Automated analysis of clinical interviews indicates altered head movements during social interactions in youth at clinical high-risk for psychosis.

Journal: Schizophrenia (Heidelberg, Germany)
Published Date: May 27, 2025

Abstract

Alterations in social functioning are commonly observed in youth at clinical high risk (CHR) for psychosis. Previous research has focused on perception and interpretation of social stimuli. Assessments of social behavior have been limited and have typically been conducted using time-consuming, manual, and not always reliable methods. The current study aimed to characterize patterns of head movements, a critical feature of nonverbal social behavior, to determine alterations among CHR individuals, using novel automated tools. A total of 87 CHR and 90 healthy control youth completed video-recorded clinical interviews. Segments when participants were responding to questions were processed using an open-access machine learning-based head tracking program. This program extracted target variables such as total head movement, amplitude, and speed in each direction (x, y, and z). Relationships between head movement patterns and symptoms were then examined. Findings indicated that the CHR group exhibited the same amount of head movements as the control group, establishing that results did not reflect a more global deficit. Notably, the CHR group executed spontaneous head turns in side-to-side movements (such as the "no" gesture) at a significantly slower speed when compared to controls (U = 2860, p = 0019, d = -0.41). Slower side-to-side head movement was also associated with elevated clinician-rated scores of "disorganized communication" (r = -0.23), but not with other symptoms in the positive domain nor negative or depressive phenomenology. These findings provide new insights into alterations in social processes in individuals at CHR and highlight the promise of using automated tools to capture spontaneous head movements, thereby expanding the assessment of social behavior, communication, and applied social cognition.

Authors

  • Juliette Lozano-Goupil
    EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France. Electronic address: juliette.lozano-goupil@umontpellier.fr.
  • Tina Gupta
    Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Trevor F Williams
    Department of Psychological Science, Kent State University, Kent, OH, USA.
  • Amy E Pinkham
    Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA.
  • Claudia M Haase
    School of Education and Social Policy, Northwestern University, Evanston, IL, USA.
  • Stewart A Shankman
    Stephen M. Stahl Center for Psychiatric Neuroscience, Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA.
  • Vijay A Mittal
    Department of Psychology, Northwestern University, Evanston, IL, USA.

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