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

Thalia Richter, Shahar Stahi, Gal Mirovsky, Hagit Hel-Or, Hadas Okon-Singer

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)473-482
Number of pages10
JournalJournal of Affective Disorders
StatePublished - 1 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier B.V.


  • Anxiety
  • Cognitive biases
  • Depression
  • Diagnosis
  • Machine-learning
  • Research domain criteria (RDoC)

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Clinical Psychology


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