Psychological medicine · 2026
Computerized assessments of emotional expression and emotional reactivity predict negative symptoms in individuals at clinical high-risk for psychosis.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Bertrand Claire Emilie, Pokorny Victor J, Gold James M, Waltz James A, Schiffman Jason, Ellman Lauren M, Strauss Gregory P, Walker Elaine F, Woods Scott W, Powers Albert R, Kenney Joshua, Corlett Philip R, Silverstein Steven M, Mittal Vijay A
Аннотация
BACKGROUND: Negative symptoms are a core feature of psychosis and a strong predictor of functional outcome, yet they remain difficult to assess due to conceptual and methodological challenges. Although abnormalities in emotional expressivity and emotional reactivity are documented in individuals at clinical high-risk (CHR) for psychosis, these domains are typically examined independently, and their relationship remains unclear.
METHODS: Facial expressions were quantified using automated facial analysis (FaceReader) during clinical interviews in 101 CHR individuals and 41 healthy controls (HCs). Emotional reactivity was assessed using the International Affective Picture System (IAPS). Principal component analyses were conducted on facial expression and emotional reactivity variables within the CHR group. Associations with negative symptom domains, positive symptoms, and social functioning were examined using correlational and two-step regression analyses.
RESULTS: CHR participants showed greater disgust expression than HCs (g = 0.40, uncorrected p = .0025, FDR-corrected p = .023). Facial expression and emotional reactivity components showed minimal associations (p > .20). Reduced high-arousal facial expressions were associated with greater emotional expressivity deficits (r = -.22, p = .027), whereas greater happy facial expression was associated with more motivation and pleasure impairment (r = .21, p = .044). Happy facial expression explained additional variance in motivation symptoms beyond emotional reactivity (ΔR2 = .089, p = .008).
CONCLUSIONS: Automated facial expression captured variance in some negative symptom domains that was largely independent of emotional reactivity. These findings support the use of multimodal, objective assessments to improve characterization of negative symptoms in psychosis risk.
METHODS: Facial expressions were quantified using automated facial analysis (FaceReader) during clinical interviews in 101 CHR individuals and 41 healthy controls (HCs). Emotional reactivity was assessed using the International Affective Picture System (IAPS). Principal component analyses were conducted on facial expression and emotional reactivity variables within the CHR group. Associations with negative symptom domains, positive symptoms, and social functioning were examined using correlational and two-step regression analyses.
RESULTS: CHR participants showed greater disgust expression than HCs (g = 0.40, uncorrected p = .0025, FDR-corrected p = .023). Facial expression and emotional reactivity components showed minimal associations (p > .20). Reduced high-arousal facial expressions were associated with greater emotional expressivity deficits (r = -.22, p = .027), whereas greater happy facial expression was associated with more motivation and pleasure impairment (r = .21, p = .044). Happy facial expression explained additional variance in motivation symptoms beyond emotional reactivity (ΔR2 = .089, p = .008).
CONCLUSIONS: Automated facial expression captured variance in some negative symptom domains that was largely independent of emotional reactivity. These findings support the use of multimodal, objective assessments to improve characterization of negative symptoms in psychosis risk.