ArticleProceedings of the National Academy of Sciences of the United States of America2024
Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Computerized assessments of emotional expression and emotional reactivity predict negative symptoms in individuals at clinical high-risk for psychosis.Psychological medicine · 2026Article
- Distinct neural temporal architectures encode rapid social expressions and sustained internal mood states.bioRxiv : the preprint server for biology · 2025Article
- Quantitative semiology: harnessing AI-generated teaching signals in psychiatry.Journal of psychiatry & neuroscience : JPN · 2025Article
- Recognition of Basic Emotions Through Facial Expressions in Children with Attention-Deficit/Hyperactivity Disorder.Revista de neurologia · 2025Article
- Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.
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Registered trials
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