Facial Expression May Indicate Depression in Older Adults

Scoralick, Francisca Magalhães; Piazzolla, Luciana Paganini; Camargos, Einstein Francisco; Freitas, Marco Polo Dias; Guimarães, Renato Maia; Laiana, Camila

doi:10.1111/j.1532-5415.2012.04169.x

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…This finding is in line with previous studies that found that individuals with some mental illnesses have fewer facial movements than healthy people due to alexithymia (43,44). An alternative explanation would be that compared with healthy people, people with poorer mental health status are more likely to produce (45) and express (46) negative emotions under neutral stimulation. Although each model had significant criterion validity, it is noteworthy that the depression model and anxiety model had lower criterion validity than the other symptomatic dimensions.…”

Section: Discussionsupporting

confidence: 92%

Identifying Psychological Symptoms Based on Facial Movements

Wang

Zhou

et al. 2020

Front. Psychiatry

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Background: Many methods have been proposed to automatically identify the presence of mental illness, but these have mostly focused on one specific mental illness. In some non-professional scenarios, it would be more helpful to understand an individual's mental health status from all perspectives.Methods: We recruited 100 participants. Their multi-dimensional psychological symptoms of mental health were evaluated using the Symptom Checklist 90 (SCL-90) and their facial movements under neutral stimulation were recorded using Microsoft Kinect. We extracted the time-series characteristics of the key points as the input, and the subscale scores of the SCL-90 as the output to build facial prediction models. Finally, the convergent validity, discriminant validity, criterion validity, and the split-half reliability were respectively assessed using a multitrait-multimethod matrix and correlation coefficients.Results: The correlation coefficients between the predicted values and actual scores were 0.26 and 0.42 (P < 0.01), which indicated good criterion validity. All models except depression had high convergent validity but low discriminant validity. Results also indicated good levels of split-half reliability for each model [from 0.516 (hostility) to 0.817 (interpersonal sensitivity)] (P < 0.001).Conclusion: The validity and reliability of facial prediction models were confirmed for the measurement of mental health based on the SCL-90. Our research demonstrated that fine-grained aspects of mental health can be identified from the face, and provided a feasible evaluation method for multi-dimensional prediction models.

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Section: Discussionsupporting

confidence: 92%

Identifying Psychological Symptoms Based on Facial Movements

Wang

Zhou

et al. 2020

Front. Psychiatry

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“…Previous studies of facial expression activity in depressed patients by neuromyography have shown that depressed patients have reduced facial muscle activity compared to normal individuals, suggesting that neutral and sad facial expressions occur more frequently in individuals with depressive symptoms [23]. In addition, it has been suggested that when the severity of depressive symptoms is higher, participants have reduced smile responses [24][25][26], tighter corners of the mouth [24], more frowning [26], shorter transient intervals [25] and more facial expressions related to contempt [27]. This makes it possible to explore the facial expression characteristics exhibited by adolescents with depression when they are faced with parental conflict situations.…”

Section: Introductionmentioning

confidence: 99%

Physiological Responses during Parental Conflicts as Potential Biomarkers for Adolescent Depression

et al. 2023

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Adolescents are a vulnerable population with a high prevalence of depression, yet there is a scarcity of biological markers for diagnosing depression specifically in this age group. In this case–control study, we examined physiological responses and facial expressions in adolescents with depression compared to healthy controls during parental conflict to identify potential biomarkers for adolescent depression. We recruited 33 families with adolescents diagnosed with depression and 25 families with healthy adolescents, matched for gender, age, and education. Baseline physiological measures, including electrocardiography (ECG), electrodermal activity (EDA), and respiration (RESP). During a 30 min parental conflict discussion, recorded on video, we analyzed the adolescents’ responses. The major depressive disorder (MDD) group displayed higher baseline heart rate (HR) and lower respiratory sinus arrhythmia (RSA). During the conflict discussion, they showed increased HR and shorter tonic periods of EDA compared to the healthy group. Facial expressions of both groups included neutral, sad, angry, and surprised. The MDD group exhibited fewer happy expressions. Receiver operating characteristic (ROC) curve analysis indicated that HR, interbeat interval (IBI), average NN interval (AVNN), number of NN50 intervals (NN50), and percentage of NN50 intervals (pNN50) had diagnostic potential for adolescent depression, with an area under the curve (AUC) greater than 0.7. Our findings suggest that adolescents with depression experience heightened sympathetic activation (higher HR) and weakened parasympathetic activity (lower RSA and HRV). These biomarkers hold promise for diagnosing adolescent depression.

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“…Studies found that a depression group opens their eyes smaller, have a longer duration of blinking [ 26 ], and have less eye contact with others [ 27 ] compared with a general group or a cured group. Other studies used facial activities to effectively predict people’s depression levels [ 28 , 29 , 30 ]. Moreover, the integration of different features brings a better prediction effect than a single feature [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of a Depressive State in Primary Care

Liu

Wang

2022

Healthcare

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The Center for Epidemiologic Studies Depression Scale (CES-D) performs well in screening depression in primary care. However, people are looking for alternatives because it screens for too many items. With the popularity of social media platforms, facial movement can be recorded ecologically. Considering that there are nonverbal behaviors, including facial movement, associated with a depressive state, this study aims to establish an automatic depression recognition model to be easily used in primary healthcare. We integrated facial activities and gaze behaviors to establish a machine learning algorithm (Kernal Ridge Regression, KRR). We compared different algorithms and different features to achieve the best model. The results showed that the prediction effect of facial and gaze features was higher than that of only facial features. In all of the models we tried, the ridge model with a periodic kernel showed the best performance. The model showed a mutual fund R-squared (R2) value of 0.43 and a Pearson correlation coefficient (r) value of 0.69 (p < 0.001). Then, the most relevant variables (e.g., gaze directions and facial action units) were revealed in the present study.

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Facial Expression May Indicate Depression in Older Adults

Cited by 4 publications

References 8 publications

Identifying Psychological Symptoms Based on Facial Movements

Identifying Psychological Symptoms Based on Facial Movements

Physiological Responses during Parental Conflicts as Potential Biomarkers for Adolescent Depression

Automatic Identification of a Depressive State in Primary Care

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