Search

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., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

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. 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. 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. Although internal validation methods were applied, models may suffer from potential overfitting due to sample size limitations. 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. • New framework for diagnosis, based on underlying cognitive mechanisms • Test battery measured six different cognitive biases for 237 participants. • Machine-learning algorithm predicted symptoms severity based on performance. • High fitting rates for prediction of transdiagnostic and disorder-specific symptoms • Endorsing intermediate approach bridging symptom-centred and transdiagnostic methods [ABSTRACT FROM AUTHOR]