A method for diagnosing depression: Facial expression mimicry is evaluated by facial expression recognition

Fu, Gang; Yu, Y. H.; Ye, Jiayu; Zheng, Yunshao; Li, Wentao; Cui, Ning; Wang, Qingxiang

doi:10.1016/j.jad.2022.12.029

Cited by 17 publications

(4 citation statements)

References 33 publications

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“…EEG studies would provide invaluable information with regard to the cognitive processing associated with facial feedback inhibition due to its superb temporal resolution, and putative markers of interest may include the functional connectivity of face processing regions [ 43 , 44 ], frontal EEG asymmetry [ 45 ], and EEG features of emotion recognition [ 46 ] and microexpressions [ 47 ]. The selection of behavioral functional tasks with good internal validity to test the facial feedback hypothesis is also a topic for discussion; these may include voluntary facial action in response to stimuli of a certain valence (e.g., negative vs. positive) [ 48 ], mirror feedback tasks [ 49 ], facial mimicry tasks [ 50 , 51 ], pen-in-mouth experiments [ 50 ], and even emotional language tasks [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

Botulinum Toxin Injections for Psychiatric Disorders: A Systematic Review of the Clinical Trial Landscape

Demchenko,

Swiderski,

Liu

et al. 2024

Toxins

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Botulinum toxin type A (BONT-A) has shown promise in improving the mood-related symptoms of psychiatric disorders by targeting muscles linked to the expression of negative emotions. We conducted a systematic review of past and ongoing efficacy trials of BONT-A therapy for psychiatric disorders to identify relevant trends in the field and discuss the refinement of therapeutic techniques. A comprehensive search for published clinical trials using BONT-A injections for psychiatric disorders was performed on 4 May 2023 through OVID databases (MEDLINE, Embase, APA PsycINFO). Unpublished clinical trials were searched through the ClinicalTrials.gov and International Clinical Trial Registry Platform public registries. The risk of bias was assessed using the JBI Critical Appraisal tools for use in systematic reviews. We identified 21 studies (17 published, 4 unpublished clinical trials) involving 471 patients. The studies focused on evaluating the efficacy of BONT-A for major depressive, borderline personality, social anxiety, and bipolar disorders. BONT-A was most commonly injected into the glabellar area, with an average dose ranging between 37.75 U and 44.5 U in published studies and between 32.7 U and 41.3 U in unpublished trials. The results indicated significant symptom reductions across all the studied psychiatric conditions, with mild adverse effects. Thus, BONT-A appears to be safe and well-tolerated for psychiatric disorders of negative affectivity. However, despite the clinical focus, there was a noted shortage of biomarker-related assessments. Future studies should focus on pursuing mechanistic explorations of BONT-A effects at the neurobiological level.

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Section: Discussionmentioning

confidence: 99%

Botulinum Toxin Injections for Psychiatric Disorders: A Systematic Review of the Clinical Trial Landscape

Demchenko,

Swiderski,

Liu

et al. 2024

Toxins

View full text Add to dashboard Cite

show abstract

“…While previous research has primarily focused on facial expression recognition ability, this study takes a step forward by directly capturing individuals’ facial expressions [ 47 , 48 ]. Other studies have explored participants’ facial expressions, but they have also relied on human raters, which may introduce biases [ 49 ]. In contrast, our study utilizes a standardized analysis method to examine nonverbal features of depressive symptoms, providing a more objective approach.…”

Section: Discussionmentioning

confidence: 99%

Facial Expressions Track Depressive Symptoms in Old Age

Kim

Kwak

Yoo

et al. 2023

Sensors

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Facial expressions play a crucial role in the diagnosis of mental illnesses characterized by mood changes. The Facial Action Coding System (FACS) is a comprehensive framework that systematically categorizes and captures even subtle changes in facial appearance, enabling the examination of emotional expressions. In this study, we investigated the association between facial expressions and depressive symptoms in a sample of 59 older adults without cognitive impairment. Utilizing the FACS and the Korean version of the Beck Depression Inventory-II, we analyzed both “posed” and “spontaneous” facial expressions across six basic emotions: happiness, sadness, fear, anger, surprise, and disgust. Through principal component analysis, we summarized 17 action units across these emotion conditions. Subsequently, multiple regression analyses were performed to identify specific facial expression features that explain depressive symptoms. Our findings revealed several distinct features of posed and spontaneous facial expressions. Specifically, among older adults with higher depressive symptoms, a posed face exhibited a downward and inward pull at the corner of the mouth, indicative of sadness. In contrast, a spontaneous face displayed raised and narrowed inner brows, which was associated with more severe depressive symptoms in older adults. These findings suggest that facial expressions can provide valuable insights into assessing depressive symptoms in older adults.

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“…Facial expression recognition has a wide range of applications in daily life. For example, in medical applications (Fu et al, 2023; Jiang et al, 2020; Liu et al, 2019), psychologists determine a patient's condition by constantly observing changes in the patient's facial expressions to suggest a treatment plan. In the application of fatigue driving (Abbas et al, 2022; Xiao et al, 2022), if the in‐vehicle detection system detects signs of fatigue driving in the driver's facial expressions, it will emit an alarm to remind the driver.…”

Section: Introductionmentioning

confidence: 99%

A multi‐scale feature fusion convolutional neural network for facial expression recognition

Zhang,

Fu,

et al. 2023

Expert Systems

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This paper designs a new facial Expression recognition network called a multi‐scale feature Fusion Convolutional neural Network (EFCN). This network is proposed to solve two problems in the facial expression recognition task. First, there are many commonalities between faces of different expression categories, and the recognition task cannot be precisely performed when the commonality is greater than the individuality. Secondly, facial detail features have a significant impact on the final results of expression recognition, while the image detail features extracted by traditional convolutional neural networks are not sufficient. In order to address the above issues, the feature enhancement network (FEN) and the detail information enhancement module (DEM) are designed. The FEN fuses deep and shallow features. Accordingly, the feature map contains richer information, making it easy to identify the samples. The DEM extracts and fuses the features passed by the backbone network with multi‐scale features to enhance the network's ability to extract features from small regions of the face. We validated the proposed method on three datasets, RAF‐DB, CK+, and JAFFE, and achieved 84.50%, 97.86%, and 91.05% accuracy, respectively, and the experimental results showed the effectiveness of the proposed method in this paper. For example, on the JAFFE dataset, the recognition accuracy of this method surpasses the MLT method by 1.87%.

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A method for diagnosing depression: Facial expression mimicry is evaluated by facial expression recognition

Cited by 17 publications

References 33 publications

Botulinum Toxin Injections for Psychiatric Disorders: A Systematic Review of the Clinical Trial Landscape

Botulinum Toxin Injections for Psychiatric Disorders: A Systematic Review of the Clinical Trial Landscape

Facial Expressions Track Depressive Symptoms in Old Age

A multi‐scale feature fusion convolutional neural network for facial expression recognition

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