Fear attenuation collaborations to optimize translation.

Here, we describe the efforts we dedicated to the challenge of modifying entrenched emotionally laden memories. In recent years, through a number of collaborations and using a combination of behavioral, molecular, and computational approaches, we: (a) developed novel approaches to fear attenuation that engage mechanisms that differ from those engaged during extinction (Monfils), (b) examined whether our approaches can generalize to other reinforcers (Lee, Gonzales, Chaudhri, Cofresi, and Monfils), (c) derived principled explanations for the differential outcomes of our approaches (Niv, Gershman, Song, and Monfils), (d) developed better assessment metrics to evaluate outcome success (Shumake and Monfils), (e) identified biomarkers that can explain significant variance in our outcomes of interest (Shumake and Monfils), and (f) developed better basic research assays and translated efforts to the clinic (Smits, Telch, Otto, Shumake, and Monfils). We briefly highlight each of these milestones and conclude with final remarks and extracted principles. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Carbon Dioxide Reactivity Differentially Predicts Fear Expression After Extinction and Retrieval-Extinction in Rats.

Background: Cues present during a traumatic event may result in persistent fear responses. These responses can be attenuated through extinction learning, a core component of exposure therapy. Exposure/extinction is effective for some people, but not all. We recently demonstrated that carbon dioxide (CO2) reactivity predicts fear extinction memory and orexin activation and that orexin activation predicts fear extinction memory, which suggests that a CO2 challenge may enable identification of whether an individual is a good candidate for an extinction-based approach. Another method to attenuate conditioned responses, retrieval-extinction, renders the original associative memory labile via distinct neural mechanisms. The purpose of the current study was to examine whether we could replicate previous findings that retrieval-extinction is more effective than extinction at preventing the return of fear and that CO2 reactivity predicts fear memory after extinction. We also examined whether CO2 reactivity predicts fear memory after retrieval-extinction. Methods: Male rats first underwent a CO2 challenge and fear conditioning and were assigned to receive either standard extinction (n = 28) or retrieval-extinction (n = 28). Then, they underwent a long-term memory (LTM) test and a reinstatement test. Results: We found that retrieval-extinction resulted in lower freezing during extinction, LTM, and reinstatement than standard extinction. Using the best subset approach to linear regression, we found that CO2 reactivity predicted LTM after extinction and also predicted LTM after retrieval-extinction, although to a lesser degree. Conclusions: CO2 reactivity could be used as a screening tool to determine whether an individual may be a good candidate for an extinction-based therapeutic approach.

Extinction learning underlies exposure therapy, a treatment for anxiety disorders. However, not everyone benefits from exposure therapy, highlighting the need in developing approaches that may help predict which individuals will respond. We tested whether extinction or an alternative treatment called retrieval-extinction would be more effective at reducing conditioned fear responses in rats and whether the response to a carbon dioxide (CO₂) challenge would predict treatment response. We found that retrieval-extinction was more effective at reducing fear, and CO₂ reactivity was better at predicting the response to extinction. These findings could help improve treatment strategies for anxiety disorders.

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.

CO2 reactivity is associated with individual differences in appetitive extinction memory.

Pavlovian conditioning can underly the maladaptive behaviors seen in psychiatric disorders such as anxiety and addiction. In both the lab and the clinic, these responses can be attenuated through extinction learning, but often return with the passage of time, stress, or a change in context. Extinction to fear and reward cues are both subject to these return of behavior phenomena and have overlap in neurocircuitry, yet it is unknown whether they share any common predictors. The orexin system has been implicated in both fear and appetitive extinction and can be activated through a CO2 challenge. We previously found that behavioral CO2 reactivity predicts fear extinction and orexin activation. Here, we sought to extend our previous findings to determine whether CO2 reactivity might also predict extinction memory for appetitive light-food conditioning. We find that the same subcomponent of behavioral CO2 reactivity that predicted fear extinction also predicts appetitive extinction, but in the opposite direction. We show evidence that this subcomponent remains stable across two CO2 challenges, suggesting it may be a stable trait of both behavioral CO2 reactivity and appetitive extinction phenotype. Our findings further the possibility for CO2 reactivity to be used as a transdiagnostic screening tool to determine whether an individual would be a good candidate for exposure therapy.

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.

CO2 reactivity as a biomarker of exposure-based therapy non-response: study protocol.

BACKGROUND: Exposure-based therapy is an effective first-line treatment for anxiety-, obsessive-compulsive, and trauma- and stressor-related disorders; however, many patients do not improve, resulting in prolonged suffering and poorly used resources. Basic research on fear extinction may inform the development of a biomarker for the selection of exposure-based therapy. Growing evidence links orexin system activity to deficits in fear extinction and we have demonstrated that reactivity to an inhaled carbon dioxide (CO2) challenge-a safe, affordable, and easy-to-implement procedure-can serve as a proxy for orexin system activity and predicts fear extinction deficits in rodents. Building upon this basic research, the goal for the proposed study is to validate CO2 reactivity as a biomarker of exposure-based therapy non-response. METHODS: We will assess CO2 reactivity in 600 adults meeting criteria for one or more fear- or anxiety-related disorders prior to providing open exposure-based therapy. By incorporating CO2 reactivity into a multivariate model predicting treatment non-response that also includes reactivity to hyperventilation as well as a number of related predictor variables, we will establish the mechanistic specificity and the additive predictive utility of the potential CO2 reactivity biomarker. By developing models independently within two study sites (University of Texas at Austin and Boston University) and predicting the other site's data, we will validate that the results are likely to generalize to future clinical samples. DISCUSSION: Representing a necessary stage in translating basic research, this investigation addresses an important public health issue by testing an accessible clinical assessment strategy that may lead to a more effective treatment selection (personalized medicine) for patients with anxiety- and fear-related disorders, and enhanced understanding of the mechanisms governing exposure-based therapy. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05467683 (20/07/2022).

An examination of the clinical utility of phonemic fluency in healthy adults and adults with mild cognitive impairment.

The Controlled Oral Word Association Test (COWAT) is a widely utilized measure of phonemic fluency. However, two issues remain: (1) whether demographic, cognitive variables, or version of test administered predict performance; (2) if the test is predictive of Mild Cognitive Impairment (MCI). Recent studies report that item-level analyses such as lexical frequency may be more sensitive to early cognitive change. The purpose of this study was to examine the clinical utility of the COWAT, considering both total correct words and the lexical frequency. Sixty-seven healthy adults and thirty-seven adults with MCI completed neuropsychological testing. Mann-Whitney U tests were used to determine if there was a difference in COWAT performance between groups. Elastic net regression models were used to assess whether variance in total scores/lexical frequencies can be predicted by demographics, test version, or diagnosis; which cognitive tests explained the variance in performance; and how total scores and lexical frequencies compared with other cognitive tests in predicting diagnosis. Overall, individuals with MCI produced fewer and higher frequency words. The variance in total correct words or lexical frequency was not explained by demographics, test version, or diagnosis. Total correct words was a more important predictor of diagnosis than lexical frequency.

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.

Web-Based Single Session Intervention for Perceived Control Over Anxiety During COVID-19: Randomized Controlled Trial.

BACKGROUND: Anxiety is rising across the United States during the COVID-19 pandemic, and social distancing mandates preclude in-person mental health care. Greater perceived control over anxiety has predicted decreased anxiety pathology, including adaptive responses to uncontrollable stressors. Evidence suggests that no-therapist, single-session interventions can strengthen perceived control over emotions like anxiety; similar programs, if designed for the COVID-19 context, could hold substantial public health value. OBJECTIVE: Our registered report evaluated a no-therapist, single-session, online intervention targeting perceived control over anxiety in the COVID-19 context against a placebo intervention encouraging handwashing. We tested whether the intervention could (1) decrease generalized anxiety and increase perceived control over anxiety and (2) achieve this without decreasing social-distancing intentions. METHODS: We tested these questions using a between-subjects design in a weighted-probability sample of US adults recruited via a closed online platform (ie, Prolific). All outcomes were indexed via online self-report questionnaires. RESULTS: Of 522 randomized individuals, 500 (95.8%) completed the baseline survey and intervention. Intent-to-treat analyses using all randomized participants (N=522) found no support for therapeutic or iatrogenic effects; effects on generalized anxiety were d=-0.06 (95% CI -0.27 to 0.15; P=.48), effects on perceived control were d=0.04 (95% CI -0.08 to 0.16; P=.48), and effects on social-distancing intentions were d=-0.02 (95% CI -0.23 to 0.19; P=.83). CONCLUSIONS: Strengths of this study included a large, nationally representative sample and adherence to open science practices. Implications for scalable interventions, including the challenge of targeting perceived control over anxiety, are discussed. TRIAL REGISTRATION: ClinicalTrials.gov NCT04459455; https://clinicaltrials.gov/show/NCT04459455.

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).

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.

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.

Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response.

Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STAR*D treatment guidelines was acceptable, AUC = .659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were .662, 95% CI [0.632, 0.692] and .655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample.

Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis.

Importance: Personalized treatment choices would increase the effectiveness of internet-based cognitive behavioral therapy (iCBT) for depression to the extent that patients differ in interventions that better suit them. Objective: To provide personalized estimates of short-term and long-term relative efficacy of guided and unguided iCBT for depression using patient-level information. Data Sources: We searched PubMed, Embase, PsycInfo, and Cochrane Library to identify randomized clinical trials (RCTs) published up to January 1, 2019. Study Selection: Eligible RCTs were those comparing guided or unguided iCBT against each other or against any control intervention in individuals with depression. Available individual patient data (IPD) was collected from all eligible studies. Depression symptom severity was assessed after treatment, 6 months, and 12 months after randomization. Data Extraction and Synthesis: We conducted a systematic review and IPD network meta-analysis and estimated relative treatment effect sizes across different patient characteristics through IPD network meta-regression. Main Outcomes and Measures: Patient Health Questionnaire-9 (PHQ-9) scores. Results: Of 42 eligible RCTs, 39 studies comprising 9751 participants with depression contributed IPD to the IPD network meta-analysis, of which 8107 IPD were synthesized. Overall, both guided and unguided iCBT were associated with more effectiveness as measured by PHQ-9 scores than control treatments over the short term and the long term. Guided iCBT was associated with more effectiveness than unguided iCBT (mean difference [MD] in posttreatment PHQ-9 scores, -0.8; 95% CI, -1.4 to -0.2), but we found no evidence of a difference at 6 or 12 months following randomization. Baseline depression was found to be the most important modifier of the relative association for efficacy of guided vs unguided iCBT. Differences between unguided and guided iCBT in people with baseline symptoms of subthreshold depression (PHQ-9 scores 5-9) were small, while guided iCBT was associated with overall better outcomes in patients with baseline PHQ-9 greater than 9. Conclusions and Relevance: In this network meta-analysis with IPD, guided iCBT was associated with more effectiveness than unguided iCBT for individuals with depression, benefits were more substantial in individuals with moderate to severe depression. Unguided iCBT was associated with similar effectiveness among individuals with symptoms of mild/subthreshold depression. Personalized treatment selection is entirely possible and necessary to ensure the best allocation of treatment resources for depression.

Neurocognitive predictors of self-reported reward responsivity and approach motivation in depression: A data-driven approach.

BACKGROUND: Individual differences in reward-related processes, such as reward responsivity and approach motivation, appear to play a role in the nature and course of depression. Prior work suggests that cognitive biases for valenced information may contribute to these reward processes. Yet there is little work examining how biased attention, processing, and memory for positively and negatively valenced information may be associated with reward-related processes in samples with depression symptoms. METHODS: We used a data-driven, machine learning (elastic net) approach to identify the best predictors of self-reported reward-related processes using multiple tasks of attention, processing, and memory for valenced information measured across behavioral, eye tracking, psychophysiological, and computational modeling approaches (n = 202). Participants were adults (ages 18-35) who ranged in depression symptom severity from mild to severe. RESULTS: Models predicted between 5.0-12.2% and 9.7-28.0% of held-out test sample variance in approach motivation and reward responsivity, respectively. Low self-referential processing of positively valenced information was the most robust, albeit modest, predictor of low approach motivation and reward responsivity. CONCLUSIONS: Self-referential processing of positive information is the strongest predictor of reward responsivity and approach motivation in a sample ranging from mild to severe depression symptom severity. Experiments are now needed to clarify the causal relationship between self-referential processing of positively valenced information and reward processes in depression.

Association between negative cognitive bias and depression: A symptom-level approach.

Cognitive models of depression posit that negatively biased self-referent processing and attention have important roles in the disorder. However, depression is a heterogeneous collection of symptoms and all symptoms are unlikely to be associated with these negative cognitive biases. The current study involved 218 community adults whose depression ranged from no symptoms to clinical levels of depression. Random forest machine learning was used to identify the most important depression symptom predictors of each negative cognitive bias. Depression symptoms were measured with the Beck Depression Inventory-II. Model performance was evaluated using predictive R-squared (Rpred2), the expected variance explained in data not used to train the algorithm, estimated by 10 repetitions of 10-fold cross-validation. Using the self-referent encoding task (SRET), depression symptoms explained 34% to 45% of the variance in negative self-referent processing. The symptoms of sadness, self-dislike, pessimism, feelings of punishment, and indecision were most important. Notably, many depression symptoms made virtually no contribution to this prediction. In contrast, for attention bias for sad stimuli, measured with the dot-probe task using behavioral reaction time (RT) and eye gaze metrics, no reliable symptom predictors were identified. Findings indicate that a symptom-level approach may provide new insights into which symptoms, if any, are associated with negative cognitive biases in depression. (PsycINFO Database Record (c) 2019 APA, all rights reserved).