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Quantifying facial affect changes in psychotic disorders with machine learning.

Jayson Jeganathan¹, Renate Thienel², Michael Breakspear³

¹School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, NSW, Australia; Hunter Medical Research Institute, Newcastle, NSW, Australia.

Psychiatry Research

|October 15, 2025

Summary

This summary is machine-generated.

Machine learning revealed distinct facial emotion patterns in psychosis. Individuals with psychosis showed altered facial muscle activity, impacting diagnosis and treatment monitoring for negative symptoms.

Keywords:

Emotion Facial action units Facial affect Facial expression Hidden Markov model Negative symptoms Psychosis Schizophrenia

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Area of Science:

Computational psychiatry
Affective computing
Neuroscience of emotion

Background:

Reduced facial expressivity is a hallmark of negative symptoms in psychotic disorders like schizophrenia.
Traditional assessment relies on subjective rating scales.
Novel machine learning approaches offer objective evaluation of facial emotions.

Purpose of the Study:

To employ machine learning and systems modeling for evaluating facial emotions in individuals with psychosis.
To identify dynamic facial affective patterns and their relationship to symptoms.

Main Methods:

Acquired video data of 48 participants with psychosis and 40 controls watching comedy.
Extracted facial action unit time series using OpenFace software.
Applied continuous wavelet transform and hidden Markov models (HMM) to identify 8 dynamic facial affective patterns.

Main Results:

Individuals with psychosis exhibited reduced activation in positive affect facial muscles and increased negative affect activity.
Psychosis group showed less time in negative HMM states and more time in low activity states.
Time in negative states correlated with positive symptoms; low activity state persistence linked to negative symptoms.

Conclusions:

Facial affect changes in psychosis are complex, encompassing both static and dynamic alterations.
Machine learning quantification of facial changes may enhance diagnostic differentiation.
Objective facial analysis could aid in monitoring treatment efficacy for negative symptoms.