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Supplementary Material for: Facial Expression and Predicting and Monitoring Response to Depression Treatment: A Systematic Review

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posted on 2026-01-08, 12:55 authored by figshare admin kargerfigshare admin karger, Plevin D., Hartmann S., Schubert K.O., Chen L., Clark S.R.
Introduction: The identification of biomarkers for treatment response in major depression is critical to the further development of personalised treatment. There is a recognised relationship between facial expression and depression of mood, and previous literature also indicates that facial expression is associated with treatment outcomes in depression. This suggests that facial expression may have use as a biomarker for treatment response. There is no previous synthesis of related research to drive the development of new digital approaches. Methods: We conducted a systematic review using three databases (Medline, Scopus and PsycInfo), identifying English-language publications (journal articles or books) that assessed either facial muscle activity or expression as predictors of treatment response or correlates of treatment outcome in depression. Risk of bias was assessed using a National Institutes of Health quality assessment tool. Results: We identified 12 studies, involving a total of 389 participants, which used a variety of different assessment methods and thus assessment outcomes, including electromyography (EMG), observer-related assessments (including Facial Action Coding System, FACS), and automated tools of facial expression assessment. Depression treatment response correlated with an increase in facial expressivity. Greater activity in the corrugator and zygomatic muscles, and lower levels of lip tightening and downward lip movement, may predict treatment response. Conclusions: Included studies were limited by heterogeneity in facial expression assessment tools and outcomes, along with demographic homogeneity. The findings of the review suggest that facial expression analysis may offer an avenue for biomarkers of depression status and treatment response prediction.

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