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Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability

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 trackin...

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Autores principales: Bangerter, Abigail, Chatterjee, Meenakshi, Manfredonia, Joseph, Manyakov, Nikolay V., Ness, Seth, Boice, Matthew A., Skalkin, Andrew, Goodwin, Matthew S., Dawson, Geraldine, Hendren, Robert, Leventhal, Bennett, Shic, Frederick, Pandina, Gahan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7212683/
https://www.ncbi.nlm.nih.gov/pubmed/32393350
http://dx.doi.org/10.1186/s13229-020-00327-4
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author Bangerter, Abigail
Chatterjee, Meenakshi
Manfredonia, Joseph
Manyakov, Nikolay V.
Ness, Seth
Boice, Matthew A.
Skalkin, Andrew
Goodwin, Matthew S.
Dawson, Geraldine
Hendren, Robert
Leventhal, Bennett
Shic, Frederick
Pandina, Gahan
author_facet Bangerter, Abigail
Chatterjee, Meenakshi
Manfredonia, Joseph
Manyakov, Nikolay V.
Ness, Seth
Boice, Matthew A.
Skalkin, Andrew
Goodwin, Matthew S.
Dawson, Geraldine
Hendren, Robert
Leventhal, Bennett
Shic, Frederick
Pandina, Gahan
author_sort Bangerter, Abigail
collection PubMed
description 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
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spelling pubmed-72126832020-05-18 Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability Bangerter, Abigail Chatterjee, Meenakshi Manfredonia, Joseph Manyakov, Nikolay V. Ness, Seth Boice, Matthew A. Skalkin, Andrew Goodwin, Matthew S. Dawson, Geraldine Hendren, Robert Leventhal, Bennett Shic, Frederick Pandina, Gahan Mol Autism Research 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 BioMed Central 2020-05-11 /pmc/articles/PMC7212683/ /pubmed/32393350 http://dx.doi.org/10.1186/s13229-020-00327-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Bangerter, Abigail
Chatterjee, Meenakshi
Manfredonia, Joseph
Manyakov, Nikolay V.
Ness, Seth
Boice, Matthew A.
Skalkin, Andrew
Goodwin, Matthew S.
Dawson, Geraldine
Hendren, Robert
Leventhal, Bennett
Shic, Frederick
Pandina, Gahan
Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability
title Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability
title_full Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability
title_fullStr Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability
title_full_unstemmed Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability
title_short Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability
title_sort automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7212683/
https://www.ncbi.nlm.nih.gov/pubmed/32393350
http://dx.doi.org/10.1186/s13229-020-00327-4
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