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

Journal: Journal of affective disorders
Published Date:

Abstract

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.

Authors

  • Yang S Liu
    Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
  • Yipeng Song
    Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada.
  • Naomi A Lee
    School of Psychology, University of Aberdeen, Aberdeen, Scotland, United Kingdom.
  • Daniel M Bennett
    School of Psychology, University of Aberdeen, Aberdeen, Scotland, United Kingdom.
  • Katherine S Button
    Department of Psychology, University of Bath, Bath, England, United Kingdom.
  • Andrew Greenshaw
    Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
  • Bo Cao
    Department of Psychiatry, University of Alberta, Edmonton, Canada.
  • Jie Sui
    School of Psychology, University of Aberdeen, Aberdeen, UK. Electronic address: jie.sui@abdn.ac.uk.