Childhood sexual abuse (CSA) and emotional maltreatment are salient risk factors for the development of major depressive disorder (MDD) in women. However, the type- and timing-specific effects of emotional maltreatment experienced during adolescence on future depressive symptomatology in women with CSA have not been explored. The goal of this study was to fill this gap.

In total, 203 women (ages 20–32) with current depressive symptoms and CSA (MDD/CSA), remitted depressive symptoms and CSA (rMDD/CSA), and current depressive symptoms without CSA (MDD/no CSA) were recruited from the community and completed self-report measures. Depressive symptoms were assessed using the Beck Depression Inventory (BDI-II) and a detailed maltreatment history was collected using the Maltreatment and Abuse Chronology of Exposure (MACE). Differences in maltreatment exposure characteristics, including multiplicity and severity of maltreatment, as well as the chronologies of emotional maltreatment subtypes were compared among groups. A random forest machine-learning algorithm was utilized to assess the impact of exposure to emotional maltreatment subtypes at specific ages on current depressive symptoms.

MDD/CSA women reported greater prevalence and severity of emotional maltreatment relative to rMDD/CSA and MDD/no CSA women [F(2,196) = 9.33, p < 0.001], specifically from ages 12 to 18. The strongest predictor of current depressive symptoms was parental verbal abuse at age 18 for both MDD/CSA women (variable importance [VI] = 1.08, p = 0.006) and MDD/no CSA women (VI = 0.68, p = 0.004).

Targeting emotional maltreatment during late adolescence might prove beneficial for future intervention efforts for MDD following CSA.

Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians.

In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance.

Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12.

Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.

Cognitive distancing is an emotion regulation strategy commonly used in psychological treatment of various mental health disorders, but its therapeutic mechanisms are unknown.

935 participants completed an online reinforcement learning task involving choices between pairs of symbols with differing reward contingencies. Half (49.1%) of the sample was randomised to a cognitive self-distancing intervention and were trained to regulate or ‘take a step back’ from their emotional response to feedback throughout. Established computational (Q-learning) models were then fit to individuals' choices to derive reinforcement learning parameters capturing clarity of choice values (inverse temperature) and their sensitivity to positive and negative feedback (learning rates).

Cognitive distancing improved task performance, including when participants were later tested on novel combinations of symbols without feedback. Group differences in computational model-derived parameters revealed that cognitive distancing resulted in clearer representations of option values (estimated 0.17 higher inverse temperatures). Simultaneously, distancing caused increased sensitivity to negative feedback (estimated 19% higher loss learning rates). Exploratory analyses suggested this resulted from an evolving shift in strategy by distanced participants: initially, choices were more determined by expected value differences between symbols, but as the task progressed, they became more sensitive to negative feedback, with evidence for a difference strongest by the end of training.

Adaptive effects on the computations that underlie learning from reward and loss may explain the therapeutic benefits of cognitive distancing. Over time and with practice, cognitive distancing may improve symptoms of mental health disorders by promoting more effective engagement with negative information.

Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect in adolescents is an open question.

Adolescent participants (n = 22; ages 13–18) with low to high levels of depressive symptoms were followed for 15 weeks using a combination of ecological momentary assessments (EMAs) and continuously collected passive smartphone sensor data. EMAs probed negative emotional states (i.e. anger, sadness and anxiety) 2–3 times per day every other week throughout the study (total: 1145 EMA measurements). Smartphone accelerometer, location and device state data were collected to derive 14 discrete estimates of behavior, including activity level, percentage of time spent at home, sleep onset and duration, and phone usage.

A personalized ensemble machine learning model derived from smartphone sensor data outperformed other statistical approaches (e.g. linear mixed model) and predicted states of elevated anger and anxiety with acceptable discrimination ability (area under the curve (AUC) = 74% and 71%, respectively), but demonstrated more modest discrimination ability for predicting states of high sadness (AUC = 66%).

To the extent that smartphone data could provide reasonably accurate real-time predictions of states of high negative affect in teens, brief ‘just-in-time’ interventions could be immediately deployed via smartphone notifications or mental health apps to alleviate these states.

Major depressive disorder (MDD) is a highly prevalent psychiatric condition, yet many patients do not receive adequate treatment. Novel and highly scalable interventions such as internet-based cognitive-behavioral-therapy (iCBT) may help to address this treatment gap. Anhedonia, a hallmark symptom of MDD that refers to diminished interest and ability to experience pleasure, has been associated with reduced reactivity in a neural reward circuit that includes medial prefrontal and striatal brain regions. Whether iCBT can reduce anhedonia severity in MDD patients, and whether these therapeutic effects are accompanied by enhanced reward circuit reactivity has yet to be examined.

Fifty-two MDD patients were randomly assigned to either 10-week iCBT (n = 26) or monitored attention control (MAC, n = 26) programs. All patients completed pre- and post-treatment assessments of anhedonia (Snaith–Hamilton Pleasure Scale; SHAPS) and reward circuit reactivity [monetary incentive delay (MID) task during functional magnetic resonance imaging (fMRI)]. Healthy control participants (n = 42) also underwent two fMRI scans while completing the MID task 10 weeks apart.

Both iCBT and MAC groups exhibited a reduction in anhedonia severity post-treatment. Nevertheless, only the iCBT group exhibited enhanced nucleus accumbens (Nacc) and subgenual anterior cingulate cortex (sgACC) activation and functional connectivity from pre- to post-treatment in response to reward feedback. Enhanced Nacc and sgACC activations were associated with reduced anhedonia severity following iCBT treatment, with enhanced Nacc activation also mediating the reduction in anhedonia severity post-treatment.

These findings suggest that increased reward circuit reactivity may contribute to a reduction in anhedonia severity following iCBT treatment for depression.

Anhedonia is a core symptom of depression that predicts worse treatment outcomes. Dysfunction in neural reward circuits is thought to contribute to anhedonia. However, whether laboratory-based assessments of anhedonia and reward-related neural function translate to adolescents' subjective affective experiences in real-world contexts remains unclear.

We recruited a sample of adolescents (n = 82; ages 12–18; mean = 15.83) who varied in anhedonia and measured the relationships among clinician-rated and self-reported anhedonia, behaviorally assessed reward learning ability, neural response to monetary reward and loss (as assessed with functional magnetic resonance imaging), and repeated ecological momentary assessment (EMA) of positive affect (PA) and negative affect (NA) in daily life.

Anhedonia was associated with lower mean PA and higher mean NA across the 5-day EMA period. Anhedonia was not related to impaired behavioral reward learning, but low PA was associated with reduced nucleus accumbens response during reward anticipation and reduced medial prefrontal cortex (mPFC) response during reward outcome. Greater mean NA was associated with increased mPFC response to loss outcome.

Traditional laboratory-based measures of anhedonia were associated with lower subjective PA and higher subjective NA in youths' daily lives. Lower subjective PA and higher subjective NA were associated with decreased reward-related striatal functioning. Higher NA was also related to increased mPFC activity to loss. Collectively, these findings demonstrate that laboratory-based measures of anhedonia translate to real-world contexts and that subjective ratings of PA and NA may be associated with neural response to reward and loss.

Major depressive disorder (MDD) is often accompanied by changes in appetite and weight. Prior task-based functional magnetic resonance imaging (fMRI) findings suggest these MDD phenotypes are associated with altered reward and interoceptive processing.

Using resting-state fMRI data, we compared the fractional amplitude of low-frequency fluctuations (fALFF) and seed-based connectivity (SBC) among hyperphagic (n = 77), hypophagic (n = 66), and euphagic (n = 42) MDD groups and a healthy comparison group (n = 38). We examined fALFF and SBC in a mask restricted to reward [nucleus accumbens (NAcc), putamen, caudate, ventral pallidum, and orbitofrontal cortex (OFC)] and interoceptive (anterior insula and hypothalamus) regions and also performed exploratory whole-brain analyses. SBC analyses included as seeds the NAcc and also regions demonstrating group differences in fALFF (i.e. right lateral OFC and right anterior insula). All analyses used threshold-free cluster enhancement.

Mask-restricted analyses revealed stronger fALFF in the right lateral OFC, and weaker fALFF in the right anterior insula, for hyperphagic MDD v. healthy comparison. We also found weaker SBC between the right lateral OFC and left anterior insula for hyperphagic MDD v. healthy comparison. Whole-brain analyses revealed weaker fALFF in the right anterior insula, and stronger SBC between the right lateral OFC and left precentral gyrus, for hyperphagic MDD v. healthy comparison. Findings were no longer significant after controlling for body mass index, which was higher for hyperphagic MDD.

Our results suggest hyperphagic MDD may be associated with altered activity in and connectivity between interoceptive and reward regions.

The association between major depressive disorder and motivation to invest cognitive effort for rewards is unclear. One reason might be that prior tasks of cognitive effort-based decision-making are limited by potential confounds such as physical effort and temporal delay discounting.

To address these interpretive challenges, we developed a new task – the Cognitive Effort Motivation Task – to assess one's willingness to exert cognitive effort for rewards. Cognitive effort was manipulated by varying the number of items (1, 2, 3, 4, 5) kept in spatial working memory. Twenty-six depressed patients and 44 healthy controls went through an extensive learning session where they experienced each possible effort level 10 times. They were then asked to make a series of choices between performing a fixed low-effort-low-reward or variable higher-effort-higher-reward option during the task.

Both groups found the task more cognitively (but not physically) effortful when effort level increased, but they still achieved ⩾80% accuracy on each effort level during training and >95% overall accuracy during the actual task. Computational modelling revealed that a parabolic model best accounted for subjects' data, indicating that higher-effort levels had a greater impact on devaluing rewards than lower levels. These procedures also revealed that MDD patients discounted rewards more steeply by effort and were less willing to exert cognitive effort for rewards compared to healthy participants.

These findings provide empirical evidence to show, without confounds of other variables, that depressed patients have impaired cognitive effort motivation compared to the general population.

Social anxiety disorder (SAD) is common, first-line treatments are often only partially effective, and reliable predictors of treatment response are lacking. Here, we assessed resting state functional connectivity (rsFC) at pre-treatment and during early treatment as a potential predictor of response to a novel attention bias modification procedure, gaze-contingent music reward therapy (GC-MRT).

Thirty-two adults with SAD were treated with GC-MRT. rsFC was assessed with multi-voxel pattern analysis of fMRI at pre-treatment and after 2–3 weeks. For comparison, 20 healthy control (HC) participants without treatment were assessed twice for rsFC over the same time period. All SAD participants underwent clinical evaluation at pre-treatment, early-treatment (week 2–3), and post-treatment.

SAD and depressive symptoms improved significantly from pre-treatment to post-treatment. After 2–3 weeks of treatment, decreased connectivity between the executive control network (ECN) and salience network (SN), and increased connectivity within the ECN predicted improvement in SAD and depressive symptoms at week 8. Increased connectivity between the ECN and default mode network (DMN) predicted greater improvement in SAD but not depressive symptoms at week 8. Connectivity within the DMN decreased significantly after 2–3 weeks of treatment in the SAD group, while no changes were found in HC over the same time interval.

We identified early changes in rsFC during a course of GC-MRT for SAD that predicted symptom change. Connectivity changes within the ECN, ECN-DMN, and ECN-SN may be related to mechanisms underlying the clinical effects of GC-MRT and warrant further study in controlled trials.

Depression and insomnia commonly co-occur. Yet, little is known about the mechanisms through which insomnia influences depression. Recent research and theory highlight reward system dysfunction as a potential mediator of the relationship between insomnia and depression. This study is the first to examine the impact of insomnia on reward learning, a key component of reward system functioning, in clinical depression.

The sample consisted of 72 veterans with unipolar depression who endorsed sleep disturbance symptoms. Participants completed the Structured Clinical Interview for DSM-IV, self-report measures of insomnia, depression, and reward processing, and a previously validated signal detection task (Pizzagalli et al., 2005, Biological Psychiatry, 57(4), 319–327). Trial-by-trial response bias (RB) estimates calculated for each of the 200 task trials were examined using linear mixed-model analyses to investigate change in reward learning.

Findings demonstrated diminished rate and magnitude of reward learning in the Insomnia group relative to the Hypersomnia/Mixed Symptom group across the task. Within the Insomnia group, participants with more severe insomnia evidenced the lowest rates of reward learning, with increased RB across the task with decreasing insomnia severity.

Among individuals with depression, insomnia is associated with decreased ability to learn associations between neutral stimuli and rewarding outcomes and/or modify behavior in response to differential receipt of reward. This attenuated reward learning may contribute to clinically meaningful decreases in motivation and increased withdrawal in this comorbid group. Results extend existing theory by highlighting impairments in reward learning specifically as a potential mediator of the association between insomnia and depression.

Treatment for major depressive disorder (MDD) is imprecise and often involves trial-and-error to determine the most effective approach. To facilitate optimal treatment selection and inform timely adjustment, the current study investigated whether neurocognitive variables could predict an antidepressant response in a treatment-specific manner.

In the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial, outpatients with non-psychotic recurrent MDD were first randomized to an 8-week course of sertraline selective serotonin reuptake inhibitor or placebo. Behavioral measures of reward responsiveness, cognitive control, verbal fluency, psychomotor, and cognitive processing speeds were collected at baseline and week 1. Treatment responders then continued on another 8-week course of the same medication, whereas non-responders to sertraline or placebo were crossed-over under double-blinded conditions to bupropion noradrenaline/dopamine reuptake inhibitor or sertraline, respectively. Hamilton Rating for Depression scores were also assessed at baseline, weeks 8, and 16.

Greater improvements in psychomotor and cognitive processing speeds within the first week, as well as better pretreatment performance in these domains, were specifically associated with higher likelihood of response to placebo. Moreover, better reward responsiveness, poorer cognitive control and greater verbal fluency were associated with greater likelihood of response to bupropion in patients who previously failed to respond to sertraline.

These exploratory results warrant further scrutiny, but demonstrate that quick and non-invasive behavioral tests may have substantial clinical value in predicting antidepressant treatment response.

This is the first report on the association between trauma exposure and depression from the Advancing Understanding of RecOvery afteR traumA(AURORA) multisite longitudinal study of adverse post-traumatic neuropsychiatric sequelae (APNS) among participants seeking emergency department (ED) treatment in the aftermath of a traumatic life experience.

We focus on participants presenting at EDs after a motor vehicle collision (MVC), which characterizes most AURORA participants, and examine associations of participant socio-demographics and MVC characteristics with 8-week depression as mediated through peritraumatic symptoms and 2-week depression.

Eight-week depression prevalence was relatively high (27.8%) and associated with several MVC characteristics (being passenger v. driver; injuries to other people). Peritraumatic distress was associated with 2-week but not 8-week depression. Most of these associations held when controlling for peritraumatic symptoms and, to a lesser degree, depressive symptoms at 2-weeks post-trauma.

These observations, coupled with substantial variation in the relative strength of the mediating pathways across predictors, raises the possibility of diverse and potentially complex underlying biological and psychological processes that remain to be elucidated in more in-depth analyses of the rich and evolving AURORA database to find new targets for intervention and new tools for risk-based stratification following trauma exposure.

Cigarette smoking is more prevalent among individuals with psychiatric disorders than the general population. Obsessive-compulsive disorder (OCD) may be an intriguing exception, although no recent study has investigated this hypothesis in OCD patients. Moreover, it is unknown whether reduced smoking rates are present in unaffected first-degree relatives of OCD patients.

We assessed smoking prevalence in adults with OCD and unaffected parents of youth with OCD (PYOCD). To this end, 113 adults with OCD completed online questionnaires assessing symptom severity and smoking status. Smoking status was obtained from an independent sample of 210 PYOCD assessed for psychiatric diagnoses.

Smoking prevalence rates in adults with OCD (13.3%; n = 15) and PYOCD (9.5%; n = 20) samples were significantly lower than those found in representative samples of the general population (19–24%, all P < .001) and Axis I disorders (36–64%; all P < .001). There were no smokers in the adult OCD subset without clinically significant depressive symptoms (n = 54).

Low prevalence of smoking in OCD may be familial and unique among psychiatric disorders, and might represent a possible state-independent OCD marker. Hypotheses concerning the uncharacteristically low prevalence rates are discussed with relation to OCD phenomenology and pathophysiology.

Glucagon-like peptide-1 receptors (GLP-1Rs) are widely expressed in the brain. Evidence suggests that they may play a role in reward responses and neuroprotection. However, the association of GLP-1R with anhedonia and depression diagnosis has not been studied. Here, we examined the association of GLP-1R polymorphisms with objective and subjective measures of anhedonia, as well as depression diagnosis.

Objective [response bias assessed by the probabilistic reward task (PRT)] and subjective [Snaith-Hamilton Pleasure Scale (SHAPS)] measures of anhedonia, clinical variables and DNA samples were collected from 100 controls and 164 patients at McLean Hospital. An independent sample genotyped as part of the Psychiatric Genomics Consortium (PGC) was used to study the effect of putative GLP-1R polymorphisms linked to response bias in PRT on depression diagnosis.

The C allele in rs1042044 was significantly associated with increased PRT response bias, when controlling for age, sex, case-control status and PRT discriminability. AA genotype of rs1042044 showed higher anhedonia phenotype based on SHAPS scores. However, analysis of PGC major depressive disorder data showed no association between rs1042044 and depression diagnosis.

Findings suggest a possible association of rs1042044 with anhedonia but no association with depression diagnosis.

Preclinical and human studies suggest an association between chronic inflammation and the development of depressive behaviors. This is proposed to occur through downstream effects of inflammatory cytokines on neuroplasticity, neurogenesis and neurotransmitter function, although the neural correlates remain poorly understood in humans.

In Study 1, structural magnetic resonance imaging and serum inflammatory cytokine data were analyzed from 53 psychiatrically healthy female participants. Correlational analyses were conducted between interleukin-6 (IL-6) and volume in a priori regions implicated in the pathophysiology of major depressive disorder (MDD). In Study 2, medical data [including serum inflammatory acute phase reactants (C-reactive protein)] were analyzed for 12 589 participants. Participants were classified as having (n = 2541) v. not having (n = 10 048) probable lifetime MDD using phenotypes derived using machine-learning approaches. Non-parametric analyses compared inflammation between groups, whereas regression analyses probed whether inflammation predicted probable MDD classification while accounting for other variables.

In Study 1, significant negative correlations emerged between IL-6 and hippocampal, caudate, putamen and amygdalar volume. In Study 2, the MDD group showed a higher probability of elevated inflammation than the non-MDD group. Moreover, elevated inflammation was a significant predictor of probable MDD classification.

Findings indicate that inflammation is cross-sectionally related to reduced volume in brain regions implicated in MDD phenotypes among a sample of psychiatrically healthy women, and is associated with the presence of probable MDD in a large clinical dataset. Future investigations may identify specific inflammatory markers predicting first MDD onset.

Cognitive deficits in depressed adults may reflect impaired decision-making. To investigate this possibility, we analyzed data from unmedicated adults with Major Depressive Disorder (MDD) and healthy controls as they performed a probabilistic reward task. The Hierarchical Drift Diffusion Model (HDDM) was used to quantify decision-making mechanisms recruited by the task, to determine if any such mechanism was disrupted by depression.

Data came from two samples (Study 1: 258 MDD, 36 controls; Study 2: 23 MDD, 25 controls). On each trial, participants indicated which of two similar stimuli was presented; correct identifications were rewarded. Quantile-probability plots and the HDDM quantified the impact of MDD on response times (RT), speed of evidence accumulation (drift rate), and the width of decision thresholds, among other parameters.

RTs were more positively skewed in depressed v. healthy adults, and the HDDM revealed that drift rates were reduced—and decision thresholds were wider—in the MDD groups. This pattern suggests that depressed adults accumulated the evidence needed to make decisions more slowly than controls did.

Depressed adults responded slower than controls in both studies, and poorer performance led the MDD group to receive fewer rewards than controls in Study 1. These results did not reflect a sensorimotor deficit but were instead due to sluggish evidence accumulation. Thus, slowed decision-making—not slowed perception or response execution—caused the performance deficit in MDD. If these results generalize to other tasks, they may help explain the broad cognitive deficits seen in depression.

Depression has been associated with abnormalities in neural underpinnings of Reward Learning (RL). However, inconsistencies have emerged, possibly owing to medication effects. Additionally, it remains unclear how neural RL signals relate to real-life behaviour. The current study, therefore, examined neural RL signals in young, mildly to moderately depressed – but non-help-seeking and unmedicated – individuals and how these signals are associated with depressive symptoms and real-life motivated behaviour.

Individuals with symptoms along the depression continuum (n = 87) were recruited from the community. They performed an RL task during functional Magnetic Resonance Imaging and were assessed with the Experience Sampling Method (ESM), completing short questionnaires on emotions and behaviours up to 10 times/day for 15 days. Q-learning model-derived Reward Prediction Errors (RPEs) were examined in striatal areas, and subsequently associated with depressive symptoms and an ESM measure capturing (non-linearly) how anticipation of reward experience corresponds to actual reward experience later on.

Significant RPE signals were found in the striatum, insula, amygdala, hippocampus, frontal and occipital cortices. Region-of-interest analyses revealed a significant association between RPE signals and (a) self-reported depressive symptoms in the right nucleus accumbens (b = −0.017, p = 0.006) and putamen (b = −0.013, p = .012); and (b) the quadratic ESM variable in the left (b = 0.010, p = .010) and right (b = 0.026, p = 0.011) nucleus accumbens and right putamen (b = 0.047, p < 0.001).

Striatal RPE signals are disrupted along the depression continuum. Moreover, they are associated with reward-related behaviour in real-life, suggesting that real-life coupling of reward anticipation and engagement in rewarding activities might be a relevant target of psychological therapies for depression.

Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits.

Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics.

Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58).

A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.