Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability.
Adolescent
Adult
Algorithms
Autism Spectrum Disorder
/ diagnosis
Case-Control Studies
Child
Child, Preschool
Clinical Trials as Topic
Emotions
Facial Expression
Female
Humans
Male
Middle Aged
Models, Theoretical
Multicenter Studies as Topic
Photic Stimulation
Reaction Time
Recognition, Psychology
Young Adult
Autism spectrum disorder
Emotional regulation
Emotions
Facial expression
Impulsive behavior
Journal
Molecular autism
ISSN: 2040-2392
Titre abrégé: Mol Autism
Pays: England
ID NLM: 101534222
Informations de publication
Date de publication:
11 05 2020
11 05 2020
Historique:
received:
06
08
2019
accepted:
10
03
2020
entrez:
13
5
2020
pubmed:
13
5
2020
medline:
29
5
2021
Statut:
epublish
Résumé
Reduction or differences in facial expression are a core diagnostic feature of autism spectrum disorder (ASD), yet evidence regarding the extent of this discrepancy is limited and inconsistent. Use of automated facial expression detection technology enables accurate and efficient tracking of facial expressions that has potential to identify individual response differences. Children and adults with ASD (N = 124) and typically developing (TD, N = 41) were shown short clips of "funny videos." Using automated facial analysis software, we investigated differences between ASD and TD groups and within the ASD group in evidence of facial action unit (AU) activation related to the expression of positive facial expression, in particular, a smile. Individuals with ASD on average showed less evidence of facial AUs (AU12, AU6) relating to positive facial expression, compared to the TD group (p < .05, r = - 0.17). Using Gaussian mixture model for clustering, we identified two distinct distributions within the ASD group, which were then compared to the TD group. One subgroup (n = 35), termed "over-responsive," expressed more intense positive facial expressions in response to the videos than the TD group (p < .001, r = 0.31). The second subgroup (n = 89), ("under-responsive"), displayed fewer, less intense positive facial expressions in response to videos than the TD group (p < .001; r = - 0.36). The over-responsive subgroup differed from the under-responsive subgroup in age and caregiver-reported impulsivity (p < .05, r = 0.21). Reduced expression in the under-responsive, but not the over-responsive group, was related to caregiver-reported social withdrawal (p < .01, r = - 0.3). This exploratory study does not account for multiple comparisons, and future work will have to ascertain the strength and reproducibility of all results. Reduced displays of positive facial expressions do not mean individuals with ASD do not experience positive emotions. Individuals with ASD differed from the TD group in their facial expressions of positive emotion in response to "funny videos." Identification of subgroups based on response may help in parsing heterogeneity in ASD and enable targeting of treatment based on subtypes. ClinicalTrials.gov, NCT02299700. Registration date: November 24, 2014.
Sections du résumé
BACKGROUND
Reduction or differences in facial expression are a core diagnostic feature of autism spectrum disorder (ASD), yet evidence regarding the extent of this discrepancy is limited and inconsistent. Use of automated facial expression detection technology enables accurate and efficient tracking of facial expressions that has potential to identify individual response differences.
METHODS
Children and adults with ASD (N = 124) and typically developing (TD, N = 41) were shown short clips of "funny videos." Using automated facial analysis software, we investigated differences between ASD and TD groups and within the ASD group in evidence of facial action unit (AU) activation related to the expression of positive facial expression, in particular, a smile.
RESULTS
Individuals with ASD on average showed less evidence of facial AUs (AU12, AU6) relating to positive facial expression, compared to the TD group (p < .05, r = - 0.17). Using Gaussian mixture model for clustering, we identified two distinct distributions within the ASD group, which were then compared to the TD group. One subgroup (n = 35), termed "over-responsive," expressed more intense positive facial expressions in response to the videos than the TD group (p < .001, r = 0.31). The second subgroup (n = 89), ("under-responsive"), displayed fewer, less intense positive facial expressions in response to videos than the TD group (p < .001; r = - 0.36). The over-responsive subgroup differed from the under-responsive subgroup in age and caregiver-reported impulsivity (p < .05, r = 0.21). Reduced expression in the under-responsive, but not the over-responsive group, was related to caregiver-reported social withdrawal (p < .01, r = - 0.3).
LIMITATIONS
This exploratory study does not account for multiple comparisons, and future work will have to ascertain the strength and reproducibility of all results. Reduced displays of positive facial expressions do not mean individuals with ASD do not experience positive emotions.
CONCLUSIONS
Individuals with ASD differed from the TD group in their facial expressions of positive emotion in response to "funny videos." Identification of subgroups based on response may help in parsing heterogeneity in ASD and enable targeting of treatment based on subtypes.
TRIAL REGISTRATION
ClinicalTrials.gov, NCT02299700. Registration date: November 24, 2014.
Identifiants
pubmed: 32393350
doi: 10.1186/s13229-020-00327-4
pii: 10.1186/s13229-020-00327-4
pmc: PMC7212683
doi:
Banques de données
ClinicalTrials.gov
['NCT02299700']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
31Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
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