured via one electrode placed approximately 1 cm below the left eye and electrode FP1. The data were digitized at a sampling rate of 1,024 Hz, using a low-pass fifth order sinc filter with –3 dB cutoff point at 208 Hz. Each active electrode was measured online with respect to a common mode sense (CMS) active electrode, located between PO3 and POz, producing a monopolar (nondifferential) channel. CMS forms a feedback loop with a paired driven right leg (DRL) electrode, located between POz and PO4, reducing the potential of the participants and increasing the common mode rejection rate (CMRR). Offline, all data were analyzed in Brain Vision Analyzer (BVA) and were referenced to the average of the left and right mastoids, and band-pass filtered with low and high cutoffs of 0.1 and 30 Hz, respectively. Eye-blink and ocular corrections were conducted per a modification of the Gratton, Coles, and Donchin (1983) algorithm, which accounts for both VEM and HEM.

A semiautomatic procedure was employed to detect and reject artifacts. The automatic criteria applied were a voltage step of more than 50.0 µV between sample points, a voltage difference of 175 µV or greater within any 400 ms window within a trial, and a voltage difference of .50 µV or less within 100 ms intervals. The data were then visually inspected and any remaining artifacts (e.g., slow-wave activity, blinks that were not adequately corrected) were rejected.

The EEG was segmented into 1,200 ms windows for each trial, beginning 200 ms before each response onset and continuing for 1,000 ms following feedback. A 200 ms window from –200 to 0 ms prior to feedback onset served as the baseline. The FN appears maximal around 300 ms at central sites; therefore, the time-window scored FN was scored as the average activity at electrode sites Cz and FCz, between 220 and 360 ms.

To examine potential associations between melancholia and ERPs elicited by feedback, we used the Mixed Models Linear procedure in SPSS version 20 to conduct repeated-measures analyses of variance to compare neural response to rewards and nonrewards. History of melancholic depression (0 = controls, 1 = nonmelancholic depression, 2 = melancholic depression), psychiatric medication usage (yes/no), and feedback condition (rewards vs. nonrewards) were fixed factors in a full factorial linear mixed model MANOVA design. Because there is evidence that gender (Kujawa et al., 2014), alcohol and/or substance use (e.g., Parvaz et al., 2012), and anxiety disorders (Guyer et al., 2012; Morris & Rottenberg, 2015) can also be associated with aberrations in reward processing, each of these was also entered as a fixed factor. Alcohol and/or substance use and anxiety disorders were scored as dichotomous variables indicating whether individuals met lifetime criteria (i.e., current or past) for the diagnoses. Because siblings violate the assumption of independent sampling, we used the MIXED procedure to account for sibling similarity. This provides an appropriate adjustment for degrees of freedom and thus statistical significance in each analysis. Restricted maximum likelihood (REML) estimation was utilized to account for missing data. REML yields unbiased estimates if data are missing at random (Roderick, Little, & Rubin, 1986), and produces estimates that agree with ANOVA estimates when data are balanced. Bonferroni post hoc comparisons were used to test for group mean differences (i.e., melancholic depression vs. controls, nonmelancholic depression vs. controls, melancholic depression vs. nonmelancholic depression).

Results

Participant characteristics

Means for current GAF, current Axis I diagnoses, and current symptoms endorsed on the IDAS-II are presented in Table 1. As indicated in Table 1, although both remitted depressed groups reported lower GAF and more current symptoms of depression, social anxiety, and panic than the HC group, the two remitted groups were within one standard deviation of the means for nonclinical samples reported in Watson and colleagues (2007). The two remitted depressed groups did not differ from one another on any variables related to current functioning, current or prior Axis I diagnoses, previous course of the depression, the severity of their worst episode, or time spent in remission (all p values > .20). The two remitted depressed groups also did not differ in lifetime SUD/AUD diagnoses (rMD n = 18, rNMD n = 24), or in lifetime diagnosis of anxiety disorder (rMD n = 5, rNMD n = 4).

Table 1.

Demographic Information, Performance Variables, Neural Responses, Means for Subscales of the Inventory of Depression and Anxiety Symptoms (IDAS-II), and Clinical Characteristics for the Three Groups

Category	Subcategory	Healthy controls (n = 81)	Nonmelancholic depression (n = 56)	Melancholic depression (n = 29)
Demographics
	Age	21.78 (2.85)	23.05 (3.26)	23.34 (3.34)
	Sex (% female)	60.5	69.6	57.7
ERPs (µV)	Mean (SD)
	Loss	8.92 (4.82)	9.64 (5.54)	8.21 (6.69)
	Gain	13.17 (5.25)^a	14.07 (6.85)^a	9.91 (7.66)^b
	Loss minus gain	−4.28 (3.69)^a	−4.61 (3.87)^a	−2.01 (3.35)^b
Current Functioning
	GAF, mean (SD)	85.19 (7.84)^a	64.39 (11.62)^b	60.07 (12.23)^b
	Psychiatric medication use (n)	1	4	5
IDAS Scales	Mean (SD)
	General depression	31.20 (7.35)^a	43.19 (14.02)^b	45.32 (15.12)^b
	Dysphoria	13.86 (4.26)^a	21.33 (8.30)^b	22.43 (9.29)^b
	Lassitude	10.30 (3.38)^a	14.33 (4.87)^b	14.43 (5.30)^b
	Insomnia	9.58 (3.81)^a	12.00 (5.58)^b	13.43 (5.15)^b
	Suicidality	6.18 (0.63)^a	6.83 (1.65)	7.79 (3.70)^b
	Appetite loss	3.89 (1.77)^a	5.50 (2.56)^b	5.07 (2.62)^b
	Appetite gain	3.80 (1.73)^a	4.72 (1.75)^b	4.68 (2.06)
	Ill temper	5.94 (2.27)^a	7.21 (2.62)^b	8.51 (4.20)^b
	Well-being	26.05 (6.66)	24.81 (6.25)	23.25 (6.82)
	Social anxiety	7.15 (1.45)^a	10.61 (4.60)^b	10.96 (4.75)^b
	Panic	9.14 (2.49)^a	11.76 (4.25)^b	12.20 (4.37)^b
Diagnoses	n
	Any current diagnosis	—	16	14
	Substance/alcohol use disorder	—	6	1
	Posttraumatic stress disorder	—	1	0
	Panic disorder	—	1	0
	Social anxiety disorder	—	3	5
	Specific phobia	—	3	5
	Obsessive-compulsive disorder	—	4	1
	Generalized anxiety disorder	—	5	1
Previous course of depression	Mean (SD)
Previous course of depression	# of MDD episodes	—	2.14 (1.37)	2.42 (1.45)
	MDD age of onset	—	18.8 (4.94)	16.08 (5.11)
	Severity of worst past episode	—	4.87 (1.58)	5.75 (1.16)
	Time in remission (months)	—	27.33 (26.19)	30.70 (37.99)

Note: If no superscripts appear, the groups did not differ significantly on the measure. Values with different superscripts were significantly different at p < .05 using post hoc Bonferroni comparisons or χ². ERP = event-related potential; GAF = global assessment of functioning; IDAS = Inventory of Depression and Anxiety Symptoms; MDD = major depressive disorder.

Association between neural response to rewards, loss, and melancholic depression

Figure 1 displays grand average response-locked ERPs at a pooling of Cz and FCz, where the nonreward minus reward difference was maximal across groups. Topographic maps from left to right for the HC, rNMD, and rMD groups, depicting voltage differences (in µV) across the scalp for nonreward minus reward feedback in the time window of the RewP, are presented in Figure 2. Average ERP values are listed in Table 1.

Fig. 1.

Stimulus-locked ERP waveforms at an average of electrode sites Cz and FCz for healthy controls (HC), remitted nonmelancholic depressed (rNMD), and melancholic depressed (rMD) groups. For each panel, stimulus onset occurred at 0 ms. Per ERP convention, negative voltages are plotted up.

Fig. 2.

Scalp topographies representing the reward-related positivity (RewP). These maps are derived from the average difference between conditions (nonreward minus reward response) from 220 to 360 ms and represent the Δ RewP for healthy controls, nonmelancholic depressed, and melancholic depressed groups.

Gender significantly predicted neural response to feedback in that women (M = 12.08, SE = 0.94; 95% CI = 10.22, 13.94) had a more positive overall response (i.e., collapsing across reward and nonreward) than men (M = 9.07, SE = 1.19; 95% CI = 6.71, 11.42), F(1, 136.64) = 7.60, p < .01. However, gender did not significantly interact with feedback type to determine neural response, F(1, 137.00) < 1. In addition, feedback type had a significant effect on neural response, F(1, 137.00) = 95.74, p < .001, such that rewards (M = 12.41, SE = 0.98; 95% CI = 10.49, 14.34) elicited a larger positivity than nonrewards (M = 8.73, SE = 0.91; 95% CI = 6.94, 10.53). Psychiatric medications were not significantly associated with neural response, F(1, 136.00) < 1. Lifetime diagnosis of SUD or AUD, F(1, 136.84) < 1, anxiety disorders, F(1, 136.31) = 1.88, p = .17, and melancholic depression, F(2, 136.41) = 2.36, p = .10, did not significantly predict the overall neural response (i.e., collapsing across feedback types). Feedback type did not interact with lifetime diagnosis of SUD/AUD, F(1, 137.00) < 1, or anxiety, F(1, 136.84) < 1, to determine neural response.

However, the effect of feedback was significantly qualified by an interaction with melancholic group, F(2, 137.00) = 5.71, p < .01. This interaction was decomposed by two separate univariate tests comparing groups on neural response to gain and loss separately. The three groups did not differ significantly in the neural response to loss, F(2, 163.00) < 1, but there were significant group differences in neural response to reward, F(2, 163.00) = 4.43, p < .05. Post hoc pairwise Bonferroni comparisons indicate that rMD individuals had a significantly blunted neural response to rewards compared with both the HC, D = 3.49, SE = 1.37, p < .05, and rNMD depressed groups, D = 4.16, SE = 1.44, p < .05. The rNMD group did not significantly differ from the HC group in their neural response to rewards, D = 0.68, SE = 1.10, p = 1.00.¹

To examine whether these effects could be attributed to residual symptoms of anhedonia or other symptoms of depression, we repeated the analyses outlined earlier, excluding factors that did not significantly relate to neural response (i.e., AUD/SUD, psychiatric medications, anxiety disorders), and controlling first for current levels of positive affect (PA; IDAS-II well-being), and then for current levels of depressive symptoms (IDAS-II depression). After adjusting for PA, the results were as those presented earlier: We observed a main effect of gender, F(1, 141.00) = 4.88, p < .05, as well as a main effect of feedback type, F(1, 142.00) = 104.70, p < .001. There was no significant main effect of melancholic group, F(2, 141.26) = 1.87, p = .16, or of current levels of PA, F(1, 140.12) < 1. PA also did not interact with feedback type, F(1, 141.00) < 1. However, as earlier, the effect of feedback was qualified by an interaction with melancholic group, F(2, 142.00) = 6.68, p < .01.

The effects also held after adjusting for other current symptoms of depression. We observed a main effect of gender, F(1, 140.00) = 6.39, p < .01, as well as a main effect of feedback type, F(1, 141.00) = 19.54, p < .001. In addition, there was a significant main effect of current depressive symptoms, F(1, 140.25) = 8.66, p < .05, as well as of melancholic group, F(2, 140.12) = 2.98, p < .05. However, residual depressive symptoms did not significantly interact with feedback type, F(1, 141.00) = 3.21, p = .09. And finally, even after adjusting for residual symptoms of depression, we observed a significant interaction between feedback type and melancholic group, F(2, 141.00) = 5.49, p < .01.

Discussion

The present study was the first to examine the impact of diagnostic heterogeneity on neural response to rewards in remitted depression. Consistent with our hypotheses, we found that individuals with rMD were characterized by a blunted neural response to rewards. These results suggest that blunted reward responsiveness persists in individuals with a history of melancholic MDD even several years after the last major depressive episode. These group-level results also remained significant after adjusting for residual current depressive symptoms in the two depressed groups and are consistent with previous evidence that impaired reward processing may be a trait marker that is insensitive to state-linked fluctuations (e.g., Foti, Kotov, et al., 2011; Kujawa et al., 2014; Kujawa et al., 2015; Weinberg, Liu, Hajcak, & Shankman, 2015; Whitton et al., 2016). Research focused on blunted reward response in remitted depression might therefore be helpful in targeting mechanisms of future relapse.

The present study also used individuals with remitted nonmelancholic depression as a psychiatric comparison group, allowing us to control not just for history of psychopathology, but also course and severity of depressive episodes. As hypothesized, individuals in the rNMD group did not differ from never-depressed controls in their response to rewards. Moreover, individuals in the rMD group exhibited a blunted RewP relative to individuals in the rNMD group, and this blunting was observed despite the fact that the rMD and rNMD groups did not differ in terms of symptom severity, impairment, or comorbidity at the time of the visit. Moreover, the magnitude of the RewP was not significantly associated with self-reported symptoms. These data suggest that the blunted RewP we observed was not a state-effect reflecting the severity of subclinical symptoms of depression at the time of the assessment. The two remitted depressed groups also did not differ in the course, number, or severity of their previous depressive episodes, suggesting the blunted RewP in the melancholic depression group does not reflect the residual effect of a more severe course or episode.

Because the individuals in this study had already experienced at least one major depressive episode, these data alone do not suggest that the blunted RewP is an index of vulnerability for the first onset of melancholic depression. It is possible that particularities of prior melancholic illness—or prior psychological and pharmacological treatments—may have had an enduring influence on the magnitude of the RewP. For instance, there is some evidence that individuals with melancholic depression both experience higher levels of stress and are more sensitive to stress compared with individuals with other forms of depression (Harkness & Monroe, 2002, 2006), and stress has been shown to attenuate the RewP (Bogdan, Santesso, Fagerness, Perlis, & Pizzagalli, 2011). However, in combination with previous evidence that a blunted RewP is evident in vulnerable but asymptomatic individuals (Foti, Kotov, et al., 2011; Kujawa et al., 2014; Kujawa et al., 2015; Weinberg, Liu, Hajcak, & Shankman, 2015) and can prospectively predict the onset of depression (Bress et al., 2013), the results we present here suggest the blunted RewP is likely not merely a “scar” resulting from prior psychological distress. Future studies might examine the ways in which significant life stressors might influence the development of the RewP and depression, as well as the potential ways in which vulnerability reflected in the blunted RewP might interact with life stressors to predict the onset and course of depressive illness.

The present results may also be useful in explaining previous inconsistencies in the literature. Although some studies have found persistent reward hyporesponsiveness in euthymic remitted depressed individuals (Dichter et al., 2012; McCabe et al., 2009; Pechtel et al., 2013; Whitton et al., 2016), not all have (Dichter et al., 2009; McFarland & Klein, 2009; Ubl et al., 2015). However, these studies have typically not examined subtypes of depression. Combined with other studies demonstrating that alterations in the magnitude of the RewP are present in at-risk individuals (Bress et al., 2013; Kujawa et al., 2014), the results of the present study suggest this trait-like blunting may specifically reflect risk for features of melancholic depression, and particularly anhedonia (Foti et al., 2014).

However, this is not to say that these trait-like vulnerability markers are immutable. An important consideration for future research will be whether observed abnormalities in the activity of reward processing systems in affected and at-risk individuals can be modified, and whether modification of the activity of this system could be clinically meaningful. For instance, Dichter and colleagues (2009) administered a brief behavioral activation treatment therapy—which aims to increase engagement with pleasant events in the environment—to a sample of adults with MDD and found that neural response to rewards normalized. Therapies that aim to specifically modify attention to and sustained processing of rewards might be more effective at treating melancholic depression or preventing relapse in individuals in remission (Browning, Holmes, Charles, Cowen, & Harmer, 2012; McMakin, Siegle, & Shirk, 2011).

Limitations of the study suggest directions for future research. First, the individuals in this sample were young adults and relatively psychologically healthy, and may not be representative of MDD or remitted MDD as a whole. The study also used a cross-sectional design and relied on retrospective report of the worst episode of past depression. Given that some participants were reporting on a previous episode several years after it occurred, it is possible that some symptoms and the degree to which they were experienced are imperfectly recalled. Moreover, we would note here that the course of depression can be rather heterotypic over time and that melancholic features assessed in a single worst past episode do not perfectly predict future melancholic episodes (e.g., Melartin et al., 2004). In the current study, we assessed the most severe previous episode, but it is possible that some individuals in the rNMD group had met criteria for melancholic depression while in a less severe episode. It is also possible that some of the individuals in the rMD group will not meet criteria for melancholia again, whereas some in the rNMD group will. All of these limitations suggest that prospective studies in vulnerable populations will be necessary to identify the stability of the effects observed in the current study, as well as possible contributions a blunted RewP might make to the onset of depression. Finally, the study did not include currently depressed comparison groups, which might be useful in further clarifying state versus trait influences.

In conclusion, the present study found that melancholic depression was specifically related to a blunted RewP and, furthermore, that this blunted RewP was evident even in the absence of clinically significant levels of current pathology. Combined, this suggests the blunted RewP may be a viable vulnerability marker for a particularly pernicious subtype of depression (Angst et al., 2007). The RewP may therefore be a valuable measure in future studies that seek to identify vulnerable individuals, and may be helpful in informing prevention efforts and tracking treatment response.

Footnotes

Declaration of Conflicting Interests

The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

Funding

This work was supported by National Institute of Mental Health Grants R01 MH098093 to Stewart A. Shankman.

Notes

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