Facial and Vocal Biomarkers of Emotional Expressivity in Post-traumatic Stress Disorder Público
Hoffman, Kevin (Spring 2024)
Published
Abstract
Posttraumatic stress disorder (PTSD) is characterized by a range of symptoms, including those that reflect exaggerated fear-related responses and deficits in feeling positive emotion. However, machine learning-based approaches have not yet examined how this difference in emotional expressivity of PTSD manifests in the face, which may differ from self-reported measures of emotion in PTSD patients. Recently, computer vision, semantic, and acoustic analysis has been limited to differentiate between PTSD and depression. We correlated biomarkers of facial expressivity, emotional expressivity, and auditory expressivity with PTSD severity using the Clinician Administered PTSD Scale DSM-5 (CAPS-5). Fifty-nine adults (mean age = 24.58; 56 women) were recruited. The Clinician-Administered PTSD Scale for DSM-5 (CAPS) was administered prior to the start of a mindfulness intervention in which the participant’s face and voice were recorded. The open-source Python library OpenWillis was used to analyze overall facial expressivity. Additionally, the degree of emotional expressivity of happiness, sadness, anger, fear, disgust, surprise, and neutral were quantified as proportions of total time during the CAPS-5. Auditory variables of pause duration, silence-to-speech ratio, and rate of speech were also collected. Bivariate Pearson’s correlations were conducted to compare CAPS-5 data with expressivity variables as well as analyze moderating variables of gender. Overall facial expressivity was negatively correlated with increased PTSD re-experiencing symptoms, which might indicate higher reactivity. Fear expression was positively correlated with overall PTSD severity. The rate of speech was increased with higher re-experiencing symptoms while formant 1 variance was reduced in individuals with higher hyperarousal symptoms. These differences between specific emotions and PTSD symptom clusters validates our approach in assessing multiple emotional domains. Analysis of the expressivity of emotion types as well as how these connect to symptom change will allow us to gain a mechanistic understanding of how these emotions relate to symptomology and could potentially yield facial expression as a predictor of intervention success.
Table of Contents
Table of Contents
I. Introduction 1
A. PTSD 1
a. Overview 1
b. PTSD Symptom Clusters PTSD 1
c. Neurophysiology-based Biomarkers of PTSD 2
d. Social Disruptions in PTSD 3
e. Current Treatments PTSD 4
B. Emotional Expressivity 4
a. Alexithymia and Emotional Regulation in PTSD 4
b. Emotional Expressivity Deficits in PTSD 6
c. Biomarkers of Emotional Expressivity for Prediction of Later PTSD or Treatment Response 7
C. Emotion Biomarkers for PTSD 7
a. Facial Action Coding System 7
b. Facial Expression and PTSD 9
c. Speech Characteristics in PTSD 10
d. Vocal Acoustics in PTSD 11
e. Formant Frequencies in PTSD 12
D. Artificial Intelligence Innovations in Assessing Facial Expressivity and Vocal/Acoustic Biomarkers of PTSD 13
a. Benefits of the Use of AI 13
b. Current Use of AI in Identifying Biomarkers of Psychopathy and Treatment Response 14
E. Present Study 16
a. Objective 16
b. Hypotheses 16
II. Materials and Methods 17
A. Participants 17
a. Recruitment 17
b. Inclusion and Exclusion Criteria 17
B. Clinical Assessment 18
C. Video Data Processing 19
D. Measurement of Facial Expressivity 19
E. Measurement of Vocal Expressivity 20
F. Data Analyses 22
III. Results 23
A. Demographics 23
B. Facial and Vocal Expressivity Descriptive Statistics 24
a. Facial Expressivity 24
b. Vocal Expressivity 25
C. Associations between Facial Expressivity and PTSD Symptoms 26
D. Associations between Vocal Expressivity and PTSD Symptoms 31
F. Correlations between Facial and Vocal Biomarkers 36
IV. Discussion 37
A. Review of Study 37
B. Facial 38
C. Vocal 40
D. Correlations between Facial and Vocal Biomarkers 42
E. Limitations 43
F. Conclusions and Future Directions 44
V. Supplemental Tables 46
VI. Bibliography 49
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