Specificity and overlap of attention and memory biases in depression

Marchetti, Igor; Everaert, Jonas; Dainer-Best, Justin; Loeys, Tom; Beevers, Christopher G.; Koster, Ernst H. W.

doi:10.1016/j.jad.2017.08.037

Cited by 81 publications

(60 citation statements)

References 75 publications

(93 reference statements)

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“…Another important finding from the current study was that negative attention bias, measured with reaction time or eye tracking, was not reliably associated with any of the depression symptoms. This finding is consistent with prior work documenting that negative attention bias (in contrast with memory bias) measured with different approaches was not strongly correlated with symptom severity across four studies involving 463 adult participants Association between negative 34 (Marchetti et al, 2018). Similarly, in a study that used best subsets regression to identify SRET and dot-probe metrics associated with concurrent depression severity, number of positive and negative words endorsed from the SRET were the best predictors of total score depression severity.…”

Section: Discussionsupporting

confidence: 89%

“…Further, cross-validated results found no reliable depression symptom predictors of negative attention bias, regardless if attention bias was measured with reaction time or eye tracking. These findings align with recent evidence suggesting that negative attention bias as measured with the dot-probe task may not track with concurrently measured depression symptom severity (Marchetti et al, 2018). Nevertheless, this study highlights the importance of cross-validating results and that negative self-referent processing (as measured with the SRET) may be most relevant for the maintenance of specific symptoms of depression, such as sadness.…”

Section: Discussionsupporting

confidence: 86%

“…Consistent with this idea, an emerging literature indicates that depression symptoms may be differentially linked to cognitive risk factors (Marchetti, Loeys, Alloy, & Koster, 2016), cognitive biases (Marchetti et al, 2018), and psychosocial functioning in adults (Fried & Nesse, Association between negative 6 2014). Prior work (Fried, Nesse, Zivin, Guille, & Sen, 2014) suggests some risk factors (i.e., depression history, childhood stress, sex, stressful life events) predict the development of only a subset of depression symptoms (and the associations with symptoms differed across risk factors).…”

Section: Association Between Negative Cognitive Bias and Depression: mentioning

confidence: 93%

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

Beevers¹,

Mullarkey²,

Dainer-Best³

et al. 2018

Preprint

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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 it is unlikely that all symptoms are 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 identifythe most important depression symptom predictors of each negative cognitive bias. Depression symptoms were measured with the Beck Depression Inventory – II. Performance of models was evaluated using predictive R-squared (𝑅2 𝑝𝑟𝑒𝑑), 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 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. General Scientific Summary: This study finds that many symptoms of depression are not strongly associated with thinking negatively about oneself or attending to negative information. This implies that negative cognitive biases may not be strongly associated with depression per se, but may instead contribute to the maintenance of specific depression symptoms, such as sadness, self-dislike, pessimism, feelings of punishment, and indecision.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 86%

Section: Association Between Negative Cognitive Bias and Depression: mentioning

confidence: 93%

See 1 more Smart Citation

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

Beevers¹,

Mullarkey²,

Dainer-Best³

et al. 2018

Preprint

Self Cite

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“…These pieces of evidence are of great interest, in that they could indicate a viable way to instill positive expectations about the future. Previous literature has shown that pessimism/hopelessness is strongly related to negative memory bias (Marchetti, Everaert, et al, 2018) and reduced capability to simulate one's own future is associated with diminished recall of specific personal memories (i.e., overgeneral autobiographical memory; Schacter, Addis, & Buckner, 2008;Williams et al, 1996). Hence, it is possible to speculate that targeting biased memory processing could help improving future expectations.…”

Section: Discussionmentioning

confidence: 99%

Hopelessness: A Network Analysis

Marchetti

2018

Cogn Ther Res

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Hopelessness is a complex phenomenon with important clinical consequences, such as depression and suicidality. Despite its major impact on mental health, little is known about the structure of hopelessness. In this study, hopelessness was investigated in a large community sample (n = 1985), recruited to be highly representative of general population in Germany. In the context of network analysis, state-of-the-art techniques were adopted (i) to investigate which thoughts and beliefs (nodes) are the most central ones and (ii) to shed light on the specific associations (edges) among them. Stability and accuracy were also checked to ensure trustworthiness of the findings. The analyses revealed that expecting more negative than positive future events and having important goals blocked along with feelings of giving-up were the most central elements of hopelessness. Moreover, being unable to imagine the future and perceiving it as vague and uncertain were both coupled with anticipating a dark future.Theoretical and clinical consequences of this study were discussed.

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“…Numerous studies employing the extinction‐reinstatement model have provided valuable understandings into the neurobiology of drug addiction, although lacked one element of drug addiction in humans, ie, adverse consequences of drug‐seeking . To address this issue, which is of particular importance in the case of comorbid depression because of the cognitive bias towards negative information found in depressed patients, the “conflict model” developed in our lab was employed in the present study. This model allows measurement of cocaine abstinence and relapse in the SA procedure, where cocaine‐seeking behavior requires an adverse consequence of crossing an electrified grid floor.…”

Section: Discussionmentioning

confidence: 99%

Increased relapse to cocaine‐seeking in a genetic model for depression

et al. 2019

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The greatest difficulty in treating cocaine addiction is the enormous rates of relapse, which occur despite immense negative consequences. Relapse risks are even greater in addicts with comorbid depression, perhaps because they use drugs to alleviate depressive symptoms. Only a few preclinical studies have examined this comorbidity, mostly exploring depressive-like effects following drug exposure. We examined rats from two different depression-like models: (a) chronic-mild-stress (CMS), which respond to antidepressant medications and (b) depressed-rat-line (DRL), a genetic model of selective breeding, which is less responsive to antidepressant medications.We tested addictive behaviors in a cocaine self-administration procedure, including the "conflict model," where drug-seeking and relapse encounter adverse consequences: an electrified grid in front of the drug-delivering lever. Following behavioral testing, we explored a potential association between behavioral outcomes and protein expression of brain-derived neurotrophic factor (BDNF). We found that DRL rats self-administer more cocaine compared with both CMS and controls, while CMS and control groups did not differ significantly. Notably, DRL but not CMS rats, displayed higher rates of relapse than controls, and expressed higher levels of BDNF in the prelimbic cortex (PLC). Potential translation of these results suggest that medication-resistant depressed patients tend to consume more drugs and are more susceptible to relapse. The increase in PLC BDNF levels is consistent with previous rat models of depression, and concomitantly, with its suggested role in promoting cocaine-seeking.

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Specificity and overlap of attention and memory biases in depression

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

Cited by 81 publications

References 75 publications

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

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

Hopelessness: A Network Analysis

Increased relapse to cocaine‐seeking in a genetic model for depression

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