Depression screening using a non-verbal self-association task: A machine-learning based pilot study

Yang S. Liu, Yipeng Song, Naomi A. Lee, Daniel M. Bennett, Katherine S. Button, Andrew Greenshaw, Bo Cao*, Jie Sui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Background: Effective screening is important to combat the raising burden of depression and opens a critical time window for early intervention. Clinical use of non-verbal depression screening is nascent, yet a promising and viable candidate to supplement verbal screening. Differential self- and emotion-processing in depression patients were previously reported by non-verbal behavioural assessments, corroborated by neuroimaging findings of distinct neuroanatomical markers. Thus non-verbal validated brain-behaviour based self-emotion-related assessment data reflect physiological differences and may support individual level screening of depression. Methods: In this pilot study (n = 84) we collected two longitudinal sessions of behavioural assessment data in a laboratory setting. Depression was assessed using Beck Depression Inventory II (BDI-II), to explore optimal screening methods with machine-learning, and to establish the validity of adapting a novel behavioural assessment focusing on self and emotions for depression screening. Results: The best machine-learning model achieved high performance in depression screening, 10-Fold cross-validation (CV) Area Under the receiver operating characteristic Curve (AUC) of 0.90 and balanced accuracy of 0.81, using a Gradient Boosting algorithm. Prospective prediction using a model trained with session 1 data to predict session 2 depression status achieved a 10-Fold CV AUC of 0.77 and balanced accuracy of 0.66. We also identified interpretable behavioural signatures for depression patients based on the best model. Conclusion: The study supports the utility of using behavioural data as a viable and cost-effective solution for depression screening, with a potential wide range of applications in clinical settings.

Original languageEnglish
Pages (from-to)87-95
Number of pages9
JournalJournal of Affective Disorders
Early online date10 May 2022
Publication statusPublished - 1 Aug 2022

Bibliographical note

Funding Information:
This research was undertaken, in part, thanks to funding from the Canada Research Chairs program (BC), Alberta Innovates (BC), Mental Health Foundation (BC), MITACS Accelerate program (YL. BC), Simon & Martina Sochatsky Fund for Mental Health (BC), the Alberta Synergies in Alzheimer's and Related Disorders (SynAD) program (YL, BC) and University of Alberta Hospital Foundation (BC). The funding sources had no impact on the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Data Availability Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.


  • Depression
  • Machine-learning
  • Matching technique
  • Self
  • Sensitive objective measurement


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