A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder
Imagery of facial expressions in Autism Spectrum Disorder (ASD) is likely impaired but has been very difficult to capture at a neurophysiological level. We developed an approach that allowed to directly link observation of emotional expressions and imagery in ASD, and to derive biomarkers that are a...
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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2018-11-01
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Series: | Frontiers in Neuroscience |
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Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2018.00791/full |
id |
doaj-9d2950cdd70c4af7b2dffe57205d1c15 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marco Simões Marco Simões Marco Simões Raquel Monteiro Raquel Monteiro João Andrade Susana Mouga Susana Mouga Susana Mouga Felipe França Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Paulo Carvalho Miguel Castelo-Branco Miguel Castelo-Branco |
spellingShingle |
Marco Simões Marco Simões Marco Simões Raquel Monteiro Raquel Monteiro João Andrade Susana Mouga Susana Mouga Susana Mouga Felipe França Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Paulo Carvalho Miguel Castelo-Branco Miguel Castelo-Branco A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder Frontiers in Neuroscience emotional facial expression mental imagery EEG biomarker machine learning autism spectrum disorder dynamic expressions |
author_facet |
Marco Simões Marco Simões Marco Simões Raquel Monteiro Raquel Monteiro João Andrade Susana Mouga Susana Mouga Susana Mouga Felipe França Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Paulo Carvalho Miguel Castelo-Branco Miguel Castelo-Branco |
author_sort |
Marco Simões |
title |
A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder |
title_short |
A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder |
title_full |
A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder |
title_fullStr |
A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder |
title_full_unstemmed |
A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder |
title_sort |
novel biomarker of compensatory recruitment of face emotional imagery networks in autism spectrum disorder |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroscience |
issn |
1662-453X |
publishDate |
2018-11-01 |
description |
Imagery of facial expressions in Autism Spectrum Disorder (ASD) is likely impaired but has been very difficult to capture at a neurophysiological level. We developed an approach that allowed to directly link observation of emotional expressions and imagery in ASD, and to derive biomarkers that are able to classify abnormal imagery in ASD. To provide a handle between perception and action imagery cycles it is important to use visual stimuli exploring the dynamical nature of emotion representation. We conducted a case-control study providing a link between both visualization and mental imagery of dynamic facial expressions and investigated source responses to pure face-expression contrasts. We were able to replicate the same highly group discriminative neural signatures during action observation (dynamical face expressions) and imagery, in the precuneus. Larger activation in regions involved in imagery for the ASD group suggests that this effect is compensatory. We conducted a machine learning procedure to automatically identify these group differences, based on the EEG activity during mental imagery of facial expressions. We compared two classifiers and achieved an accuracy of 81% using 15 features (both linear and non-linear) of the signal from theta, high-beta and gamma bands extracted from right-parietal locations (matching the precuneus region), further confirming the findings regarding standard statistical analysis. This robust classification of signals resulting from imagery of dynamical expressions in ASD is surprising because it far and significantly exceeds the good classification already achieved with observation of neutral face expressions (74%). This novel neural correlate of emotional imagery in autism could potentially serve as a clinical interventional target for studies designed to improve facial expression recognition, or at least as an intervention biomarker. |
topic |
emotional facial expression mental imagery EEG biomarker machine learning autism spectrum disorder dynamic expressions |
url |
https://www.frontiersin.org/article/10.3389/fnins.2018.00791/full |
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doaj-9d2950cdd70c4af7b2dffe57205d1c152020-11-25T00:15:13ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-11-011210.3389/fnins.2018.00791418353A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum DisorderMarco Simões0Marco Simões1Marco Simões2Raquel Monteiro3Raquel Monteiro4João Andrade5Susana Mouga6Susana Mouga7Susana Mouga8Felipe França9Guiomar Oliveira10Guiomar Oliveira11Guiomar Oliveira12Guiomar Oliveira13Guiomar Oliveira14Paulo Carvalho15Miguel Castelo-Branco16Miguel Castelo-Branco17Coimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, PortugalFaculty of Medicine, University of Coimbra, Coimbra, PortugalCenter for Informatics and Systems, University of Coimbra, Coimbra, PortugalCoimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, PortugalFaculty of Medicine, University of Coimbra, Coimbra, PortugalFaculty of Medicine, University of Coimbra, Coimbra, PortugalCoimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, PortugalFaculty of Medicine, University of Coimbra, Coimbra, PortugalNeurodevelopmental and Autism Unit from Child Developmental Center, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, PortugalPESC-COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, BrazilCoimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, PortugalFaculty of Medicine, University of Coimbra, Coimbra, PortugalNeurodevelopmental and Autism Unit from Child Developmental Center, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, PortugalUniversity Clinic of Pediatrics, Faculty of Medicine of the University of Coimbra, Coimbra, PortugalCentro de Investigação e Formação Clínica, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, PortugalCenter for Informatics and Systems, University of Coimbra, Coimbra, PortugalCoimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, PortugalFaculty of Medicine, University of Coimbra, Coimbra, PortugalImagery of facial expressions in Autism Spectrum Disorder (ASD) is likely impaired but has been very difficult to capture at a neurophysiological level. We developed an approach that allowed to directly link observation of emotional expressions and imagery in ASD, and to derive biomarkers that are able to classify abnormal imagery in ASD. To provide a handle between perception and action imagery cycles it is important to use visual stimuli exploring the dynamical nature of emotion representation. We conducted a case-control study providing a link between both visualization and mental imagery of dynamic facial expressions and investigated source responses to pure face-expression contrasts. We were able to replicate the same highly group discriminative neural signatures during action observation (dynamical face expressions) and imagery, in the precuneus. Larger activation in regions involved in imagery for the ASD group suggests that this effect is compensatory. We conducted a machine learning procedure to automatically identify these group differences, based on the EEG activity during mental imagery of facial expressions. We compared two classifiers and achieved an accuracy of 81% using 15 features (both linear and non-linear) of the signal from theta, high-beta and gamma bands extracted from right-parietal locations (matching the precuneus region), further confirming the findings regarding standard statistical analysis. This robust classification of signals resulting from imagery of dynamical expressions in ASD is surprising because it far and significantly exceeds the good classification already achieved with observation of neutral face expressions (74%). This novel neural correlate of emotional imagery in autism could potentially serve as a clinical interventional target for studies designed to improve facial expression recognition, or at least as an intervention biomarker.https://www.frontiersin.org/article/10.3389/fnins.2018.00791/fullemotional facial expressionmental imageryEEG biomarkermachine learningautism spectrum disorderdynamic expressions |