Facial Emotion Recognition in Schizophrenia

Gao, Zhiyun; Zhao, Wentao; Li, Sha; Liu, Zhifen; Yang, Chongguang; Xu, Yong

doi:10.3389/fpsyt.2021.633717

Cited by 52 publications

(32 citation statements)

References 63 publications

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“…Facial expression is also a good vehicle of emotional signals [44,45]. Since part of the face is covered with the VR goggles, the inference of emotion has to be made through mouth and lips shape [46,47].…”

Section: Data Integration and Processingmentioning

confidence: 99%

Digital Technologies for Innovative Mental Health Rehabilitation

et al. 2021

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Schizophrenia is a chronic mental illness, characterized by the loss of the notion of reality, failing to distinguish it from the imaginary. It affects the patient in life’s major areas, such as work, interpersonal relationships, or self-care, and the usual treatment is performed with the help of anti-psychotic medication, which targets primarily the hallucinations, delirium, etc. Other symptoms, such as the decreased emotional expression or avolition, require a multidisciplinary approach, including psychopharmacology, cognitive training, and many forms of therapy. In this context, this paper addresses the use of digital technologies to design and develop innovative rehabilitation techniques, particularly focusing on mental health rehabilitation, and contributing for the promotion of well-being and health from a holistic perspective. In this context, serious games and virtual reality allows for creation of immersive environments that contribute to a more effective and lasting recovery, with improvements in terms of quality of life. The use of machine learning techniques will allow the real-time analysis of the data collected during the execution of the rehabilitation procedures, as well as enable their dynamic and automatic adaptation according to the profile and performance of the patients, by increasing or reducing the exercises’ difficulty. It relies on the acquisition of biometric and physiological signals, such as voice, heart rate, and game performance, to estimate the stress level, thus adapting the difficulty of the experience to the skills of the patient. The system described in this paper is currently in development, in collaboration with a health unit, and is an engineering effort that combines hardware and software to develop a rehabilitation tool for schizophrenic patients. A clinical trial is also planned for assessing the effectiveness of the system among negative symptoms in schizophrenia patients.

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Section: Data Integration and Processingmentioning

confidence: 99%

Digital Technologies for Innovative Mental Health Rehabilitation

et al. 2021

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“…This automatic processing is largely attributed to the emotional salience portrayed by a face, whereby expressions of anger, fear and disgust significantly raise our vigilance and give an evolutionary advantage for avoiding harm (Murphy and Zajonc, 1993 ; Öhman, 2002 ). By contrast, difficulties with face perception may severely impact social cognitions, e.g., emotion recognition and interpretation of social cues, as seen in autism spectrum disorders and schizophrenia (Kleinhans et al, 2011 ; Gao et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Subliminal Emotional Faces Elicit Predominantly Right-Lateralized Amygdala Activation: A Systematic Meta-Analysis of fMRI Studies

et al. 2022

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Prior research suggests that conscious face processing occurs preferentially in right hemisphere occipito-parietal regions. However, less is known about brain regions associated with non-conscious processing of faces, and whether a right-hemispheric dominance persists in line with specific affective responses. We aim to review the neural responses systematically, quantitatively, and qualitatively underlying subliminal face processing. PubMed was searched for Functional Magnetic Resonance Imaging (fMRI) publications assessing subliminal emotional face stimuli up to March 2022. Activation Likelihood Estimation (ALE) meta-analyses and narrative reviews were conducted on all studies that met ALE requirements. Risk of bias was assessed using the AXIS tool. In a meta-analysis of all 22 eligible studies (merging clinical and non-clinical populations, whole brain and region of interest analyses), bilateral amygdala activation was reported in the left (x = −19.2, y = 1.5, z = −17.1) in 59% of studies, and in the right (x = 24.4, y = −1.7, z = −17.4) in 68% of studies. In a second meta-analysis of non-clinical participants only (n = 18), bilateral amygdala was again reported in the left (x = −18, y = 3.9, z = −18.4) and right (x = 22.8, y = −0.9, z = −17.4) in 56% of studies for both clusters. In a final meta-analysis of whole-brain studies only (n=14), bilateral amygdala was also reported in the left (x = −20.2, y = 2.9, z = −17.2) in 64% of studies, and right (x = 24.2, y = −0.7, z = −17.8) in 71% of studies. The findings suggest that non-consciously detected emotional faces may influence amygdala activation, especially right-lateralized (a higher percentage of convergence in studies), which are integral for pre-conscious affect and long-term memory processing.

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“…Moreover, one of the most researched social cognitions in schizophrenia is facial emotion recognition (FER). Unfortunately, FER was found to be impaired in patients with schizophrenia 5,6 . Another study showed that de ciency in FER were stable across phases of schizophrenia and were already present in rst-episode schizophrenia (FSZ) patients 7 .…”

Section: Introductionmentioning

confidence: 99%

The correlation between fractional amplitude of low-frequency fluctuation-based resting-state functional magnetic resonance imaging and facial emotion recognition ability in patients with first-episode schizophrenia

Kuang

Zhou

et al. 2022

Preprint

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It was still unclear that the correlation between the resting-state intrinsic activity in brain regions and facial emotion recognition (FER) ability in patients with first-episode schizophrenia (FSZ). Our aim was to analyse the correlation between the fractional amplitude of low-frequency fluctuation (fALFF) and FER ability in FSZ patients. A total of 28 patients with FSZ and 33 healthy controls (HCs) completed visual search tasks for FER ability. Regions of interest (ROIs) related to facial emotion were obtained from a previous meta-analysis. Pearson correlation analysis was performed to understand the correlation between fALFF and FER ability. Our results indicated that the patients performed worse than the HCs in the accuracy performances of happy FER and fearful FER. The previous meta-analysis results showed that the brain regions related to FER included the bilateral amygdala (AMY)/hippocampus (HIP), right fusiform gyrus (FFG), and right supplementary motor area (SMA). Pearson correlation showed that the fALFF of the right FFG was associated with high-load fearful FER accuracy (r = -0.43, p = 0.022). Multiple regression analysis showed that the fALFF of the right FFG was an independent contributor to fearful FER accuracy (low-load task: B = -0.40, t = -2.29, p = 0.034; high-load task: B = -0.46, t = -3.30, p = 0.004). Our study indicated that FER ability is correlated with resting-state intrinsic activity in brain regions related to facial emotion, which may provide a reference for the study of FER in schizophrenia.

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Facial Emotion Recognition in Schizophrenia

Cited by 52 publications

References 63 publications

Digital Technologies for Innovative Mental Health Rehabilitation

Digital Technologies for Innovative Mental Health Rehabilitation

Subliminal Emotional Faces Elicit Predominantly Right-Lateralized Amygdala Activation: A Systematic Meta-Analysis of fMRI Studies

The correlation between fractional amplitude of low-frequency fluctuation-based resting-state functional magnetic resonance imaging and facial emotion recognition ability in patients with first-episode schizophrenia

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