Disorder-specific versus transdiagnostic cognitive mechanisms in anxiety and depression: Machine-learning-based prediction of symptom severity.

INTRODUCTION: Psychiatric evaluation of anxiety and depression is currently based on self-reported symptoms and their classification into discrete disorders. Yet the substantial overlap between these disorders as well as their within-disorder heterogeneity may contribute to the mediocre success rates of treatments. The proposed research examines a new framework for diagnosis that is based on alterations in underlying cognitive mechanisms. In line with the Research Domain Criteria (RDoC) approach, the current study directly compares disorder-specific and transdiagnostic cognitive patterns in predicting the severity of anxiety and depression symptoms. METHODS: The sample included 237 individuals exhibiting differing levels of anxiety and depression symptoms, as measured by the STAI-T and BDI-II. Random Forest regressors were used to analyze their performance on a battery of six computerized cognitive-behavioral tests targeting selective and spatial attention, expectancy, interpretation, memory, and cognitive control biases. RESULTS: Unique anxiety-specific biases were found, as well as shared anxious-depressed bias patterns. These cognitive biases exhibited relatively high fitting rates when predicting symptom severity (questionnaire scores common range 0-60, MAE = 6.03, RMSE = 7.53). Interpretation and expectancy biases exhibited the highest association with symptoms, above all other individual biases. LIMITATIONS: Although internal validation methods were applied, models may suffer from potential overfitting due to sample size limitations. CONCLUSION: In the context of the ongoing dispute regarding symptom-centered versus transdiagnostic approaches, the current study provides a unique comparison of these two views, yielding a novel intermediate approach. The results support the use of mechanism-based dimensional diagnosis for adding precision and objectivity to future psychiatric evaluations.

The attention-emotion interaction in healthy female participants on oral contraceptives during 1-week escitalopram intake.

Previous findings in healthy humans suggest that selective serotonin reuptake inhibitors (SSRIs) modulate emotional processing via earlier changes in attention. However, many previous studies have provided inconsistent findings. One possible reason for such inconsistencies is that these studies did not control for the influence of either sex or sex hormone fluctuations. To address this inconsistency, we administered 20 mg escitalopram or placebo for seven consecutive days in a randomized, double-blind, placebo-controlled design to sixty healthy female participants with a minimum of 3 months oral contraceptive (OC) intake. Participants performed a modified version of an emotional flanker task before drug administration, after a single dose, after 1 week of SSRI intake, and after a 1-month wash-out period. Supported by Bayesian analyses, our results do not suggest a modulatory effect of escitalopram on behavioral measures of early attentional-emotional interaction in female individuals with regular OC use. While the specific conditions of our task may be a contributing factor, it is also possible that a practice effect in a healthy sample may mask the effects of escitalopram on the attentional-emotional interplay. Consequently, 1 week of escitalopram administration may not modulate attention toward negative emotional distractors outside the focus of attention in healthy female participants taking OCs. While further research in naturally cycling females and patient samples is needed, our results represent a valuable contribution toward the preclinical investigation of antidepressant treatment.

How Social Experiences Affect Interpretation Bias Among Individuals With Non-clinical Depression: The Role of Ostracism.

BACKGROUND: Extensive knowledge and research indicate that interpretation bias is very common among individuals with sub-clinical and clinical levels of depression. Nevertheless, little is known about the role of social experiences in enhancing interpretation bias. Given the major relevance of social experiences in the context of depression, the present study investigated the role of potential interactions between social experiences and levels of depression symptoms in the interpretation of ambiguous information. METHOD: Seventy participants underwent a laboratory controlled manipulation either of social ostracism or of overinclusion. Participants completed a computerized task that measured both direct and indirect interpretation bias and reported their level of depression symptoms. RESULTS: The findings show that ostracism enhanced interpretation bias when symptom levels were higher, while overinclusion did not. This interaction effect between social ostracism and symptom level was found both for direct and for indirect interpretation bias. CONCLUSION: Whereas previous research showed the existence of interpretation bias among people with symptoms of depression, the present study expands previous knowledge by shedding light on the conditions under which interpretation bias emerges, suggesting that ostracism enhances negative interpretation of ambiguous information when levels of depression symptoms are higher.

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions.

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper's overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders.

In light of the need for objective mechanism-based diagnostic tools, the current research describes a novel diagnostic support system aimed to differentiate between anxiety and depression disorders in a clinical sample. Eighty-six psychiatric patients with clinical anxiety and/or depression were recruited from a public hospital and assigned to one of the experimental groups: Depression, Anxiety, or Mixed. The control group included 25 participants with no psychiatric diagnosis. Participants performed a battery of six cognitive-behavioral tasks assessing biases of attention, expectancies, memory, interpretation and executive functions. Data were analyzed with a machine-learning (ML) random forest-based algorithm and cross-validation techniques. The model assigned participants to clinical groups based solely on their aggregated cognitive performance. By detecting each group's unique performance pattern and the specific measures contributing to the prediction, the ML algorithm predicted diagnosis classification in two models: (I) anxiety/depression/mixed vs. control (76.81% specificity, 69.66% sensitivity), and (II) anxiety group vs. depression group (80.50% and 66.46% success rates in classifying anxiety and depression, respectively). The findings demonstrate that the cognitive battery can be utilized as a support system for psychiatric diagnosis alongside the clinical interview. This implicit tool, which is not based on self-report, is expected to enable the clinician to achieve increased diagnostic specificity and precision. Further, this tool may increase the confidence of both clinician and patient in the diagnosis by equipping them with an objective assessment tool. Finally, the battery provides a profile of biased cognitions that characterizes the patient, which in turn enables more fine-tuned, individually-tailored therapy.

Using machine learning-based analysis for behavioral differentiation between anxiety and depression.

Anxiety and depression are distinct-albeit overlapping-psychiatric diseases, currently diagnosed by self-reported-symptoms. This research presents a new diagnostic methodology, which tests rigorously for differences in cognitive biases among subclinical anxious and depressed individuals. 125 participants were divided into four groups based on the levels of their anxiety and depression symptoms. A comprehensive behavioral test battery detected and quantified various cognitive-emotional biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The prediction model for differentiating between symptomatic participants (i.e., high symptoms of depression, anxiety, or both) compared to the non-symptomatic control group revealed a 71.44% prediction accuracy for the former (sensitivity) and 70.78% for the latter (specificity). 68.07% and 74.18% prediction accuracy was obtained for a two-group model with high depression/anxiety, respectively. The analysis also disclosed which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.

Neural correlates of emotion-attention interactions: From perception, learning, and memory to social cognition, individual differences, and training interventions.

Due to their ability to capture attention, emotional stimuli tend to benefit from enhanced perceptual processing, which can be helpful when such stimuli are task-relevant but hindering when they are task-irrelevant. Altered emotion-attention interactions have been associated with symptoms of affective disturbances, and emerging research focuses on improving emotion-attention interactions to prevent or treat affective disorders. In line with the Human Affectome Project's emphasis on linguistic components, we also analyzed the language used to describe attention-related aspects of emotion, and highlighted terms related to domains such as conscious awareness, motivational effects of attention, social attention, and emotion regulation. These terms were discussed within a broader review of available evidence regarding the neural correlates of (1) Emotion-Attention Interactions in Perception, (2) Emotion-Attention Interactions in Learning and Memory, (3) Individual Differences in Emotion-Attention Interactions, and (4) Training and Interventions to Optimize Emotion-Attention Interactions. This comprehensive approach enabled an integrative overview of the current knowledge regarding the mechanisms of emotion-attention interactions at multiple levels of analysis, and identification of emerging directions for future investigations.