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.

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.

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.

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- (PGS_BD) and clinical- (PGS_MDD) 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 PGS_BD 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 PGS_MDD 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.

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.

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).

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.

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.

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.

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.

Getting Fewer "Likes" Than Others on Social Media Elicits Emotional Distress Among Victimized Adolescents

Three studies examined the effects of receiving fewer signs of positive feedback than others on social media. In Study 1, adolescents (N = 613, Mage  = 14.3 years) who were randomly assigned to receive few (vs. many) likes during a standardized social media interaction felt more strongly rejected, and reported more negative affect and more negative thoughts about themselves. In Study 2 (N = 145), negative responses to receiving fewer likes were associated with greater depressive symptoms reported day-to-day and at the end of the school year. Study 3 (N = 579) replicated Study 1's main effect of receiving fewer likes and showed that adolescents who already experienced peer victimization at school were the most vulnerable. The findings raise the possibility that technology which makes it easier for adolescents to compare their social status online-even when there is no chance to share explicitly negative comments-could be a risk factor that accelerates the onset of internalizing symptoms among vulnerable youth.

Factors predicting the development of psychopathology among first responders: A prospective, longitudinal study

Objective: Previous research has shown that first responders exhibit elevated rates of psychopathology. Factors predicting the development of this psychopathology, however, remain understudied. This study longitudinally examined predictors of posttraumatic stress disorder (PTSD), depression, and anxiety symptoms in first responders. Method: Participants included 135 emergency medical service (EMS) providers. Multiple linear regressions were used to model predictors of change in PTSD, depression, and anxiety symptomatology from baseline to 3-month follow-up. Baseline levels of social support, sleep, emotional stability, and perceived stress were examined as potential predictors. Results: Results revealed that (a) increases in PTSD symptoms, (b) increases in depression symptoms, and (c) increases in anxiety symptoms at 3-month follow-up were each predicted by worse sleep and lower social support at baseline. In particular, the sleep subscale of disturbed sleep and the social support subscale of appraisal appeared to be driving these effects. Conclusion: These results highlight the importance of social support and sleep hygiene in protecting against increases in psychopathology symptoms in EMS providers, and set the stage for future interventions to target sleep disturbances and encourage deeper social connections in order to foster resilience in first responders. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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.

Network analyses reveal which symptoms improve (or not) following an Internet intervention (Deprexis) for depression

BACKGROUND

Depression is a heterogeneous collection of symptoms. Prior meta-analyses using symptom sum scores have shown the Internet intervention, Deprexis, to be an efficacious treatment for depression. However, no prior research has investigated how Deprexis (or any other Internet intervention for depression) impacts specific symptoms of depression. The current study utilizes symptom-level analyses to examine which symptoms are directly, indirectly, or minimally influenced by treatment.

METHODS

Network analysis and mean-level approaches examined which symptoms, assessed by the Quick Inventory of Depression Symptoms, were affected by an 8-week course of Deprexis compared with a waitlist in a nationally recruited sample from the United States (N = 295).

RESULTS

Deprexis directly improved the symptoms of sadness and indecision. Changes in these symptoms, in turn, was associated with a change in early insomnia, middle insomnia, self-dislike, fatigue, anhedonia, suicidality, slowness, and agitation. All of these symptoms (except for agitation and early insomnia) show decreases with Deprexis compared with a waitlist after correcting for multiple comparisons. Six additional symptoms, particularly the somatic symptoms, were not impacted by Deprexis compared with a waitlist.

CONCLUSIONS

In this sample, the efficacy of Deprexis was due to its direct impact on sadness and indecision. Examining the treatment-related change in specific symptoms may facilitate a more nuanced understanding of how a treatment works compared with examining symptom sum scores. Symptom-level approaches may also identify symptoms that do not improve and provide important direction for future treatment development.

A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression

BACKGROUND

Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.

METHOD

An elastic net and random forest were trained to predict depression symptoms and related disability after an 8-week course of an Internet intervention, Deprexis, involving adults (N = 283) from across the USA. Candidate predictors included psychopathology, demographics, treatment expectancies, treatment usage, and environmental context obtained from population databases. Model performance was evaluated using predictive R2$\lpar R_{{\rm pred}}^2\rpar\comma $ the expected variance explained in a new sample, estimated by 10 repetitions of 10-fold cross-validation.

RESULTS

An ensemble model was created by averaging the predictions of the elastic net and random forest. Model performance was compared with a benchmark linear autoregressive model that predicted each outcome using only its baseline. The ensemble predicted more variance in post-treatment depression (8.0% gain, 95% CI 0.8-15; total $R_{{\rm pred}}^2 \; $= 0.25), disability (5.0% gain, 95% CI -0.3 to 10; total $R_{{\rm pred}}^2 \; $= 0.25), and well-being (11.6% gain, 95% CI 4.9-19; total $R_{{\rm pred}}^2 \; $= 0.29) than the benchmark model. Important predictors included comorbid psychopathology, particularly total psychopathology and dysthymia, low symptom-related disability, treatment credibility, lower access to therapists, and time spent using certain Deprexis modules.

CONCLUSION

A number of variables predict symptom improvement following an Internet intervention, but each of these variables makes relatively small contributions. Machine learning ensembles may be a promising statistical approach for identifying the cumulative contribution of many weak predictors to psychosocial depression treatment response.

Impact of depression on speech perception in noise

Effective speech communication is critical to everyday quality of life and social well-being. In addition to the well-studied deficits in cognitive and motor function, depression also impacts communication. Here, we examined speech perception in individuals who were clinically diagnosed with major depressive disorder (MDD) relative to neurotypical controls. Forty-two normal-hearing (NH) individuals with MDD and 41 NH neurotypical controls performed sentence recognition tasks across three conditions with maskers varying in the extent of linguistic content (high, low, and none): 1-talker masker (1T), reversed 1-talker masker (1T_tr), and speech-shaped noise (SSN). Individuals with MDD, relative to neurotypical controls, demonstrated lower recognition accuracy in the 1T condition but not in the 1T_tr or SSN condition. To examine the nature of the listening condition-specific speech perception deficit, we analyzed speech recognition errors. Errors as a result of interference from masker sentences were higher for individuals with MDD (vs. neurotypical controls) in the 1T condition. This depression-related listening condition-specific pattern in recognition errors was not observed for other error types. We posit that this depression-related listening condition-specific deficit in speech perception may be related to heightened distractibility due to linguistic interference from background talkers.

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).

Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization

Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early interventions that may reduce individual suffering and societal costs. Toward this aim, we applied ensemble machine learning to predict PTSD screening status three months after severe injury using cost-effective and minimally invasive data. Participants (N = 271) were recruited at a Level 1 Trauma Center where they provided variables routinely collected at the hospital, including pulse, injury severity, and demographics, as well as psychological variables, including self-reported current depression, psychiatric history, and social support. Participant zip codes were used to extract contextual variables including population total and density, average annual income, and health insurance coverage rates from publicly available U.S. Census data. Machine learning yielded good prediction of PTSD screening status 3 months post-hospitalization, AUC = 0.85 95% CI [0.83, 0.86], and significantly outperformed all benchmark comparison models in a cross-validation procedure designed to yield an unbiased estimate of performance. These results demonstrate that good prediction can be attained from variables that individually have relatively weak predictive value, pointing to the promise of ensemble machine learning approaches that do not rely on strong isolated risk factors.

Attentional bias modification treatment for depression: Study protocol for a randomized controlled trial

Theoretical models and empirical research point to negatively biased attention as a maintaining factor in depression. Although preliminary studies suggest experimentally modifying attentional biases (i.e., attentional bias modification; ABM) reduces depression symptoms and depression risk, relatively few rigorous studies with clinical samples have been completed. This clinical trial examines the impact of ABM on a sample of adults (N = 123) with elevated depression severity who also exhibit at least modest levels of negatively biased attention prior to treatment. Participants will be randomly assigned to either active ABM, placebo ABM, or an assessment-only control condition. Individuals assigned to ABM will complete 5 trainings per week (2 in-clinic, 3 brief trainings at-home) during a four-week period. Throughout this four-week period, participants will complete weekly assessments of symptom severity and putative treatment mediators measured across different levels of analysis (e.g., eye tracking, behavioral measures, and functional Magnetic Resonance Imaging). This article details the rationale and design of the clinical trial, including methodological issues that required more extensive consideration. Our findings may not only point to an easily-accessible, efficacious treatment for depression but may also provide a meaningful test of whether a theoretically important construct, negatively biased attention, maintains depression.

Determining optimal parameters of the self-referent encoding task: A large-scale examination of self-referent cognition and depression

[Correction Notice: An Erratum for this article was reported online in Psychological Assessment on Aug 2 2018 (see record 2018-38659-001). In this article, there was an error in how exclusions for one of the three samples were reported, which resulted in inaccurate reporting of how many participants did not have complete data. This error did not change the primary results of the article or the conclusions. However, in the second paragraph of the Participant Attrition and Data Filtering section, the number of exclusions for the adolescent sample should be 301, not 163. As a result, n=408 should read n=270 in the abstract; in paragraph 3 of the Method section; and in the Figure 1 legend. In addition, the correct values for the Adolescents sample reported in Tables 1 and 2 are provided in the erratum.] Although the self-referent encoding task (SRET) is commonly used to measure self-referent cognition in depression, many different SRET metrics can be obtained. The current study used best subsets regression with cross-validation and independent test samples to identify the SRET metrics most reliably associated with depression symptoms in three large samples: a college student sample (n = 572), a sample of adults from Amazon Mechanical Turk (n = 293), and an adolescent sample from a school field study (n = 408). Across all 3 samples, SRET metrics associated most strongly with depression severity included number of words endorsed as self-descriptive and rate of accumulation of information required to decide whether adjectives were self-descriptive (i.e., drift rate). These metrics had strong intratask and split-half reliability and high test-retest reliability across a 1-week period. Recall of SRET stimuli and traditional reaction time (RT) metrics were not robustly associated with depression severity. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Acetaminophen enhances the reflective learning process

Acetaminophen has been shown to influence cognitive and affective behavior possibly via alterations in serotonin function. This study builds upon this previous work by examining the relationship between acetaminophen and dual-learning systems, comprising reflective (rule-based) and reflexive (information-integration) processing. In a double-blind, placebo-controlled study, a sample of community-recruited adults (N = 87) were randomly administered acetaminophen (1000 mg) or placebo and then completed reflective-optimal and reflexive-optimal category learning tasks. For the reflective-optimal category learning task, acetaminophen compared to placebo was associated with enhanced accuracy prior to the first rule switch (but not overall accuracy), with needing fewer trials to reach criterion and with a faster learning rate. Acetaminophen modestly attenuated performance on the reflexive-optimal category learning task compared to placebo. These findings indirectly support two positions that have been proposed elsewhere. First, they are consistent with the view that acetaminophen has an influence on the serotonergic system. Second, the findings are consistent with a proposed link between elevated serotonin function and relative dominance of effortful, rule-based processing.

Positive imagery training increases positive self-referent cognition in depression

Depressed adults often show a bias towards negative self-referent processing at the expense of positive self-referent processing. The current study assessed whether a mental imagery intervention (Positive Self Reference Training-PSRT) delivered via the Internet could improve self-referent processing and depressive symptomatology among adults with moderate or greater depression symptoms. Participants were recruited via online methods and randomly assigned to one of two computerized interventions: active PSRT (n=44) or control training (NTC; n=43). The PSRT involved visualizing the self in response to different positive cues (e.g., an achievement) every other day for two weeks. The NTC provided neutral cues about objects. Self-referential processing of positive and negative adjectives and depression symptoms were measured at baseline, one week, and two weeks after initiating training. Over those two weeks, PSRT participants showed a greater increase in positive self-referent processing than did NTC participants. Negative self-referent processing and symptoms of depression declined comparably in both groups. Similarly, for both groups, increase in positive and decrease in negative self-referent processing was associated with a greater reduction in depression. These results indicate that mental imagery has the potential to improve self-referential processing, especially for positive stimuli, which may, in turn, help reduce depressive symptomatology.

Negative self-referential processing is associated with genetic variation in the serotonin transporter-linked polymorphic region (5-HTTLPR): Evidence from two independent studies

The current research examined whether carriers of the short 5-HTTLPR allele (in SLC6A4), who have been shown to selectively attend to negative information, exhibit a bias towards negative self-referent processing. The self-referent encoding task (SRET) was used to measure self-referential processing of positive and negative adjectives. Ratcliff's diffusion model isolated and extracted decision-making components from SRET responses and reaction times. Across the initial (N = 183) and replication (N = 137) studies, results indicated that short 5-HTTLPR allele carriers more easily categorized negative adjectives as self-referential (i.e., higher drift rate). Further, drift rate was associated with recall of negative self-referential stimuli. Findings across both studies provide further evidence that genetic variation may contribute to the etiology of negatively biased processing of self-referent information. Large scale studies examining the genetic contributions to negative self-referent processing may be warranted.

Using Network Analysis to Identify Central Symptoms of Adolescent Depression

Experiencing depression symptoms, even at mild to moderate levels, is associated with maladaptive outcomes for adolescents. We used network analysis to evaluate which symptoms (and associations between symptoms) are most central to adolescent depression. Participants were part of a large, diverse community sample (N = 1,409) of adolescents between 13 and 19 years of age. Network analysis was used to identify the most central symptoms (nodes) and associations between symptoms (edges) assessed by the Children's Depression Inventory. We also evaluated these centrality indicators for network robustness using stability and accuracy tests, associated symptom centrality with mean levels of symptoms, and examined potential differences between the structure and connectivity of depression networks in boys and girls. The most central symptoms in the network were self-hatred, loneliness, sadness, and pessimism. The strongest associations between symptoms were sadness-crying, anhedonia-school dislike, sadness-loneliness, school work difficulty-school performance decrement, self-hatred-negative body image, sleep disturbance-fatigue, and self-deprecation-self-blame. The network was robust to stability and accuracy tests. Notably, symptom centrality and mean levels of symptoms were not associated. Boys and girls' networks did not differ in levels of connectivity, though the link between body image and self-hatred was stronger in girls than boys. Self-hatred, loneliness, sadness, and pessimism were the most central symptoms in adolescent depression networks, suggesting that these symptoms (and associations between symptoms) should be prioritized in theoretical models of adolescent depression and could also serve as important treatment targets for adolescent depression interventions.

Specificity and overlap of attention and memory biases in depression

BACKGROUND

Attentional and memory biases are viewed as crucial cognitive processes underlying symptoms of depression. However, it is still unclear whether these two biases are uniquely related to depression or whether they show substantial overlap.

METHODS

We investigated the degree of specificity and overlap of attentional and memory biases for depressotypic stimuli in relation to depression and anxiety by means of meta-analytic commonality analysis. By including four published studies, we considered a pool of 463 healthy and subclinically depressed individuals, different experimental paradigms, and different psychological measures.

RESULTS

Memory bias is reliably and strongly related to depression and, specifically, to symptoms of negative mood, worthlessness, feelings of failure, and pessimism. Memory bias for negative information was minimally related to anxiety. Moreover, neither attentional bias nor the overlap between attentional and memory biases were significantly related to depression.

LIMITATIONS

Limitations include cross-sectional nature of the study.

CONCLUSIONS

Our study showed that, across different paradigms and psychological measures, memory bias (and not attentional bias) represents a primary mechanism in depression.

Sustained engagement of attention is associated with increased negative self-referent processing in major depressive disorder

This study investigated the link between self-reference and attentional engagement in adults with (n=22) and without (HC; n=24) Major Depressive Disorder (MDD). Event-related potentials (ERPs) were recorded while participants completed the Self-Referent Encoding Task (SRET). MDD participants endorsed significantly fewer positive words and more negative words as self-descriptive than HC participants. A whole-scalp data analysis technique revealed that the MDD participants had larger difference wave (negative words minus positive words) ERP amplitudes from 380 to 1000ms across posterior sites, which positively correlated with number of negative words endorsed. No group differences were observed for earlier attentional components (P1, P2). The results suggest that among adults with MDD, negative stimuli capture attention during later information processing; this engagement is associated with greater self-referent endorsement of negative adjectives. Sustained cognitive engagement for self-referent negative stimuli may be an important target for neurocognitive depression interventions.

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n =25) and healthy controls (n =25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.

Additive genetic contribution to symptom dimensions in major depressive disorder

Major depressive disorder (MDD) is a phenotypically heterogeneous disorder with a complex genetic architecture. In this study, genomic-relatedness-matrix restricted maximum-likelihood analysis (GREML) was used to investigate the extent to which variance in depression symptoms/symptom dimensions can be explained by variation in common single nucleotide polymorphisms (SNPs) in a sample of individuals with MDD (N = 1,558) who participated in the National Institute of Mental Health Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. A principal components analysis of items from the Hamilton Rating Scale for Depression (HRSD) obtained prior to treatment revealed 4 depression symptom components: (a) appetite, (b) core depression symptoms (e.g., depressed mood, anhedonia), (c) insomnia, and (d) anxiety. These symptom dimensions were associated with SNP-based heritability (hSNP2) estimates of 30%, 14%, 30%, and 5%, respectively. Results indicated that the genetic contribution of common SNPs to depression symptom dimensions were not uniform. Appetite and insomnia symptoms in MDD had a relatively strong genetic contribution whereas the genetic contribution was relatively small for core depression and anxiety symptoms. While in need of replication, these results suggest that future gene discovery efforts may strongly benefit from parsing depression into its constituent parts. (PsycINFO Database Record

Effectiveness of an internet intervention (Deprexis) for depression in a united states adult sample: A parallel-group pragmatic randomized controlled trial

OBJECTIVE

To examine the effectiveness of an Internet intervention for depression with a randomized, controlled trial in a large sample of adults recruited from the United States.

METHOD

The current study examines the effectiveness of Deprexis, an Internet treatment for depression that was provided with relatively minimal support. There were 376 treatment-seeking adults (mean age = 32 years; 74% female; 77% Caucasian, 7% Asian, 7% multiple races, 4% African American, and 11% Hispanic/Latino) with elevated depression (Quick Inventory of Depressive Symptoms-Self-Report [QIDS-SR] > = 10) who were randomized to receive an 8-week course of treatment immediately (n = 285) or after an 8-week delay (n = 91; i.e., waitlist control).

RESULTS

Intention-to-treat analyses indicated that treatment was associated with greater reduction in self-reported symptoms of depression (effect size d = .80) and 12 times greater likelihood of experiencing at least 50% symptom improvement compared with waitlist control. Similar effects were observed for several secondary outcomes, such as interviewer-rated depression symptoms, well-being, and depression-related disability. Treatment effects for symptoms of social anxiety, panic, and traumatic intrusions were relatively small.

CONCLUSION

Results suggest that Deprexis can produce symptomatic improvement among depressed adults recruited from the United States. Additional research is needed that examines whether improvements are maintained over time and who is particularly likely to respond to this form of treatment. (PsycINFO Database Record

Serotonin Transporter Genetic Variation is Differentially Associated with Reflexive- and Reflective-Optimal Learning

Learning to respond optimally under a broad array of environmental conditions is a critical brain function that requires engaging the cognitive systems that are optimal for solving the task at hand. Serotonin is implicated in learning and decision-making, but the specific functions of serotonin in system-level cognitive control remain unclear. Across 3 studies, we examined the influence of a polymorphism within the promoter region of the serotonin transporter gene (5-HTTLPR polymorphism in SLC6A4) on participants' ability to engage the task appropriate cognitive system when the reflexive (Experiments 1 and 2) or the reflective (Experiment 3) system was optimal. Critically, we utilized a learning task for which all aspects remain fixed with only the nature of the optimal cognitive processing system varying across experiments. Using large community samples, Experiments 1 and 2 (screened for psychiatric diagnosis) found that 5-HTTLPR S/LG allele homozygotes, with putatively lower serotonin transport functionality, outperformed LA allele homozygotes in a reflexive-optimal learning task. Experiment 3 used a large community sample, also screened for psychiatric diagnosis, and found that 5-HTTLPR LA homozygotes, with putatively higher serotonin transport functionality, outperformed S/LG allele homozygotes in a reflective-optimal learning task.

Attention bias modification for major depressive disorder: Effects on attention bias, resting state connectivity, and symptom change

Cognitive theories of depression posit that selective attention for negative information contributes to the maintenance of depression. The current study experimentally tested this idea by randomly assigning adults with Major Depressive Disorder (MDD) to 4 weeks of computer-based attention bias modification designed to reduce negative attention bias or 4 weeks of placebo attention training. Findings indicate that compared to placebo training, attention bias modification reduced negative attention bias and increased resting-state connectivity within a neural circuit (i.e., middle frontal gyrus and dorsal anterior cingulate cortex) that supports control over emotional information. Further, pre- to post-training change in negative attention bias was significantly correlated with depression symptom change only in the active training condition. Exploratory analyses indicated that pre- to post-training changes in resting state connectivity within a circuit associated with sustained attention to visual information (i.e., precuenus and middle frontal gyrus) contributed to symptom improvement in the placebo condition. Importantly, depression symptoms did not change differentially between the training groups-overall, a 40% decrease in symptoms was observed across attention training conditions. Findings suggest that negative attention bias is associated with the maintenance of depression; however, deficits in general attentional control may also maintain depression symptoms, as evidenced by resting state connectivity and depression symptom improvement in the placebo training condition.

Differential sensitivity to the environment: contribution of cognitive biases and genes to psychological wellbeing

Negative cognitive biases and genetic variation have been associated with risk of psychopathology in largely independent lines of research. Here, we discuss ways in which these dynamic fields of research might be fruitfully combined. We propose that gene by environment (G × E) interactions may be mediated by selective cognitive biases and that certain forms of genetic 'reactivity' or 'sensitivity' may represent heightened sensitivity to the learning environment in a 'for better and for worse' manner. To progress knowledge in this field, we recommend including assessments of cognitive processing biases; examining G × E interactions in 'both' negative and positive environments; experimentally manipulating the environment when possible; and moving beyond single-gene effects to assess polygenic sensitivity scores. We formulate a new methodological framework encapsulating cognitive and genetic factors in the development of both psychopathology and optimal wellbeing that holds long-term promise for the development of new personalized therapies.

A Preliminary Study of Genetic Variation in the Dopaminergic and Serotonergic Systems and Genome-wide Additive Genetic Effects on Depression Severity and Treatment Response

Major depression is a heritable disorder that is commonly treated with selective serotonin reuptake inhibitors. However, no study has quantified the overlap in genetic effects between pretreatment depression severity and treatment response and the extent to which genetic effects could be attributed to variation in the dopaminergic and serotonergic systems (DA/5-HT). Data (N=1618) from the clinician-rated Hamilton Rating Scale of Depression and the clinician-rated Quick Inventory of Depressive Symptomatology were obtained from participants of European ancestry in the Sequenced Treatment Alternatives to Relieve Depression clinical trial. Genetic variants explained 31%–64% of the variance across assessments of pretreatment depression severity and treatment response. However, effects from the DA/5-HT systems genes were negligible. There was also limited evidence for genetic overlap for pretreatment depression severity and treatment response. Despite the clear genetic contributions to these depression phenotypes, different genetic factors may contribute to depression severity and treatment response.

The effects of respiratory sinus arrhythmia on anger reactivity and persistence in major depression

The experience of anger during a depressive episode has recently been identified as a poor prognostic indicator of illness course. Given the clinical implications of anger in major depressive disorder (MDD), understanding the mechanisms involved in anger reactivity and persistence is critical for improved intervention. Biological processes involved in emotion regulation during stress, such as respiratory sinus arrhythmia (RSA), may play a role in maintaining negative moods. Clinically depressed (MDD; n = 49) and nondepressed (non-MDD; n = 50) individuals were challenged with a stressful computer task shown to increase anger, while RSA (high frequency range 0.15-0.4 Hz) was collected. RSA predicted future anger, but was unrelated to current anger. That is, across participants, low baseline RSA predicted anger reactivity during the task, and in depressed individuals, those with low RSA during the task had a greater likelihood of anger persistence during a recovery period. These results suggest that low RSA may be a psychophysiological process involved in anger regulation in depression. Low RSA may contribute to sustained illness course by diminishing the repair of angry moods.

Depression and Interpersonal Responses to Others' Moods: The Solicitation of Negative Information about Happy People

A total of four experiments tested the prediction that social comparison concerns lead depressed individuals to solicit more negative disclosures from happy people than they otherwise would. In Experiments 1 and 2, depressed, mildly depressed, and nondepressed subjects reviewed information about another person that included ratings of that person's mood (Experiment 2 also included a control condition without mood information). After reviewing the material, subjects chose a subset of items from a list of positive, negative, and neutral questions to ask the person in an upcoming meeting. Experiment 3 replicated this procedure but used a mood manipulation to define subject groups. Experiment 4 examined the impact of subjects' line of questioning on others. Taken together; the results indicate that when they believe their partner is happy, depressed individuals are especially likely to solicit negative information about that person. This unfavorable interpersonal response increases depressed individuals' risk of social rejection.

Transcranial Laser Stimulation as Neuroenhancement for Attention Bias Modification in Adults with Elevated Depression Symptoms

BACKGROUND

Low-level light therapy (LLLT) with transcranial laser is a non-invasive form of neuroenhancement shown to regulate neuronal metabolism and cognition. Attention bias modification (ABM) is a cognitive intervention designed to improve depression by decreasing negative attentional bias, but to date its efficacy has been inconclusive. Adjunctive neuroenhancement to augment clinical effectiveness has shown promise, particularly for individuals who respond positively to the primary intervention.

OBJECTIVE/HYPOTHESIS

This randomized, sham-controlled proof-of-principle study is the first to test the hypothesis that augmentative LLLT will improve the effects of ABM among adults with elevated symptoms of depression.

METHODS

Fifty-one adult participants with elevated symptoms of depression received ABM before and after laser stimulation and were randomized to one of three conditions: right forehead, left forehead, or sham. Participants repeated LLLT two days later and were assessed for depression symptoms one and two weeks later.

RESULTS

A significant three-way interaction between LLLT condition, ABM response, and time indicated that right LLLT led to greater symptom improvement among participants whose attention was responsive to ABM (i.e., attention was directed away from negative stimuli). Minimal change in depression was observed in the left and sham LLLT.

CONCLUSIONS

The beneficial effects of ABM on depression symptoms may be enhanced when paired with adjunctive interventions such as right prefrontal LLLT; however, cognitive response to ABM likely moderates the impact of neuroenhancement. The results suggest that larger clinical trials examining the efficacy of using photoneuromodulation to augment cognitive training are warranted.

BDNF Val66Met Polymorphism as a Moderator of Exercise Enhancement of Smoking Cessation Treatment in Anxiety Vulnerable Adults

BACKGROUND

Exercise interventions facilitate odds of quit success among high-anxiety sensitive adults smokers. We examined the dependency of these benefits on the genetic BDNF Val66Met (rs6265) polymorphism; individuals who are Met carriers have lower BDNF responses and reduced associated benefits from exercise. Accordingly, we hypothesized that the efficacy of vigorous exercise for smoking cessation would be specific to high-anxiety sensitive Val/Val carriers.

METHODS

Participants were adults (N=55) of European ancestry who had participated in a randomized controlled trial comparing a smoking cessation program augmented with exercise vs. augmented with a wellness control treatment. In this secondary analysis, growth curve models for point-prevalence abstinence (PPA) and prolonged abstinence (PA) employed for the main outcome analyses were amended to test the moderator effects of the BDNF Val66Met polymorphism.

RESULTS

Consistent with prediction, the advantage of exercise over control for PPA was significantly greater among high-anxiety sensitive persons with the Val/Val genotype than for those with the Val/Met genotype. This advantage did not reach statistical significance for PA. Differences in abstinence between the exercise and control interventions among low-anxiety sensitive smokers were not dependent on the BDNF Val66Met polymorphism.

CONCLUSIONS

We found that the efficacy of exercise for augmenting smoking cessation treatment is intensified among high-anxiety sensitive smokers who are Val/Val carriers. This observation is consistent with findings documenting BDNF mediation of exercise benefits and greater negative affect among smokers who are Val/Val carriers. These data encourage further evaluation of the association between the BDNF polymorphism, exercise, anxiety sensitivity, and smoking cessation.

Self-referential schemas and attentional bias predict severity and naturalistic course of depression symptoms

Attentional bias and self-referential schemas have been observed in numerous cross-sectional studies of depressed adults and are theorised to maintain negative mood. However, few longitudinal studies have examined whether maladaptive cognition predicts the course of depressive symptoms. Fifty-seven adults with elevated depression symptoms were assessed for negative attentional bias using a dot-probe task with eye-tracking and self-referential schemas using a self-referent encoding task. Participants subsequently completed five weekly depression symptom assessments. Participants with more negative self-referential schemas had higher baseline depression symptoms (r = .55). However, participants who spent more time attending to negative words showed greater symptom worsening over time (r = .42). The findings for negative self-referential schemas replicate past research, while the findings for negative attention bias represent the first evidence showing that attentional biases predict naturalistic symptom course. This work suggests that negative attention biases maintain depression symptoms and represent an important treatment target for neurocognitive therapeutics.

Attentional biases to emotional stimuli: Key components of the RDoC constructs of sustained threat and loss

Biased attention to emotional stimuli plays a key role in the RDoC constructs of Sustained Threat and Loss. In this article, we review approaches to assessing these biases, their links with psychopathology, and the underlying neural influences. We then review evidence from twin and candidate gene studies regarding genetic influences on attentional biases. We also discuss the impact of developmental and environmental influences and end with a number of suggestions for future research in this area.