Complex Emotion Dynamics Contribute to the Prediction of Depression: A Machine Learning and Time Series Feature Extraction Approach.

Emotion dynamics have demonstrated mixed ability to predict depressive symptoms and outperform traditional metrics like the mean and standard deviation of emotion reports. Here, we expand the types of emotion dynamic features used in prior work and apply a machine learning algorithm to predict depression symptoms. We obtained seven ecological momentary assessment (EMA) studies from previous work on depression and emotion dynamics (N = 890). These studies measured self-reported sadness, positive affect, and negative affect 5 to 10 times per day for 7 to 21 days (schedule varied across studies). These data were fed through a feature extraction routine to generate hundreds of emotion dynamic features. A gradient boosting machine (GBM) using all available emotion dynamics features was the best of all models assessed. This model's out-of-sample prediction (R 2 pred) for depression severity ranged from .20 to .44 depending on EMA interpolation method and samples included in the analysis. It also explained significantly more variance than a benchmark model of individuals' mean emotion ratings over the assessment period, R 2 pred = .089. Comprehensive feature mining of emotion dynamics obtained during EMA may be necessary to identify processes that predict depression symptoms beyond mean emotion ratings.

The Evaluation of a Web-Based Intervention (Deprexis) to Decrease Depression and Restore Functioning in Veterans: Protocol for a Randomized Controlled Trial.

BACKGROUND: Depressive symptoms are common in veterans, and the presence of these symptoms increases disability as well as suicidal thoughts and behaviors. However, there is evidence that these symptoms often go untreated. Intervening before symptoms become severe and entrenched is related to better long-term outcomes, including improved functioning and less disease chronicity. Computer-delivered interventions may be especially appropriate for those veterans with mild to moderate depressive symptoms, because these interventions can require fewer resources and have lower barriers to access and thus have potential for wider reach. Despite this potential, there is a dearth of research examining computerized interventions for depressive symptoms in veteran samples. OBJECTIVE: The aim of this study is to evaluate the efficacy of Deprexis (GAIA AG), a computerized intervention for depressive symptoms and related functional impairment. METHODS: Veterans will be recruited through the US Department of Veterans Affairs electronic medical record and through primary care and specialty clinics. First, qualitative interviews will be completed with a small subset of veterans (n=16-20) to assess the acceptability of treatment procedures. Next, veterans (n=132) with mild to moderate depressive symptoms will be randomly assigned to the fully automated Deprexis intervention or a treatment-as-usual control group. The primary outcomes will be self-reported depressive symptoms and various dimensions of psychosocial functioning. RESULTS: This project was funded in May 2024, and data collection will be conducted between October 2024 and April 2029. Overall, 4 participants have been recruited as of the submission of the manuscript, and data analysis is expected in June 2029, with initial results expected in November 2029. CONCLUSIONS: This study will provide initial evidence for the efficacy of self-guided, computerized interventions for depressive symptoms and functional impairment in veterans. If effective, these types of interventions could improve veteran access to low-resource psychosocial treatments. TRIAL REGISTRATION: ClinicalTrials.gov NCT06217198; https://www.clinicaltrials.gov/study/NCT06217198. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/59119.

Negative self-referent cognition predicts future depression symptom change: an intensive sampling approach.

Cognitive theories of depression assert that negative self-referent cognition has a causal role in the development and maintenance of depression symptoms, but few studies have examined temporal associations between these constructs using intensive, longitudinal sampling strategies. In three samples of undergraduate students, we examined associations between change in self-referent processing and depression across 5 daily assessments (Sample 1, N = 303, 1,194 measurements, 79% adherence), 7 daily assessments (Sample 2, N = 313, 1,784 measurements, 81% adherence), and 7 weekly assessments (Sample 3; N = 155, 833 measurements, 81% adherence). Random intercept cross-lagged panel models indicated large cross-lagged effects in two of the three samples (Samples 1 and 3 but not Sample 2), such that more negative self-referent thinking than usual was significantly associated with a subsequent increase in depression symptoms at the next time lag. Notably, change in depression from usual was not associated with increases in negative self-referent processing at the next time point in any sample. These findings suggest that change in negative self-referent processing may be causally linked to future increases in depression on a day-to-day and week-to-week basis, although confidence in this conclusion is tempered somewhat by a lack of replication in Sample 2.

Approach Bias and Tobacco Craving as Mechanisms of the Effect of Approach Bias Retraining on Smoking Cessation.

INTRODUCTION: Approach bias, the automatic tendency to advance toward, rather than move away from appetitive cues, has been associated with greater tobacco cravings, dependence, and likelihood of smoking relapse. Approach bias retraining (ABR) has emerged as one way to reduce approach bias and promote avoidance toward smoking cues. Yet, additional research is needed to identify the mechanisms that may help explain the effect of ABR on smoking cessation. METHODS: The current study uses data collected as part of a randomized controlled trial to test two unique mechanisms of action ([1] approach bias and [2] tobacco craving) for the efficacy of standard smoking cessation treatment (ST) augmented by ABR on smoking abstinence. Participants were 96 adult daily smokers (Mage=43.1, SD=10.7) motivated to quit smoking. RESULTS: Results showed that lower approach bias and lower cravings at a treatment session were significantly related to next session smoking abstinence (p's<.018). Further, deviations in approach bias partially mediated the effect of ABR on smoking abstinence (ab=-12.17, 95%CI: [-29.67, -0.53]). However, deviations in tobacco craving did not mediate this relation (ab=.40, 95%CI: [-.27, 1.34]). CONCLUSIONS: The current findings add to extant literature by identifying approach bias as a mechanism of action of the effect of ABR on smoking abstinence during smoking cessation treatment. IMPLICATIONS: The current study adds to our knowledge on the effectiveness of approach bias retraining (ABR) as a part of smoking cessation treatment. Results indicate that reductions in approach bias partially mediate the effect of ABR on smoking abstinence. These findings are consistent with previous research on alcohol-dependent adults and underline the potential of ABR to reduce approach bias and promote smoking cessation among smokers. Such findings could inform the development of future research exploring more targeted and effective smoking cessation interventions, ultimately improving outcomes for individuals attempting to quit smoking.

Perceptual Observer Modeling Reveals Likely Mechanisms of Face Expression Recognition Deficits in Depression.

BACKGROUND: Deficits in face emotion recognition are well documented in depression, but the underlying mechanisms are poorly understood. Psychophysical observer models provide a way to precisely characterize such mechanisms. Using model-based analyses, we tested 2 hypotheses about how depression may reduce sensitivity to detect face emotion: 1) via a change in selectivity for visual information diagnostic of emotion or 2) via a change in signal-to-noise ratio in the system performing emotion detection. METHODS: Sixty adults, one half meeting criteria for major depressive disorder and the other half healthy control participants, identified sadness and happiness in noisy face stimuli, and their responses were used to estimate templates encoding the visual information used for emotion identification. We analyzed these templates using traditional and model-based analyses; in the latter, the match between templates and stimuli, representing sensory evidence for the information encoded in the template, was compared against behavioral data. RESULTS: Estimated happiness templates produced sensory evidence that was less strongly correlated with response times in participants with depression than in control participants, suggesting that depression was associated with a reduced signal-to-noise ratio in the detection of happiness. The opposite results were found for the detection of sadness. We found little evidence that depression was accompanied by changes in selectivity (i.e., information used to detect emotion), but depression was associated with a stronger influence of face identity on selectivity. CONCLUSIONS: Depression is more strongly associated with changes in signal-to-noise ratio during emotion recognition, suggesting that deficits in emotion detection are driven primarily by deprecated signal quality rather than suboptimal sampling of information used to detect emotion.

Conversational assessment using artificial intelligence is as clinically useful as depression scales and preferred by users.

BACKGROUND: Depression is prevalent, chronic, and burdensome. Due to limited screening access, depression often remains undiagnosed. Artificial intelligence (AI) models based on spoken responses to interview questions may offer an effective, efficient alternative to other screening methods. OBJECTIVE: The primary aim was to use a demographically diverse sample to validate an AI model, previously trained on human-administered interviews, on novel bot-administered interviews, and to check for algorithmic biases related to age, sex, race, and ethnicity. METHODS: Using the Aiberry app, adults recruited via social media (N = 393) completed a brief bot-administered interview and a depression self-report form. An AI model was used to predict form scores based on interview responses alone. For all meaningful discrepancies between model inference and form score, clinicians performed a masked review to determine which one they preferred. RESULTS: There was strong concurrent validity between the model predictions and raw self-report scores (r = 0.73, MAE = 3.3). 90 % of AI predictions either agreed with self-report or with clinical expert opinion when AI contradicted self-report. There was no differential model performance across age, sex, race, or ethnicity. LIMITATIONS: Limitations include access restrictions (English-speaking ability and access to smartphone or computer with broadband internet) and potential self-selection of participants more favorably predisposed toward AI technology. CONCLUSION: The Aiberry model made accurate predictions of depression severity based on remotely collected spoken responses to a bot-administered interview. This study shows promising results for the use of AI as a mental health screening tool on par with self-report measures.

Associating broad and clinically defined polygenic scores for depression with depression-related phenotypes.

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank in an independent sample of adults (N = 210; 100% European Ancestry) who were extensively phenotyped for depression and related neurocognitive traits (e.g., rumination, emotion regulation, anhedonia, and resting frontal alpha asymmetry). The UK Biobank-derived PGSBD had small associations with MDD, depression severity, anhedonia, cognitive reappraisal, brooding, and suicidal ideation but only the association with suicidal ideation remained statistically significant after correcting for multiple comparisons. Similarly small associations were observed for the PGSMDD but none remained significant after correcting for multiple comparisons. These findings provide important initial guidance about the expected effect sizes between current UKB PGSs for depression and depression-related neurocognitive phenotypes.

Altered electroencephalography resting state network coherence in remitted MDD.

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

Approach bias retraining to augment smoking cessation: A pilot randomized controlled trial.

BACKGROUND: Approach tendency to smoking-related cues has been associated with greater cravings, nicotine dependence, and the likelihood of relapse. In this pilot randomized clinical trial, we examined the efficacy of approach bias retraining (ABR; i.e., increasing avoidance tendency) for enhancing standard smoking cessation treatment (ST). METHODS: Adult smokers (N = 96) motivated to quit were randomly assigned to 7 weekly in-person treatment sessions consisting of either (1) cognitive-behavioral therapy for smoking cessation (ST) and ABR (ST+ABR) or ST and sham retraining (ST+Sham). All participants also received optional nicotine replacement therapy for up to 8 weeks following the scheduled quit date (week 6). We measured avoidance tendency from weeks 1-7. Point prevalence abstinence (PPA) and prolonged abstinence (PA) were measured up to 3 months following the quit attempt (week 18 follow-up). RESULTS: Consistent with our hypothesis, participants in ST+ABR evidenced higher abstinence rates than those in ST+Sham at the final follow-up (b=0.71, 95 % CI: [0.14, 1.27], t[1721]=2.46, p = 0.014, OR=2.03, 95 % CI: [1.15, 3.57]). Specifically, PPA and PA rates were 50 % and 66 % in ST+ABR compared to 31 % and 47 % in ST+Sham. As expected, participants assigned to the ST+ABR condition also showed a greater training-compatible increase in avoidance tendency scores relative to those assigned to the ST+Sham condition (b=248.06, 95 % CI: [148.51, 347,62], t[84]=4.96, p < .001). CONCLUSIONS: The current pilot randomized clinical trial provides initial evidence for the efficacy of integrating standard smoking cessation with ABR. These findings encourage the testing of the long-term efficacy and mechanisms of action of this integrated intervention.

Corrigendum to "Approach bias retraining to augment smoking cessation: Study protocol for a randomized controlled trial" [Contemp. Clin. Trials Commun. 14 (2019) 100340].

[This corrects the article DOI: 10.1016/j.conctc.2019.100340.].

Not just "big" data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions.

There is strong interest in developing a more efficient mental health care system. Digital interventions and predictive models of treatment prognosis will likely play an important role in this endeavor. This article reviews the application of popular machine learning models to the prediction of treatment prognosis, with a particular focus on digital interventions. Assuming that the prediction of treatment prognosis will involve modeling a complex combination of interacting features with measurement error in both the predictors and outcomes, our simulations suggest that to optimize complex prediction models, sample sizes in the thousands will be required. Machine learning methods capable of discovering complex interactions and nonlinear effects (e.g., decision tree ensembles such as gradient boosted machines) perform particularly well in large samples when the predictors and outcomes have virtually no measurement error. However, in the presence of moderate measurement error, these methods provide little or no benefit over regularized linear regression, even with very large sample sizes (N = 100,000) and a non-linear ground truth. Given these sample size requirements, we argue that the scalability of digital interventions, especially when used in combination with optimal measurement practices, provides one of the most effective ways to study treatment prediction models. We conclude with suggestions about how to implement these algorithms into clinical practice.

Trait attributions and threat appraisals explain why an entity theory of personality predicts greater internalizing symptoms during adolescence.

Adolescents who hold an entity theory of personality - the belief that people cannot change - are more likely to report internalizing symptoms during the socially stressful transition to high school. It has been puzzling, however, why a cognitive belief about the potential for change predicts symptoms of an affective disorder. The present research integrated three models - implicit theories, hopelessness theories of depression, and the biopsychosocial model of challenge and threat - to shed light on this issue. Study 1 replicated the link between an entity theory and internalizing symptoms by synthesizing multiple datasets (N = 6,910). Study 2 examined potential mechanisms underlying this link using 8-month longitudinal data and 10-day diary reports during the stressful first year of high school (N = 533, 3,199 daily reports). The results showed that an entity theory of personality predicted increases in internalizing symptoms through tendencies to make fixed trait causal attributions about the self and maladaptive (i.e., "threat") stress appraisals. The findings support an integrative model whereby situation-general beliefs accumulate negative consequences for psychopathology via situation-specific attributions and appraisals.

Change in negative attention bias mediates the association between attention bias modification training and depression symptom improvement.

OBJECTIVE: Attention bias modification training (ABMT) is purported to reduce depression by targeting and modifying an attentional bias for sadness-related stimuli. However, few tests of this hypothesis have been completed. METHOD: The present study examined whether change in attentional bias mediated a previously reported association between ABMT condition (active ABMT, sham ABMT, assessments only; N = 145) and depression symptom change among depressed adults. The preregistered, primary measure of attention bias was a discretized eye-tracking metric that quantified the proportion of trials where gaze time was greater for sad stimuli than neutral stimuli. RESULTS: Contemporaneous longitudinal simplex mediation indicated that change in attentional bias early in treatment partially mediated the effect of ABMT on depression symptoms. Specificity analyses indicated that in contrast to the eye-tracking mediator, reaction time assessments of attentional bias for sad stimuli (mean bias and trial level variability) and lapses in sustained attention did not mediate the association between ABMT and depression change. Results also suggested that mediation effects were limited to a degree by suboptimal measurement of attentional bias for sad stimuli. CONCLUSION: When effective, ABMT may improve depression in part by reducing an attentional bias for sad stimuli, particularly early on during ABMT. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A computational account of the mechanisms underlying face perception biases in depression.

Here, we take a computational approach to understand the mechanisms underlying face perception biases in depression. Thirty participants diagnosed with major depressive disorder and 30 healthy control participants took part in three studies involving recognition of identity and emotion in faces. We used signal detection theory to determine whether any perceptual biases exist in depression aside from decisional biases. We found lower sensitivity to happiness in general, and lower sensitivity to both happiness and sadness with ambiguous stimuli. Our use of highly-controlled face stimuli ensures that such asymmetry is truly perceptual in nature, rather than the result of studying expressions with inherently different discriminability. We found no systematic effect of depression on the perceptual interactions between face expression and identity. We also found that decisional strategies used in our task were different for people with depression and controls, but in a way that was highly specific to the stimulus set presented. We show through simulation that the observed perceptual effects, as well as other biases found in the literature, can be explained by a computational model in which channels encoding positive expressions are selectively suppressed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Symptom centrality and infrequency of endorsement identify adolescent depression symptoms more strongly associated with life satisfaction.

Although depression symptoms are often treated as interchangeable, some symptoms may relate to adolescent life satisfaction more strongly than others. To assess this premise, we first conducted a network analysis on the Mood and Feelings Questionnaire (MFQ) in a large (N = 1,059), cross-sectional sample of community adolescents (age M = 14.72 ± 1.79). The most central symptoms of adolescent depression, as indexed by strength, were self-hatred, loneliness, sadness, and worthlessness while the least frequently endorsed symptoms were self-hatred, anhedonia, feeling like a bad person, and feeling unloved. Moreover, the more central a depression symptom was in the network (i.e., higher strength), the more variance it shared with life satisfaction (r = 0.59, 95% CI: 0.27, 0.76). How frequently a symptom was endorsed was negatively associated with the variance symptoms shared with life satisfaction (r = -0.48, 95% CI: -0.63, -0.21). Cross-validated, prediction focused models found central symptoms were expected to predict more out of fold variance in life satisfaction than peripheral symptoms and frequently endorsed symptoms, but not the least frequently endorsed symptoms. These findings show certain depression symptoms may be more strongly associated with life satisfaction in adolescence and these symptoms can be identified by multiple symptom-level metrics. Limitations include use of cross-sectional data and utilizing a community sample. Better understanding which symptoms of depression share more variance with important outcomes like life satisfaction could help us develop a more fine-grained understanding of adolescent depression.

Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data.

BACKGROUND: Internet cognitive behavioural therapy (iCBT) is a viable delivery format of CBT for depression. However, iCBT programmes include training in a wide array of cognitive and behavioural skills via different delivery methods, and it remains unclear which of these components are more efficacious and for whom. METHODS: We did a systematic review and individual participant data component network meta-analysis (cNMA) of iCBT trials for depression. We searched PubMed, PsycINFO, Embase, and the Cochrane Library for randomised controlled trials (RCTs) published from database inception to Jan 1, 2019, that compared any form of iCBT against another or a control condition in the acute treatment of adults (aged ≥18 years) with depression. Studies with inpatients or patients with bipolar depression were excluded. We sought individual participant data from the original authors. When these data were unavailable, we used aggregate data. Two independent researchers identified the included components. The primary outcome was depression severity, expressed as incremental mean difference (iMD) in the Patient Health Questionnaire-9 (PHQ-9) scores when a component is added to a treatment. We developed a web app that estimates relative efficacies between any two combinations of components, given baseline patient characteristics. This study is registered in PROSPERO, CRD42018104683. FINDINGS: We identified 76 RCTs, including 48 trials contributing individual participant data (11 704 participants) and 28 trials with aggregate data (6474 participants). The participants' weighted mean age was 42·0 years and 12 406 (71%) of 17 521 reported were women. There was suggestive evidence that behavioural activation might be beneficial (iMD -1·83 [95% credible interval (CrI) -2·90 to -0·80]) and that relaxation might be harmful (1·20 [95% CrI 0·17 to 2·27]). Baseline severity emerged as the strongest prognostic factor for endpoint depression. Combining human and automated encouragement reduced dropouts from treatment (incremental odds ratio, 0·32 [95% CrI 0·13 to 0·93]). The risk of bias was low for the randomisation process, missing outcome data, or selection of reported results in most of the included studies, uncertain for deviation from intended interventions, and high for measurement of outcomes. There was moderate to high heterogeneity among the studies and their components. INTERPRETATION: The individual patient data cNMA revealed potentially helpful, less helpful, or harmful components and delivery formats for iCBT packages. iCBT packages aiming to be effective and efficient might choose to include beneficial components and exclude ones that are potentially detrimental. Our web app can facilitate shared decision making by therapist and patient in choosing their preferred iCBT package. FUNDING: Japan Society for the Promotion of Science.

Personalized cognitive training: Protocol for individual-level meta-analysis implementing machine learning methods.

Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.

Efficacy of attention bias modification training for depressed adults: a randomized clinical trial.

BACKGROUND: This study examined the efficacy of attention bias modification training (ABMT) for the treatment of depression. METHODS: In this randomized clinical trial, 145 adults (77% female, 62% white) with at least moderate depression severity [i.e. self-reported Quick Inventory of Depressive Symptomatology (QIDS-SR) ⩾13] and a negative attention bias were randomized to active ABMT, sham ABMT, or assessments only. The training consisted of two in-clinic and three (brief) at-home ABMT sessions per week for 4 weeks (2224 training trials total). The pre-registered primary outcome was change in QIDS-SR. Secondary outcomes were the 17-item Hamilton Depression Rating Scale (HRSD) and anhedonic depression and anxious arousal from the Mood and Anxiety Symptom Questionnaire (MASQ). Primary and secondary outcomes were administered at baseline and four weekly assessments during ABMT. RESULTS: Intent-to-treat analyses indicated that, relative to assessment-only, active ABMT significantly reduced QIDS-SR and HRSD scores by an additional 0.62 ± 0.23 (p = 0.008, d = -0.57) and 0.74 ± 0.31 (p = 0.021, d = -0.49) points per week. Similar results were observed for active v. sham ABMT: a greater symptom reduction of 0.44 ± 0.24 QIDS-SR (p = 0.067, d = -0.41) and 0.69 ± 0.32 HRSD (p = 0.033, d = -0.42) points per week. Sham ABMT did not significantly differ from the assessment-only condition. No significant differences were observed for the MASQ scales. CONCLUSION: Depressed individuals with at least modest negative attentional bias benefitted from active ABMT.

Multifactorial prediction of depression diagnosis and symptom dimensions.

While depression is a leading cause of disability, prior investigations of depression have been limited by studying correlates in isolation. A data-driven approach was applied to identify out-of-sample predictors of current depression from adults (N = 217) sampled on a continuum of no depression to clinical levels. The current study used elastic net regularized regression and predictors from sociodemographic, self-report, polygenic scores, resting electroencephalography, pupillometry, actigraphy, and cognitive tasks to classify individuals into currently depressed (MDE), psychiatric control (PC), and no current psychopathology (NP) groups, as well as predicting symptom severity and lifetime MDE. Cross-validated models explained 20.6% of the out-of-fold deviance for the classification of MDEs versus PC, 33.2% of the deviance for MDE versus NP, but -0.6% of the deviance between PC and NP. Additionally, predictors accounted for 25.7% of the out-of-fold variance in anhedonia severity, 65.7% of the variance in depression severity, and 12.9% of the deviance in lifetime depression (yes/no). Self-referent processing, anhedonia, and psychosocial functioning emerged as important differentiators of MDE and PC groups. Findings highlight the advantages of using psychiatric control groups to isolate factors specific to depression.