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Fulltext Review of emotion recognition in stroke patients

Yuvaraj R, Murugappan M, Norlinah MI, Sundaraj K, Khairiyah M

Dement Geriatr Cogn Disord, 2013;36(3-4):179-96.
PMID: 23899462 DOI: 10.1159/000353440

OBJECTIVE: Patients suffering from stroke have a diminished ability to recognize emotions. This paper presents a review of neuropsychological studies that investigated the basic emotion processing deficits involved in individuals with interhemispheric brain (right, left) damage and normal controls, including processing mode (perception) and communication channels (facial, prosodic-intonational, lexical-verbal).
METHODS: An electronic search was conducted using specific keywords for studies investigating emotion recognition in brain damage patients. The PubMed database was searched until March 2012 as well as citations and reference lists. 92 potential articles were identified.
RESULTS: The findings showed that deficits in emotion perception were more frequently observed in individuals with right brain damage than those with left brain damage when processing facial, prosodic and lexical emotional stimuli.
CONCLUSION: These findings suggest that the right hemisphere has a unique contribution in emotional processing and provide support for the right hemisphere emotion hypothesis.
SIGNIFICANCE:
This robust deficit in emotion recognition has clinical significance. The extent of emotion recognition deficit in brain damage patients appears to be correlated with a variety of interpersonal difficulties such as complaints of frustration in social relations, feelings of social discomfort, desire to connect with others, feelings of social disconnection and use of controlling behaviors.

Matched MeSH terms: Facial Expression
Exploring bedside clinical features of parkinsonism: A focus on differential diagnosis

Bhidayasiri R, Rattanachaisit W, Phokaewvarangkul O, Lim TT, Fernandez HH

Parkinsonism Relat Disord, 2019 Feb;59:74-81.
PMID: 30502095 DOI: 10.1016/j.parkreldis.2018.11.005

The proper diagnosis of parkinsonian disorders usually involves three steps: identifying core features of parkinsonism; excluding other causes; and collating supportive evidence based on clinical signs or investigations. While the recognition of cardinal parkinsonian features is usually straightforward, the appreciation of clinical features suggestive of specific parkinsonian disorders can be challenging, and often requires greater experience and skills. In this review, we outline the clinical features that are relevant to the differential diagnosis of common neurodegenerative parkinsonian disorders, including Parkinson's disease, multiple system atrophy, progressive supranuclear palsy, and corticobasal degeneration. We aim to make this process relatable to clinicians-in-practice, therefore, have categorised the list of clinical features into groups according to the typical sequence on how clinicians would elicit them during the examination, starting with observation of facial expression and clinical signs of the face, spotting eye movement abnormalities, examination of tremors and jerky limb movements, and finally, examination of posture and gait dysfunction. This review is not intended to be comprehensive. Rather, we have focused on the most common clinical signs that are potentially key to making the correct diagnosis and those that do not require special skills or training for interpretation. Evidence is also provided, where available, such as diagnostic criteria, consensus statements, clinicopathological studies or large multi-centre registries. Pitfalls are also discussed when relevant to the diagnosis. While no clinical signs are pathognomonic for certain parkinsonian disorders, certain clinical clues may assist in narrowing a differential diagnosis and tailoring focused investigations for the individual patient.

Matched MeSH terms: Facial Expression
Fulltext An Intelligent Mental Health Identification Method for College Students: A Mixed-Method Study

Li C, Yang M, Zhang Y, Lai KW

Int J Environ Res Public Health, 2022 Nov 14;19(22).
PMID: 36429697 DOI: 10.3390/ijerph192214976

PURPOSE: Mental health assessments that combine patients' facial expressions and behaviors have been proven effective, but screening large-scale student populations for mental health problems is time-consuming and labor-intensive. This study aims to provide an efficient and accurate intelligent method for further psychological diagnosis and treatment, which combines artificial intelligence technologies to assist in evaluating the mental health problems of college students.
MATERIALS AND METHODS: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score.
RESULTS: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively.
CONCLUSION: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students' psychological problems.

Matched MeSH terms: Facial Expression
Using other minds as a window onto the world: guessing what happened from clues in behaviour

Pillai D, Sheppard E, Ropar D, Marsh L, Pearson A, Mitchell P

J Autism Dev Disord, 2014 Oct;44(10):2430-9.
PMID: 24710812 DOI: 10.1007/s10803-014-2106-x

It has been proposed that mentalising involves retrodicting as well as predicting behaviour, by inferring previous mental states of a target. This study investigated whether retrodiction is impaired in individuals with autism spectrum disorders (ASD). Participants watched videos of real people reacting to the researcher behaving in one of four possible ways. Their task was to decide which of these four "scenarios" each person responded to. Participants' eye movements were recorded. Participants with ASD were poorer than comparison participants at identifying the scenario to which people in the videos were responding. There were no group differences in time spent looking at the eyes or mouth. The findings imply those with ASD are impaired in using mentalising skills for retrodiction.

Matched MeSH terms: Facial Expression
Fulltext Protocol for a between-group experimental study examining cultural differences in emotion processing between Malay and Caucasian adults with and without major depressive disorder

Mohan SN, Mukhtar F, Jobson L

BMJ Open, 2016 Oct 21;6(10):e012774.
PMID: 27798019 DOI: 10.1136/bmjopen-2016-012774

INTRODUCTION: Depression is a mood disorder that affects a significant proportion of the population worldwide. In Malaysia and Australia, the number of people diagnosed with depression is on the rise. It has been found that impairments in emotion processing and emotion regulation play a role in the development and maintenance of depression. This study is based on Matsumoto and Hwang's biocultural model of emotion and Triandis' Subjective Culture model. It aims to investigate the influence of culture on emotion processing among Malaysians and Australians with and without major depressive disorder (MDD).
METHODS AND ANALYSIS: This study will adopt a between-group design. Participants will include Malaysian Malays and Caucasian Australians with and without MDD (N=320). There will be four tasks involved in this study, namely: (1) the facial emotion recognition task, (2) the biological motion task, (3) the subjective experience task and (4) the emotion meaning task. It is hypothesised that there will be cultural differences in how participants with and without MDD respond to these emotion tasks and that, pan-culturally, MDD will influence accuracy rates in the facial emotion recognition task and the biological motion task.
ETHICS AND DISSEMINATION: This study is approved by the Universiti Putra Malaysia Research Ethics Committee (JKEUPM) and the Monash University Human Research Ethics Committee (MUHREC). Permission to conduct the study has also been obtained from the National Medical Research Register (NMRR; NMRR-15-2314-26919). On completion of the study, data will be kept by Universiti Putra Malaysia for a specific period of time before they are destroyed. Data will be published in a collective manner in the form of journal articles with no reference to a specific individual.

Matched MeSH terms: Facial Expression